# Potential Future Directions in Optimization of Students’ Performance Prediction System

Authors: Sadique Ahmad; Mohammed A. El-Affendi; M. Shahid Anwar; Rizwan Iqbal
Journal: Computational Intelligence and Neuroscience
 (2022)

---

## Abstract

Previous studies widely report the optimization of performance predictions to highlight at-risk students and advance the achievement of excellent students. They also have contributions that overlap different fields of research. On the one hand, they have insightful psychological studies, data mining discoveries, and data analysis findings. On the other hand, they produce a variety of performance prediction approaches to assess students’ performance during cognitive tasks. However, the synchronization between these studies is still a black box that increases prediction systems’ dependency on real-world datasets. It also delays the mathematical modeling of students’ emotional attributes. This review paper performs an insightful analysis and thorough literature-based survey to draw a comprehensive picture of potential challenges and prior contributions. The review consists of 1497 publications from 1990 to 2022 (32 years), which reported various opportunities for future performance prediction researchers. First, it evaluates psychological studies, data analysis results, and data mining findings to provide a general picture of the statistical association among students’ performance and various influential factors. Second, it critically evaluates new students’ performance prediction techniques, modifications in existing techniques, and comprehensive studies based on the comparative analysis. Lastly, future directions and potential pilot projects based on the assumption-based dataset are highlighted to optimize the existing performance prediction systems.


---

## Body

## 1. Introduction

Over the past few decades, students’ performance has been predicted while evaluating the influence of different factors, such as emotional attributes, family attributes, study schedule, institutional attributes, and students’ scores in assignments, quizzes, and final examinations [1–5]. Such systems provide useful applications to a wide area in academia, i.e., students’ success and failure estimation due to influential factors [6–10]. This study splits the earlier contributions into two groups. The first group consists of insightful psychological studies, data mining discoveries, and data analysis findings that indirectly contribute to the optimization of students’ performance prediction systems. The second group reports the optimization of existing prediction systems based on the findings of the first group. However, the extensive synchronization between the two groups is still a black box that ultimately increases students’ performance prediction systems’ dependency on a real-world dataset. Such synchronization can provide useful ideas during the optimization and data collection process. It also paves the way for an assumption-based dataset to prove the viability of pilot project implementations that will speed up modeling students’ emotional attributes.This review paper conducts an insightful study and literature-based survey to draw a comprehensive picture of the prior studies on student performance analysis and prediction. The review consists of 1497 articles from 1990 to 2022 (32 years), which reported various information for future researchers:1.
It explores and lists the research fields’ contributions focusing on students’ performance optimization. Psychology and data analysis fields pave the way for effective solutions to the problems of data deficiency. They provide qualitative findings that can be used for creating an assumption-based dataset for pilot project implementation.2.
It thoroughly considered new and modified algorithms that predict students’ performance. Also, a comparative analysis was performed between the existing students’ performance prediction approaches to provide better recommendations for optimization.3.
The study delivers a comprehensive picture of potential challenges and research direction for future researchers. The review also shows that very few contributions have mathematically modeled emotional attributes.The remaining sections of this review are as follows: Section2 gives a detailed literature review. Section 3 elaborates the review methodology, and Section 4 produces data evaluation. Section 5 presents future challenges, and Section 6 concludes the study.


## 2. Literature Review

Students’ performance prediction systems have enormous applications in academia, such as predicting at-risk students, course recommendation, and basic counseling against negative emotions, highlighting the influence of institutional attributes, family factors, etc. [11–14]. It is also needed to advance the academic achievement of excellent students [15–19]. Prior studies deliver qualitative and quantitative results in extending students’ performance evaluation and calculation, and highlighting the factors that influence the performance [20–25]. For a few decades, psychology, data mining, cognitive computing, and data analysis fields directly or indirectly contributed to the optimization of students’ performance prediction systems [26–34]. Therefore, related work is split into the following subsections.


### 2.1. Contributions of Psychology

Psychological studies results manifest that students’ performance is easily influenced via emotional attributes, such as frustration, anxiety, stress, over expectation of parents, and parents’ relationship [35–37]. The results provide correlations statistics among emotional factors and the expected performance of students in cognitive activity, such as attempting the examination, quizzes, assignments, class activities, and extracurricular activities. These particular emotional factors can negatively and positively impact the students’ performance. In such a situation, emotional severity, family attributes, and institutional factors play a crucial role in influencing performance [38–40]. It shows that performance is always very sensitive and affected by the individuals’ surroundings.


### 2.2. Contributions of Data Mining

The data mining evaluates the relationship among various students’ factors, such as the role of emotional factors, family attributes, institutional factors, and class performance. Such studies provide good opportunities for accurate estimations of expected students’ performance [41–47]. The meaningful patterns always produce good directions for further exploration. The previous articles lack coordination between the students’ influential attributes and academic performance. The literature lacks accurate techniques to simulate students’ performance due to the insufficient synchronization and coordination among earlier studies on students’ factors. The mathematical modulation of students’ performance needs to formulate the function of student factors. However, it is inspiring to closely examine the quantitative influence of several student factors on academic achievements. The earlier studies show that emotional, family, study schedule, and institutional attributes are the significant factors that can easily influence students’ academic performance in any critical cognitive activity. Prior studies illustrate that educational data mining practices contribute to students’ factor evaluation process and performance prediction. Institutional factors involve teaching methodology, engaging students in the classroom, and the vision of instruction. According to literature studies, teachers play an active role in institutional attributes influencing students’ performance. They provide administrative assistance and assistance in ensuring discipline [48–57].


### 2.3. Synchronization among Existing Studies

Accurate performance prediction needs to examine students’ factors beyond the computer science framework. The literature studies are still limited in finding an authentic and extendable approach that overlaps psychology, data mining, data analysis, and cognitive research. Articles have various solutions to predict students’ performance using different techniques that could have the potential to be escalated to more general problems of predicting student performance [58–62]. The primary objective of the current review attempt is to efficiently explore the relationship between students’ factors (as mentioned earlier) and their performance. Therefore, the literature is studied with the selected students’ attributes (emotional, family, study schedule, and institutional) and effects. Articles show that most students do not participate in extracurricular activities, believing extra activities would negatively affect their academic achievements. Earlier studies also focus on predicting college students’ performance by considering all the important aspects. They delivered a prediction system to estimate performance by assisting the university in selecting each candidate using past academic records of students granted admissions [29, 63–69]. Such efforts show that earlier studies contribute to decreasing the number of at-risk students and advancing the performance of excellent students.Literature also attempted to perform a survey on classroom learning in different environments. It analyzes various aspects and factors influencing (positively or negatively) performance in a classroom that interfere with learning. This paper presents a systematic review of numerous studies on students’ performance in classroom learning. For a few decades, the research has produced numerous results in students’ performance evaluation; however, the education system needs a complete and detailed performance prediction system that can ensure interaction and coordination between the aforementioned students’ factors. Literature studies delivered various contributions, such as the proposal of an innovative model that targets modifying learning sustainability through smart education applications and regression and correlation among students’ factors, and logistic regression analyses generated that being female, first-semester GPA, number of courses per regular semester, and number of courses per summer semester were imperative predictors of baccalaureate degree achievement [70–77].


### 2.4. Existing Models and Performance Prediction System Optimization

Studies have focused on applying artificial neural networks to predict performance in different environments. Articles are also saturated with deep learning techniques that deliver prediction and highlight at-risk students. Few other technologies provide opportunities to accurately evaluate the performance and reduce the failure rates [78–82]. It also helps in counseling students in alarming situations that can positively impact their academic achievements, i.e., COVID-19. Thus, during the literature survey, we have found many students’ prediction systems which are interesting; nevertheless, they are failed to mathematical model emotional attributes and synchronized them with institutional attributes, study schedules, and family attributes [31, 37, 83–98]. The objective of this study is to identify the relationship between extracurricular activities and students’ performances.The articles deliver many results on the effects of influential students’ factors. This study explores performance prediction beyond the scope of computer science and machine learning.


### 2.5. Related Performance Prediction Methods

As discussed earlier, many studies solved meaningful challenges in students’ performance prediction area of research for a few decades. The earlier studies have many contributions in the form of neural works, recommendation systems, course recommendations, and students’ performance evaluation systems [87, 99–109]. The prior studies demonstrate comprehensive work on students’ performance prediction systems that use information obtained during the interaction of students with the institutional attributes. To mathematically consider the expected actions of a students’ factors, such information provides proper guidelines. The significant characteristic is the identical structure of the information processing system of students, which can be replicated to construct a learning algorithm (cognitive architecture). Literature studies are flooded with many findings that primarily contribute to prediction algorithms and mathematical models; nevertheless, modeling the relationship between students’ emotions (frustration, stress, etc.) and students’ performance is very little focused [110–115].Also, the published studies on modeling emotion are not extendable toward a matured prediction system. So, the dire need is to assess the main framework of existing prediction algorithms. Exploring the qualitative results of psychological studies and data analysis discoveries is needed to estimate students’ performance. It will also help in the iterative calculation of emotional influence on performance during critical cognitive activities.Extraordinary academic performance is only possible with excellent cognitive skills. Such skills are needed to accomplish any task requiring problem-solving approaches, reasoning, and memory management. However, with inadequate cognitive abilities, an individual cannot achieve an excellent score in various cognitive tasks, i.e., assignments, quizzes, and written examinations. They require students to process new information, organize learning, and retrieve that data (from memory) for later use. So, predicting performance while calculating the intense impact of various groups of factors is crucial not only for tutors to ensure effective teaching methodology but also for students’ achievements and effective academic policies. Earlier studies have delivered many approaches that predict students’ performance; nevertheless, they have paved the way for new challenges for effective educational systems. The skills levels of students are changing as they learn and forget. The educational system needs such a system that can manage the students’ dynamic behavior during cognitive activities.Other studies have described the students’ personality traits and the essential characteristics of personality. Results reveal that performance prediction design can be broken down into subsections that are more realistic in comparison to other techniques. It also paves the way for the development of performance prediction architectures which were easy to understand. The current review illustrates that the performance prediction system needs to coordinate among prediction architecture, psychological experiments, semantical investigations, statistical analysis, and mathematical formulation. This work provided an outstanding opportunity for researchers belonging to performance prediction, bioinformatics, data mining, and data integration.


## 2.1. Contributions of Psychology

Psychological studies results manifest that students’ performance is easily influenced via emotional attributes, such as frustration, anxiety, stress, over expectation of parents, and parents’ relationship [35–37]. The results provide correlations statistics among emotional factors and the expected performance of students in cognitive activity, such as attempting the examination, quizzes, assignments, class activities, and extracurricular activities. These particular emotional factors can negatively and positively impact the students’ performance. In such a situation, emotional severity, family attributes, and institutional factors play a crucial role in influencing performance [38–40]. It shows that performance is always very sensitive and affected by the individuals’ surroundings.


## 2.2. Contributions of Data Mining

The data mining evaluates the relationship among various students’ factors, such as the role of emotional factors, family attributes, institutional factors, and class performance. Such studies provide good opportunities for accurate estimations of expected students’ performance [41–47]. The meaningful patterns always produce good directions for further exploration. The previous articles lack coordination between the students’ influential attributes and academic performance. The literature lacks accurate techniques to simulate students’ performance due to the insufficient synchronization and coordination among earlier studies on students’ factors. The mathematical modulation of students’ performance needs to formulate the function of student factors. However, it is inspiring to closely examine the quantitative influence of several student factors on academic achievements. The earlier studies show that emotional, family, study schedule, and institutional attributes are the significant factors that can easily influence students’ academic performance in any critical cognitive activity. Prior studies illustrate that educational data mining practices contribute to students’ factor evaluation process and performance prediction. Institutional factors involve teaching methodology, engaging students in the classroom, and the vision of instruction. According to literature studies, teachers play an active role in institutional attributes influencing students’ performance. They provide administrative assistance and assistance in ensuring discipline [48–57].


## 2.3. Synchronization among Existing Studies

Accurate performance prediction needs to examine students’ factors beyond the computer science framework. The literature studies are still limited in finding an authentic and extendable approach that overlaps psychology, data mining, data analysis, and cognitive research. Articles have various solutions to predict students’ performance using different techniques that could have the potential to be escalated to more general problems of predicting student performance [58–62]. The primary objective of the current review attempt is to efficiently explore the relationship between students’ factors (as mentioned earlier) and their performance. Therefore, the literature is studied with the selected students’ attributes (emotional, family, study schedule, and institutional) and effects. Articles show that most students do not participate in extracurricular activities, believing extra activities would negatively affect their academic achievements. Earlier studies also focus on predicting college students’ performance by considering all the important aspects. They delivered a prediction system to estimate performance by assisting the university in selecting each candidate using past academic records of students granted admissions [29, 63–69]. Such efforts show that earlier studies contribute to decreasing the number of at-risk students and advancing the performance of excellent students.Literature also attempted to perform a survey on classroom learning in different environments. It analyzes various aspects and factors influencing (positively or negatively) performance in a classroom that interfere with learning. This paper presents a systematic review of numerous studies on students’ performance in classroom learning. For a few decades, the research has produced numerous results in students’ performance evaluation; however, the education system needs a complete and detailed performance prediction system that can ensure interaction and coordination between the aforementioned students’ factors. Literature studies delivered various contributions, such as the proposal of an innovative model that targets modifying learning sustainability through smart education applications and regression and correlation among students’ factors, and logistic regression analyses generated that being female, first-semester GPA, number of courses per regular semester, and number of courses per summer semester were imperative predictors of baccalaureate degree achievement [70–77].


## 2.4. Existing Models and Performance Prediction System Optimization

Studies have focused on applying artificial neural networks to predict performance in different environments. Articles are also saturated with deep learning techniques that deliver prediction and highlight at-risk students. Few other technologies provide opportunities to accurately evaluate the performance and reduce the failure rates [78–82]. It also helps in counseling students in alarming situations that can positively impact their academic achievements, i.e., COVID-19. Thus, during the literature survey, we have found many students’ prediction systems which are interesting; nevertheless, they are failed to mathematical model emotional attributes and synchronized them with institutional attributes, study schedules, and family attributes [31, 37, 83–98]. The objective of this study is to identify the relationship between extracurricular activities and students’ performances.The articles deliver many results on the effects of influential students’ factors. This study explores performance prediction beyond the scope of computer science and machine learning.


## 2.5. Related Performance Prediction Methods

As discussed earlier, many studies solved meaningful challenges in students’ performance prediction area of research for a few decades. The earlier studies have many contributions in the form of neural works, recommendation systems, course recommendations, and students’ performance evaluation systems [87, 99–109]. The prior studies demonstrate comprehensive work on students’ performance prediction systems that use information obtained during the interaction of students with the institutional attributes. To mathematically consider the expected actions of a students’ factors, such information provides proper guidelines. The significant characteristic is the identical structure of the information processing system of students, which can be replicated to construct a learning algorithm (cognitive architecture). Literature studies are flooded with many findings that primarily contribute to prediction algorithms and mathematical models; nevertheless, modeling the relationship between students’ emotions (frustration, stress, etc.) and students’ performance is very little focused [110–115].Also, the published studies on modeling emotion are not extendable toward a matured prediction system. So, the dire need is to assess the main framework of existing prediction algorithms. Exploring the qualitative results of psychological studies and data analysis discoveries is needed to estimate students’ performance. It will also help in the iterative calculation of emotional influence on performance during critical cognitive activities.Extraordinary academic performance is only possible with excellent cognitive skills. Such skills are needed to accomplish any task requiring problem-solving approaches, reasoning, and memory management. However, with inadequate cognitive abilities, an individual cannot achieve an excellent score in various cognitive tasks, i.e., assignments, quizzes, and written examinations. They require students to process new information, organize learning, and retrieve that data (from memory) for later use. So, predicting performance while calculating the intense impact of various groups of factors is crucial not only for tutors to ensure effective teaching methodology but also for students’ achievements and effective academic policies. Earlier studies have delivered many approaches that predict students’ performance; nevertheless, they have paved the way for new challenges for effective educational systems. The skills levels of students are changing as they learn and forget. The educational system needs such a system that can manage the students’ dynamic behavior during cognitive activities.Other studies have described the students’ personality traits and the essential characteristics of personality. Results reveal that performance prediction design can be broken down into subsections that are more realistic in comparison to other techniques. It also paves the way for the development of performance prediction architectures which were easy to understand. The current review illustrates that the performance prediction system needs to coordinate among prediction architecture, psychological experiments, semantical investigations, statistical analysis, and mathematical formulation. This work provided an outstanding opportunity for researchers belonging to performance prediction, bioinformatics, data mining, and data integration.


## 3. Review Methodology

The review process is started from the initial screening within the scope of the current attempt. As elaborated earlier, this review focused on recent and state-of-the-art contributions to student performance prediction. Figure1 illustrates the methodology of the review. We have divided the complete review process into the following sections.Figure 1
Framework illustrated the main modules of the current review process.


### 3.1. Review Process

This study reviews the earlier studies thoroughly based on the procedures prescribed by Petersen et al. [116] and Keele [117]. The methodology is adopted from Keele while the study mapping method is copied from Petersen et al. The review process is initiated with the modified procedure, which is demonstrated in Figure 1. For better understanding, the review delivers a detailed methodology of the prior work contributing to student performance prediction directly or indirectly. Moreover, the study put a list of research questions to demonstrate the main objectives. These research questions enable us to choose relevant research studies for screening and investigating the main challenges in students’ performance predictions. Every research question has a list of keywords to explore the literature and learn about a particular question. These keywords are used to search publications, including peer-reviewed book chapters, conferences proceeding, and journal articles.


### 3.2. Research Questions

1.
Q1: what are the applications of student performance prediction systems?2.
Q2: what are the factors that can optimize student performance prediction?3.
Q3: what is the intensity of research findings in the field of student performance prediction systems optimization?4.
Q4: are the findings of psychological studies, data mining, and contribution in algorithms synchronized with each other for the viability of the pilot project?5.
Q5: how synchronization and coordination of prior psychological, data mining, and algorithmic findings contribute to the effective educational system via student performance prediction algorithm.


### 3.3. Searching Keywords

The current study adopted a manual review methodology introduced by Keele [117]. The automatic review presented by Petersen et al. has a few disadvantages [116]. (1) The automatic search is not feasible for the current review [118]. (2) The manual searching strategy gives more relevant studies. Table 1 reflects the list of keywords that have produced a variety of articles published by various publishers, i.e., IEEE, Elsevier, Springer, Hindawi, MDPI, ACM, Wiley, and others. It shows many articles, including journals, book chapters, and conference proceedings.Table 1
Result obtained from the Google Scholar during keyword searching.
NoKeywordsIEEEACMSpringerMDPIHindawiElsevierWileyOthersTotal1Student performance prediction14138512777732Student performance and negative emotions984312976583Student emotional factors97122462334Work experience and student performance2010442534525Student biological factor and academics1156232510446Student academic achievements prediction117863516477Student frustration42522237278Student performance and frustration37764347419Student frustration severity57950813136010At-risk student prediction696111177116811At-risk student cognitive skills353843823612Cognitive skills prediction86474510105413Emotional impact on student performance697529755014Family impact on student achievements7259841155115Student anxiety9721167254916Student stress2981197275517Review on student performance3138345434318Student performance quantization8362112844419COVID-19, frustration, and student performance7553981145220COVID-19 and at-risk student888463624521Impact of online classes3288147324722Online classes and student learning71126981086123Learning prediction234482853624Student learning outcome prediction655496695025Student performance measurement2124484975026Performance measurement algorithm542496654127Performance prediction algorithm11456629115428Student performance evaluation algorithm726262363429Student performance prediction algorithm123948121556830Student performance measurement algorithm362375243231Cognitive skills prediction algorithm3757456542The keywords were searched directly on publishers’ websites and Google Scholar with a default setting. We have evaluated all the articles and collected those that deliver relevant findings for further screening. Furthermore, the main factors, topics, and relevant studies, including journals, conference proceedings, and book chapters, are given below:1.
Emotional attributes2.
Family factors3.
Study Schedule4.
Institutional attributes5.
Psychology, data mining, and data analysis findings on the factors as mentioned earlier6.
Contribution of cognitive computing, deep learning, and machine learning in students’ performance prediction7.
Reviews and comparison


### 3.4. Screening

Screening of studies is performed with the following terms and conditions:1.
The team selected the publications of the more relevant journal, conference, and book chapter.2.
Second, we have focused on the relevant title with impressive citations in Google Scholar.3.
Third, rapid reviews were performed for further evaluation and data extraction. During the rapid review, we have focused on the abstract and introduction to get some idea about the challenges, motivations, and contributions.These three steps were performed to create a database for further information extraction and data collection.


### 3.5. Information Collection

Various information was extracted from the selected publications during the information collection process, which are shown in Table1 to 4 Also, a spreadsheet was used to record the various information for further consideration of the research questions. The recorded data are shown in the tables mentioned above.Table 2
Research domain-wise keyword searching results and evaluation.
New methodsModificationData analysisPsychological findingsComparisonAnalysis of applicationReview workTotal number of publicationsIEEE1523686524109214ACM1416657013149201Springer11178732478166MDPI181248613613161Hindawi16227245171218202Elsevier221536704215164Wiley896632145617202Others53378713393187Table 3
Research domain-wise and factors-wise evaluation.
AttributesNew methodsModificationData analysisPsychological findingsComparisonAnalysis of applicationReview workTotal number of publicationsFrustration342624192431131Frustration severity0013334351499Stress2281913191679Stress severity0018189112177Anxiety19121417914691Anxiety severity02102221142493Depression00122023122188Parents’ influence4313116251678Distance from home and school33181313131982Mobile game222725171224109Outdoor game001972013766Indoor game00221721101080Watching TV00131024181681Students social network8121719724895Gender33101415151676Parents cohabitation status0011615181464Parent service245151132060International students005101171548Table 4
Intensity of various domain contributions.
Research outcomesNew methodsModificationData analysisPsychological findingsComparisonAnalysis of applicationReview workTotal number of publicationsSRI325PSP-PRP549AS-EDM33MAR-LD4610RFA6410MAR-KD77AF-TM11PSP-GPA6118PSP-MC5510EDU-DMCR459UPSP-EDU66PSP-OCS77PC-SD5510EDU-DMA33EDU-DMLA66SEDU-DM33EDU-DMA55SRCT7310PSBS1326EITE55ICAP77TEO55LAD55IEDU-PM66SAFP52310SPIE99PSS-TEDC729DMA-SD426EDU-DMPC437PSP-CA66PSS-CF2327DMM-SC145PMSA88QACL12317HSE77PPM-AS235RAE88ICASP279PSP-TDF1326SSC44EDPLC55KPS-EDU535PSP-NBT126SMA257SMCS112PSP-LMS54514TLS257ARICM22PSP-ALA55SARL358EDM224TRI-PAP325PSP-PA22AWP33TSBCP33AUD55CPU325DDC134EDMD-APS22EDM-PAAP327214HGS-AFS33RHS66SE-TE1111DAS-AARM55MPAP44EPMSS257LA66DLA8210SGP-NN325PAP1214MTQ55AD-SLS336CMPL145USWT66LAP55PSP22NSP-KDHED22MLM11ID-CS44SAP55PP-PSP33PSP-M88PA-PS22PF235IDF-SAP44SC-NF639OEP-TRF17210PCS22SVA-PC235PSL44SAP-EDC55PRD-AP22SSP-CL33QE-ELC55GRP-OEWB33SP-DMC22PPRD-DT44SUR-MSR44SPRD-ARMBA22EXP-HPF33IPT-SP347DM-ETSP77EDM-ASAP22SPP-DL22DM-E17311PSM-HOU33PSP-ML33PA-EDM22LS-PRDE246PRD-AP22HSC-SA133714PRI-MPP77SE-OES12317FCD-EP77EB-PSP235MSM-ENB88M-KME279EPRD-BL55MA-TE22PRD-SF-GP22SE-UT167SE-DRVPU33PRD-DFA336AANL-PISA44INT-INF44PRD-GR426ESEG-AP2158SAG-EDM22MSD-PRD134PRI-SRMA33SRS-AL34411SET-IFP66SRL-HYPM22SAP-DM448LAS-TEL134DMKMS268DSS-LE55FGCAC22SAPM22DM-PSP22OPCA11HESSP-PP33IOMC22DM-CRTL22DM-ED55FGSK-SP22EDM-ARW369GP-SSM11FENTP33TE-LMSF358RGTE33DOF-DTT11P-CSI33DTDM112PSP-SDMA54211SAS66ODF-AFQP22PSP-OLDF11EDM-S358EDM-RSA123ASP-DCBC426CSP-LCV22EEDM-IPC22PSP-C336PSP-DMT2810WUGC22SEDM-PSP44LAEDM-CC22PSP-EDT358TQSA-ES448PMTP33DFUS33SPP-CS123MED-CS44IAPP145MM-SN77TQ-CS538MA-FTE224FGAM77MRF-CA235GSM3233OCM33LRMP44IDK7411EDM22ILA-EDM55RPP1427SP-RBFNN&PCA44SP-MLR&PCA459WTM167SCS44LF-PP22SA55SET448WBLC123MRC33MBA-GL22SPM44S-GPA516PFDM66DMT-SN33PPS-COVID-1944ATI-F22NA-FD-COVID-19437COVID-19-AS22PI-COVID-1922SD-COVID-1922Edu-COVID-1922NCAS-COVID-1922Imp-COVID-19235SS-PPP-DM189A-EDM-TD33RPSP-DMT33SPP-CL66ER-KCP33SDP1111EDP-DM44PAP-SH77HMRS22IGR-PSP33PSPP-ML22ECE-RL332210SML-OC22Inf-COVID-1933PEEP-COVID-19257ATI-F459CCI-OC44ETES-COVID-19325TFL-SF44OC-BL66NP-PSP55SPP-BL22RSNL66DN-CS134PR-MS77DSF-HB123612VFP-C77EAK-P235SP-EG-MM88LMS-CAP279FDG22BFE33AD-CS235SP-ALA88TVL-CA279EAG-CSC44ML-CSC22SP-DM-LAT459S-GC44MR-PCQ33MPA-M123MCA-E437T-PR33NS-SE55ARFE33CSMA88NT-PPCS33MSG-IC55GD-ATC33GD-AT-SCI33LS-ESP-R44GD-SE22GD-AT-IT44GD-RC325GD-LTS22SG-TM-CAP246GDSL44GD-MS-SL358GD-MR55DSS-CP26210GD-TET44GD-HSS235TP-MA437GD-NCS123GD-SP-EC44SSG44GD-DSS358ETP-SSA55GES-E33PSD22IQ-PAP44TSI-SSC33BFP-MA347ESF-SS22FPP-AUS5611ACA369AAGT1359SLC-A235RHAS88PSO-LPS1231497


## 3.1. Review Process

This study reviews the earlier studies thoroughly based on the procedures prescribed by Petersen et al. [116] and Keele [117]. The methodology is adopted from Keele while the study mapping method is copied from Petersen et al. The review process is initiated with the modified procedure, which is demonstrated in Figure 1. For better understanding, the review delivers a detailed methodology of the prior work contributing to student performance prediction directly or indirectly. Moreover, the study put a list of research questions to demonstrate the main objectives. These research questions enable us to choose relevant research studies for screening and investigating the main challenges in students’ performance predictions. Every research question has a list of keywords to explore the literature and learn about a particular question. These keywords are used to search publications, including peer-reviewed book chapters, conferences proceeding, and journal articles.


## 3.2. Research Questions

1.
Q1: what are the applications of student performance prediction systems?2.
Q2: what are the factors that can optimize student performance prediction?3.
Q3: what is the intensity of research findings in the field of student performance prediction systems optimization?4.
Q4: are the findings of psychological studies, data mining, and contribution in algorithms synchronized with each other for the viability of the pilot project?5.
Q5: how synchronization and coordination of prior psychological, data mining, and algorithmic findings contribute to the effective educational system via student performance prediction algorithm.


## 3.3. Searching Keywords

The current study adopted a manual review methodology introduced by Keele [117]. The automatic review presented by Petersen et al. has a few disadvantages [116]. (1) The automatic search is not feasible for the current review [118]. (2) The manual searching strategy gives more relevant studies. Table 1 reflects the list of keywords that have produced a variety of articles published by various publishers, i.e., IEEE, Elsevier, Springer, Hindawi, MDPI, ACM, Wiley, and others. It shows many articles, including journals, book chapters, and conference proceedings.Table 1
Result obtained from the Google Scholar during keyword searching.
NoKeywordsIEEEACMSpringerMDPIHindawiElsevierWileyOthersTotal1Student performance prediction14138512777732Student performance and negative emotions984312976583Student emotional factors97122462334Work experience and student performance2010442534525Student biological factor and academics1156232510446Student academic achievements prediction117863516477Student frustration42522237278Student performance and frustration37764347419Student frustration severity57950813136010At-risk student prediction696111177116811At-risk student cognitive skills353843823612Cognitive skills prediction86474510105413Emotional impact on student performance697529755014Family impact on student achievements7259841155115Student anxiety9721167254916Student stress2981197275517Review on student performance3138345434318Student performance quantization8362112844419COVID-19, frustration, and student performance7553981145220COVID-19 and at-risk student888463624521Impact of online classes3288147324722Online classes and student learning71126981086123Learning prediction234482853624Student learning outcome prediction655496695025Student performance measurement2124484975026Performance measurement algorithm542496654127Performance prediction algorithm11456629115428Student performance evaluation algorithm726262363429Student performance prediction algorithm123948121556830Student performance measurement algorithm362375243231Cognitive skills prediction algorithm3757456542The keywords were searched directly on publishers’ websites and Google Scholar with a default setting. We have evaluated all the articles and collected those that deliver relevant findings for further screening. Furthermore, the main factors, topics, and relevant studies, including journals, conference proceedings, and book chapters, are given below:1.
Emotional attributes2.
Family factors3.
Study Schedule4.
Institutional attributes5.
Psychology, data mining, and data analysis findings on the factors as mentioned earlier6.
Contribution of cognitive computing, deep learning, and machine learning in students’ performance prediction7.
Reviews and comparison


## 3.4. Screening

Screening of studies is performed with the following terms and conditions:1.
The team selected the publications of the more relevant journal, conference, and book chapter.2.
Second, we have focused on the relevant title with impressive citations in Google Scholar.3.
Third, rapid reviews were performed for further evaluation and data extraction. During the rapid review, we have focused on the abstract and introduction to get some idea about the challenges, motivations, and contributions.These three steps were performed to create a database for further information extraction and data collection.


## 3.5. Information Collection

Various information was extracted from the selected publications during the information collection process, which are shown in Table1 to 4 Also, a spreadsheet was used to record the various information for further consideration of the research questions. The recorded data are shown in the tables mentioned above.Table 2
Research domain-wise keyword searching results and evaluation.
New methodsModificationData analysisPsychological findingsComparisonAnalysis of applicationReview workTotal number of publicationsIEEE1523686524109214ACM1416657013149201Springer11178732478166MDPI181248613613161Hindawi16227245171218202Elsevier221536704215164Wiley896632145617202Others53378713393187Table 3
Research domain-wise and factors-wise evaluation.
AttributesNew methodsModificationData analysisPsychological findingsComparisonAnalysis of applicationReview workTotal number of publicationsFrustration342624192431131Frustration severity0013334351499Stress2281913191679Stress severity0018189112177Anxiety19121417914691Anxiety severity02102221142493Depression00122023122188Parents’ influence4313116251678Distance from home and school33181313131982Mobile game222725171224109Outdoor game001972013766Indoor game00221721101080Watching TV00131024181681Students social network8121719724895Gender33101415151676Parents cohabitation status0011615181464Parent service245151132060International students005101171548Table 4
Intensity of various domain contributions.
Research outcomesNew methodsModificationData analysisPsychological findingsComparisonAnalysis of applicationReview workTotal number of publicationsSRI325PSP-PRP549AS-EDM33MAR-LD4610RFA6410MAR-KD77AF-TM11PSP-GPA6118PSP-MC5510EDU-DMCR459UPSP-EDU66PSP-OCS77PC-SD5510EDU-DMA33EDU-DMLA66SEDU-DM33EDU-DMA55SRCT7310PSBS1326EITE55ICAP77TEO55LAD55IEDU-PM66SAFP52310SPIE99PSS-TEDC729DMA-SD426EDU-DMPC437PSP-CA66PSS-CF2327DMM-SC145PMSA88QACL12317HSE77PPM-AS235RAE88ICASP279PSP-TDF1326SSC44EDPLC55KPS-EDU535PSP-NBT126SMA257SMCS112PSP-LMS54514TLS257ARICM22PSP-ALA55SARL358EDM224TRI-PAP325PSP-PA22AWP33TSBCP33AUD55CPU325DDC134EDMD-APS22EDM-PAAP327214HGS-AFS33RHS66SE-TE1111DAS-AARM55MPAP44EPMSS257LA66DLA8210SGP-NN325PAP1214MTQ55AD-SLS336CMPL145USWT66LAP55PSP22NSP-KDHED22MLM11ID-CS44SAP55PP-PSP33PSP-M88PA-PS22PF235IDF-SAP44SC-NF639OEP-TRF17210PCS22SVA-PC235PSL44SAP-EDC55PRD-AP22SSP-CL33QE-ELC55GRP-OEWB33SP-DMC22PPRD-DT44SUR-MSR44SPRD-ARMBA22EXP-HPF33IPT-SP347DM-ETSP77EDM-ASAP22SPP-DL22DM-E17311PSM-HOU33PSP-ML33PA-EDM22LS-PRDE246PRD-AP22HSC-SA133714PRI-MPP77SE-OES12317FCD-EP77EB-PSP235MSM-ENB88M-KME279EPRD-BL55MA-TE22PRD-SF-GP22SE-UT167SE-DRVPU33PRD-DFA336AANL-PISA44INT-INF44PRD-GR426ESEG-AP2158SAG-EDM22MSD-PRD134PRI-SRMA33SRS-AL34411SET-IFP66SRL-HYPM22SAP-DM448LAS-TEL134DMKMS268DSS-LE55FGCAC22SAPM22DM-PSP22OPCA11HESSP-PP33IOMC22DM-CRTL22DM-ED55FGSK-SP22EDM-ARW369GP-SSM11FENTP33TE-LMSF358RGTE33DOF-DTT11P-CSI33DTDM112PSP-SDMA54211SAS66ODF-AFQP22PSP-OLDF11EDM-S358EDM-RSA123ASP-DCBC426CSP-LCV22EEDM-IPC22PSP-C336PSP-DMT2810WUGC22SEDM-PSP44LAEDM-CC22PSP-EDT358TQSA-ES448PMTP33DFUS33SPP-CS123MED-CS44IAPP145MM-SN77TQ-CS538MA-FTE224FGAM77MRF-CA235GSM3233OCM33LRMP44IDK7411EDM22ILA-EDM55RPP1427SP-RBFNN&PCA44SP-MLR&PCA459WTM167SCS44LF-PP22SA55SET448WBLC123MRC33MBA-GL22SPM44S-GPA516PFDM66DMT-SN33PPS-COVID-1944ATI-F22NA-FD-COVID-19437COVID-19-AS22PI-COVID-1922SD-COVID-1922Edu-COVID-1922NCAS-COVID-1922Imp-COVID-19235SS-PPP-DM189A-EDM-TD33RPSP-DMT33SPP-CL66ER-KCP33SDP1111EDP-DM44PAP-SH77HMRS22IGR-PSP33PSPP-ML22ECE-RL332210SML-OC22Inf-COVID-1933PEEP-COVID-19257ATI-F459CCI-OC44ETES-COVID-19325TFL-SF44OC-BL66NP-PSP55SPP-BL22RSNL66DN-CS134PR-MS77DSF-HB123612VFP-C77EAK-P235SP-EG-MM88LMS-CAP279FDG22BFE33AD-CS235SP-ALA88TVL-CA279EAG-CSC44ML-CSC22SP-DM-LAT459S-GC44MR-PCQ33MPA-M123MCA-E437T-PR33NS-SE55ARFE33CSMA88NT-PPCS33MSG-IC55GD-ATC33GD-AT-SCI33LS-ESP-R44GD-SE22GD-AT-IT44GD-RC325GD-LTS22SG-TM-CAP246GDSL44GD-MS-SL358GD-MR55DSS-CP26210GD-TET44GD-HSS235TP-MA437GD-NCS123GD-SP-EC44SSG44GD-DSS358ETP-SSA55GES-E33PSD22IQ-PAP44TSI-SSC33BFP-MA347ESF-SS22FPP-AUS5611ACA369AAGT1359SLC-A235RHAS88PSO-LPS1231497


## 4. In-Dept Analysis

### 4.1. Q1: What Are the Applications of Student Performance Prediction Systems

A performance prediction system is essential to predict at-risk students to devise a solution for successful graduation and goal achievements, such as special treatment and counseling sessions. Such prediction systems are more challenging due to the significant factors affecting students’ performance. Thus, a systematic review of the literature has been performed to highlight potential issues in predicting student performance. The study also shows the contributions of previous articles beyond the scope of artificial intelligence, i.e., data mining, data analysis, and psychology techniques contributing to performance prediction. Also, this study provides an overview of prediction techniques that have been used to estimate performance. It focuses on how the predictive algorithm can be used to identify key attributes in influencing students’ academic achievements. With the help of data mining and machine learning techniques in education, the study could have a more effective methodology in proposing a new prediction algorithm and modifying existing students’ performance prediction systems. The primary application outcomes of students’ performance prediction are given below.


#### 4.1.1. Prediction of At-Risk Student

It is crucial to predict at-risk students and devise an effective learning environment in classrooms and laboratories. Although the literature studies are saturated with tremendous results, it is still challenging as the prediction system cannot synchronize and mathematically model emotional attributes, family issues, study schedules, and institutional attributes to develop a significant prediction system. The current review’s first target is to highlight the possibilities of predicting at-risk students while coordinating between literature studies.


#### 4.1.2. Advances the Students’ Academic Achievements

The performance prediction system is essential for at-risk, average, and excellent students. The influential factors that drive academic achievement are an eternal global challenge associated with students, families, teachers, and educational policymakers. Exploring these factors benefits all those interested in developing a system for students’ performance prediction worldwide. Suppose the prediction system considers a large number of influential factors. In that case, the academic achievement of excellent students can also be advanced, i.e., the prediction system could highlight problems due to various emotional, study schedules, family, and institute-related attributes.


#### 4.1.3. Monitoring Students’ Behavior

Student behavior plays a significant role in improving academic achievements, such as interaction and attitude with the teachers, seriousness, and unseriousness in the classroom. Articles of psychology and data analysis contribute to student behavior evaluation, merits, and demerits of various aspects of behavior. We need a prediction system that efficiently modulates the relationship between behavior and students’ performance to highlight, monitor, and improve students’ interaction and engagement in the classroom. It is also essential for the institution to devise effective controlling policies to counter and control the demerit of various behaviors. Through such a prediction system, teachers can easily guide their students in setting and achieving academic goals. A teacher can also help students understand their behavior and its impact on others. The adverse effects of behavior can be overcome and later on monitored by supervising students. Such a system enhances the overall reputation of the institution. Other benefits include preventing early school drop-ups and building good relationships among students. According to Kennelly and Monrad [119, 120], the behavioral problem plays a key role in indicating students at risk and highlighting the individuals near to being dropped off at the institute. Therefore, employing strategies to monitor and control student behavior is extremely important for an effective educational system in a society.


### 4.2. Q2: What Are the Factors That Can Optimize Student Performance Prediction?

The literature studies indicate that many factors influence students’ performance in cognitive activities, such as quizzes, assignments, examinations, and homework. It includes family-related factors, emotional factors, gender description, and institution-related factors. A brief description of these factors is given below.


#### 4.2.1. Family-Related Factors

The parental involvement and their particular influence are two-fold. First, the earlier studies claim that the interaction of parents positively influences performance. It enhances the academic achievements of the student in critical environments. Research results highlight that parents’ friendly attitudes positively affect student performance, such as daily engagement in cognitive activities. Positive parent involvement can advance the performance, and that father or mother is the first teacher who plays the role of an enduring educator. Such research findings show that parents’ positive and active role cannot be underestimated. Second, the overexpectation of parents can push children towards frustration [121, 122]. Parent mostly observes remarkable achievement on social media, so they also start demanding good grades from their children. With such pressure, students are easily frustrated, which negatively influences their academic outcome during cognitive activities, such as assignments, quizzes, and mid- and final-term examinations. So, the role of the parents should be supportive and motivational, which would help against unnecessary pressure.To achieve a student performance prediction system, we need to consider parental involvement and the aforementioned other attributes, such as the cohabitation status of parents, the relationship among their parents, socioeconomic situation, and the number of children. Prediction systems need to quantize all these attributes to evaluate future student performance properly. If we look into literature studies, a minimal contribution can be evidenced toward mathematical modeling of student performance for a better educational system.


#### 4.2.2. Emotional Factors

Emotional attributes play a fundamental role in impacting student performance during cognitive tasks. The current study discusses severity levels of frustration, anxiety, depression, and stress. The impact of frustration is the natural part of learning as well as the engaging session (for references, see the literature review section). Such emotion is always found during comprehensive cognitive activities. Literature saturated with many qualitative findings focused on the statistical association between student performance and frustration. However, the study has not been evidenced a comprehensive approach to solve the challenges produced by frustration during cognitive tasks.We need to analyze the performance of excellent, average, and at-risk students while mathematically modeling the relationship between institution-related attributes, students’ emotional factors, and family-related attributes. Also, the teacher can help frustrated students’ through collaborative exercises, group activities, and group assignments [123]. It will help students easily share their confusion and problems with group members to overcome their frustrations in a comprehensive learning environment. An individual can learn better in offline mode with face-to-face interaction as compared to online interaction [124]. Additionally, the COVID-19 outbreak has accelerated the influence of negative emotions on students’ performance. COVID-19 has created a more critical situation for students’ learning and adjusted them to the online environment with fewer resources. Thus, we are in dire need to evaluate the academic development of students while statistically associating the aforementioned factors and mathematically modeling the proposed relationship to prepare for the critical situation [125].


#### 4.2.3. Gender Description

In the literature review section, the study has shown that earlier studies statistically associated students’ performance with emotional attributes and gender description. Students perform differently while considering aging and gender [126]. Both emotion and gender need to evaluate differently during cognitive activities. Literature studies are evidenced with many contributions on gender differences. They show that different gender individuals perform differently during cognitive activities, solving assignments, attempting quizzes, and examinations. Earlier studies depict that gender difference is an independent biological factor whose magnitude is sometimes dependent on other factors such as cultures, socioeconomic condition, language, age, etc. Gender differences play a crucial role in influencing mental abilities and cognitive processing in mathematical tasks, physics, research, reading, and writing. These issues create a big gap between male and female individuals, referred to as natural and biological differences.


#### 4.2.4. Institution-Related Factors

Different institutional factors are directly or indirectly involved in influencing students’ performance. These factors include but are not limited to instructor teaching methodology, interaction with a student advisor, extracurricular activities in the institution, student complaint platform, the distance between the institution and students’ residence, transport facility, and the behaviors of the friends. These all factors have merits and demerits for student performance. The literature studies of psychology and data analysis have enormous contributions to student performance analysis; however, insignificant contributions have been reported in the form of algorithms and mathematical models in students’ performance prediction.


### 4.3. Q3: What Is the Intensity of Research Findings in the field of Student Performance Prediction Systems Optimization?

Literature reported many challenges because the students’ performance prediction overlaps psychology, data analysis, and mathematical and algorithmic contributions. The intensity of publications in the student performance prediction area is reported below.


#### 4.3.1. Intensity of Psychological Findings

As discussed earlier, we can find many psychological research contributions in the field of student performance analysis, which show that emotional attributes always affect students’ performance during cognitive activities. So, to provide an efficient solution for student performance prediction, the study must need to evaluate the psychological findings that directly or indirectly focus on student performance evaluation.


#### 4.3.2. Intensity of Data Analysis Findings

Data analysis contribution provides a quantitative measurement for student performance prediction. Such research findings pave the way for an accurate mathematical model to better contribute to the performance prediction area of research.


#### 4.3.3. Intensity of Students’ Performance Prediction Systems

The literature is also saturated with student performance prediction techniques focusing on students’ performance prediction in critical cognitive tasks; nevertheless, these findings are not synchronized and linked toward a significant student performance model. So, the main objective of this review paper is to provide an effective platform for future researchers in student performance prediction. It will pave the way for an effective system to predict at-risk students and excellent student performances, which ultimately provides us with the opportunity to enhance their skills and performance.


### 4.4. Q4: Are the Findings of Psychological Studies, Data Mining, and Contribution in Algorithms Are Synchronized with Each Other for the Viability of Pilot Project?

The intensity of publications contributing to student performance prediction is quite good, but these contributions are not synchronized with each other to mathematically model emotional, family, and institution-related attributes. One of the main objectives of the current review is to highlight the lack of coordination and synchronization of the literature from different research fields. This review would allow future readers of deep learning to collaborate with other research fields.


### 4.5. Q5: How Synchronization and Coordination of Prior Psychological, Data Mining, and Algorithmic Findings Contribute to the Effective Educational System via Student Performance Prediction Algorithm

Psychological literature produces both qualitative and quantitative findings in students’ performance prediction; nevertheless, the data analysis field highlights the association among students’ factors, i.e., emotional, family, and institutional attributes. If these findings are linked with the objective of qualitative data repositories and algorithms, then, we can move toward an efficient student performance prediction system. The psychological work produces accurate students’ emotional data focusing on their performance. On the other hand, the data analysis field makes the meaningful statistical association and correlation information. The data analysis field of research provides a couple of tests to find the correlation between student emotional attributes and their performance, i.e., Pearson correlation and regression. These tests verify the correlation among different factors.We are in dire need to have the abovementioned psychological and data analysis findings to propose a comprehensive algorithm. Every part of the student performance prediction area of research is interlinked. The psychological result verifies the emotional change during the evaluations of the frustration, severity, anxiety, and stress. Second, the data analysis findings associate the student attributes. Third, the student performance prediction algorithm mathematically model the statistical association among the student influencing factors and their performance outcome.


### 4.6. Specific Keywords-Wise Publications

This section intensively discusses the specific keyword-wise research output focusing on students’ performance, emotional factors, and prediction algorithms. The list of keywords is illustrated in Table1. The study collected articles based on these keywords for further technical assessment. The specific domain for the technical evaluation includes but is not limited to new methods, modifications in prior work, data analysis, psychological findings, application analysis, review work, and comparison. The self-explanatory Table 1 illustrated the intensity of publications in the domain above using the list of keywords.


### 4.7. Yearly Publications

Literature studies deliver thousand of research findings that directly or indirectly contribute to students’ performance analysis and prediction. As illustrated in Figure2, 37 published articles were evaluated (1990 to 1994). About 110 articles mainly focus on student performance and students’ study-related factors assessment. They have evaluated those factors that affect students’ performance during cognitive activities (1995 to 1999). From 2000 to 2004, the study included 144 articles on students’ performance and emotional attributes. The number of featured articles increases with time. From 2005 to 2009, we have assessed 310 articles that contributed to performance prediction.Figure 2
Yearly research contributions.Furthermore, we have collected 557 research studies that mainly focused on prediction algorithms. The researchers delivered a considerable amount articles from 2010 to 2014. We have found a slight decrease in analysis and psychological studies production until 2019; however, an increase was observed in algorithmic work from 2015 to 2019. Thus, 321 studies were considered during this segment of time. Eventually, the study reviewed 18 articles on students’ performance prediction algorithms published from 2020 to 2022. We have collected 1497 articles during the review process of the current study.


### 4.8. Domain-Wise Evaluation

The review paper carries out a perceptive analysis and literature-based survey to draw out an inclusive representation of the famous publishers who publish the most highly cited research papers. It involves overall 1497 publications, which are selected after searching on Google Scholar. These studies were evaluated upon their relevant findings, such as new methods, modified approaches, statistical findings, and psychological results (for more information see Table2 and Figure 3). The major part of this review was to assess the existing students’ performance prediction approaches. So, the study analytically assessed new students’ performance prediction measures, modifications in state-of-the-art techniques, and comparative analysis. Additionally, Figure 4 and Table 3 represent the detailed domain-wise and factors-wise analysis.Figure 3
Domain-wise and publishers-wise outcomes.Figure 4
Factors-wise and domain-wise outcomes.


## 4.1. Q1: What Are the Applications of Student Performance Prediction Systems

A performance prediction system is essential to predict at-risk students to devise a solution for successful graduation and goal achievements, such as special treatment and counseling sessions. Such prediction systems are more challenging due to the significant factors affecting students’ performance. Thus, a systematic review of the literature has been performed to highlight potential issues in predicting student performance. The study also shows the contributions of previous articles beyond the scope of artificial intelligence, i.e., data mining, data analysis, and psychology techniques contributing to performance prediction. Also, this study provides an overview of prediction techniques that have been used to estimate performance. It focuses on how the predictive algorithm can be used to identify key attributes in influencing students’ academic achievements. With the help of data mining and machine learning techniques in education, the study could have a more effective methodology in proposing a new prediction algorithm and modifying existing students’ performance prediction systems. The primary application outcomes of students’ performance prediction are given below.


### 4.1.1. Prediction of At-Risk Student

It is crucial to predict at-risk students and devise an effective learning environment in classrooms and laboratories. Although the literature studies are saturated with tremendous results, it is still challenging as the prediction system cannot synchronize and mathematically model emotional attributes, family issues, study schedules, and institutional attributes to develop a significant prediction system. The current review’s first target is to highlight the possibilities of predicting at-risk students while coordinating between literature studies.


### 4.1.2. Advances the Students’ Academic Achievements

The performance prediction system is essential for at-risk, average, and excellent students. The influential factors that drive academic achievement are an eternal global challenge associated with students, families, teachers, and educational policymakers. Exploring these factors benefits all those interested in developing a system for students’ performance prediction worldwide. Suppose the prediction system considers a large number of influential factors. In that case, the academic achievement of excellent students can also be advanced, i.e., the prediction system could highlight problems due to various emotional, study schedules, family, and institute-related attributes.


### 4.1.3. Monitoring Students’ Behavior

Student behavior plays a significant role in improving academic achievements, such as interaction and attitude with the teachers, seriousness, and unseriousness in the classroom. Articles of psychology and data analysis contribute to student behavior evaluation, merits, and demerits of various aspects of behavior. We need a prediction system that efficiently modulates the relationship between behavior and students’ performance to highlight, monitor, and improve students’ interaction and engagement in the classroom. It is also essential for the institution to devise effective controlling policies to counter and control the demerit of various behaviors. Through such a prediction system, teachers can easily guide their students in setting and achieving academic goals. A teacher can also help students understand their behavior and its impact on others. The adverse effects of behavior can be overcome and later on monitored by supervising students. Such a system enhances the overall reputation of the institution. Other benefits include preventing early school drop-ups and building good relationships among students. According to Kennelly and Monrad [119, 120], the behavioral problem plays a key role in indicating students at risk and highlighting the individuals near to being dropped off at the institute. Therefore, employing strategies to monitor and control student behavior is extremely important for an effective educational system in a society.


## 4.1.1. Prediction of At-Risk Student

It is crucial to predict at-risk students and devise an effective learning environment in classrooms and laboratories. Although the literature studies are saturated with tremendous results, it is still challenging as the prediction system cannot synchronize and mathematically model emotional attributes, family issues, study schedules, and institutional attributes to develop a significant prediction system. The current review’s first target is to highlight the possibilities of predicting at-risk students while coordinating between literature studies.


## 4.1.2. Advances the Students’ Academic Achievements

The performance prediction system is essential for at-risk, average, and excellent students. The influential factors that drive academic achievement are an eternal global challenge associated with students, families, teachers, and educational policymakers. Exploring these factors benefits all those interested in developing a system for students’ performance prediction worldwide. Suppose the prediction system considers a large number of influential factors. In that case, the academic achievement of excellent students can also be advanced, i.e., the prediction system could highlight problems due to various emotional, study schedules, family, and institute-related attributes.


## 4.1.3. Monitoring Students’ Behavior

Student behavior plays a significant role in improving academic achievements, such as interaction and attitude with the teachers, seriousness, and unseriousness in the classroom. Articles of psychology and data analysis contribute to student behavior evaluation, merits, and demerits of various aspects of behavior. We need a prediction system that efficiently modulates the relationship between behavior and students’ performance to highlight, monitor, and improve students’ interaction and engagement in the classroom. It is also essential for the institution to devise effective controlling policies to counter and control the demerit of various behaviors. Through such a prediction system, teachers can easily guide their students in setting and achieving academic goals. A teacher can also help students understand their behavior and its impact on others. The adverse effects of behavior can be overcome and later on monitored by supervising students. Such a system enhances the overall reputation of the institution. Other benefits include preventing early school drop-ups and building good relationships among students. According to Kennelly and Monrad [119, 120], the behavioral problem plays a key role in indicating students at risk and highlighting the individuals near to being dropped off at the institute. Therefore, employing strategies to monitor and control student behavior is extremely important for an effective educational system in a society.


## 4.2. Q2: What Are the Factors That Can Optimize Student Performance Prediction?

The literature studies indicate that many factors influence students’ performance in cognitive activities, such as quizzes, assignments, examinations, and homework. It includes family-related factors, emotional factors, gender description, and institution-related factors. A brief description of these factors is given below.


### 4.2.1. Family-Related Factors

The parental involvement and their particular influence are two-fold. First, the earlier studies claim that the interaction of parents positively influences performance. It enhances the academic achievements of the student in critical environments. Research results highlight that parents’ friendly attitudes positively affect student performance, such as daily engagement in cognitive activities. Positive parent involvement can advance the performance, and that father or mother is the first teacher who plays the role of an enduring educator. Such research findings show that parents’ positive and active role cannot be underestimated. Second, the overexpectation of parents can push children towards frustration [121, 122]. Parent mostly observes remarkable achievement on social media, so they also start demanding good grades from their children. With such pressure, students are easily frustrated, which negatively influences their academic outcome during cognitive activities, such as assignments, quizzes, and mid- and final-term examinations. So, the role of the parents should be supportive and motivational, which would help against unnecessary pressure.To achieve a student performance prediction system, we need to consider parental involvement and the aforementioned other attributes, such as the cohabitation status of parents, the relationship among their parents, socioeconomic situation, and the number of children. Prediction systems need to quantize all these attributes to evaluate future student performance properly. If we look into literature studies, a minimal contribution can be evidenced toward mathematical modeling of student performance for a better educational system.


### 4.2.2. Emotional Factors

Emotional attributes play a fundamental role in impacting student performance during cognitive tasks. The current study discusses severity levels of frustration, anxiety, depression, and stress. The impact of frustration is the natural part of learning as well as the engaging session (for references, see the literature review section). Such emotion is always found during comprehensive cognitive activities. Literature saturated with many qualitative findings focused on the statistical association between student performance and frustration. However, the study has not been evidenced a comprehensive approach to solve the challenges produced by frustration during cognitive tasks.We need to analyze the performance of excellent, average, and at-risk students while mathematically modeling the relationship between institution-related attributes, students’ emotional factors, and family-related attributes. Also, the teacher can help frustrated students’ through collaborative exercises, group activities, and group assignments [123]. It will help students easily share their confusion and problems with group members to overcome their frustrations in a comprehensive learning environment. An individual can learn better in offline mode with face-to-face interaction as compared to online interaction [124]. Additionally, the COVID-19 outbreak has accelerated the influence of negative emotions on students’ performance. COVID-19 has created a more critical situation for students’ learning and adjusted them to the online environment with fewer resources. Thus, we are in dire need to evaluate the academic development of students while statistically associating the aforementioned factors and mathematically modeling the proposed relationship to prepare for the critical situation [125].


### 4.2.3. Gender Description

In the literature review section, the study has shown that earlier studies statistically associated students’ performance with emotional attributes and gender description. Students perform differently while considering aging and gender [126]. Both emotion and gender need to evaluate differently during cognitive activities. Literature studies are evidenced with many contributions on gender differences. They show that different gender individuals perform differently during cognitive activities, solving assignments, attempting quizzes, and examinations. Earlier studies depict that gender difference is an independent biological factor whose magnitude is sometimes dependent on other factors such as cultures, socioeconomic condition, language, age, etc. Gender differences play a crucial role in influencing mental abilities and cognitive processing in mathematical tasks, physics, research, reading, and writing. These issues create a big gap between male and female individuals, referred to as natural and biological differences.


### 4.2.4. Institution-Related Factors

Different institutional factors are directly or indirectly involved in influencing students’ performance. These factors include but are not limited to instructor teaching methodology, interaction with a student advisor, extracurricular activities in the institution, student complaint platform, the distance between the institution and students’ residence, transport facility, and the behaviors of the friends. These all factors have merits and demerits for student performance. The literature studies of psychology and data analysis have enormous contributions to student performance analysis; however, insignificant contributions have been reported in the form of algorithms and mathematical models in students’ performance prediction.


## 4.2.1. Family-Related Factors

The parental involvement and their particular influence are two-fold. First, the earlier studies claim that the interaction of parents positively influences performance. It enhances the academic achievements of the student in critical environments. Research results highlight that parents’ friendly attitudes positively affect student performance, such as daily engagement in cognitive activities. Positive parent involvement can advance the performance, and that father or mother is the first teacher who plays the role of an enduring educator. Such research findings show that parents’ positive and active role cannot be underestimated. Second, the overexpectation of parents can push children towards frustration [121, 122]. Parent mostly observes remarkable achievement on social media, so they also start demanding good grades from their children. With such pressure, students are easily frustrated, which negatively influences their academic outcome during cognitive activities, such as assignments, quizzes, and mid- and final-term examinations. So, the role of the parents should be supportive and motivational, which would help against unnecessary pressure.To achieve a student performance prediction system, we need to consider parental involvement and the aforementioned other attributes, such as the cohabitation status of parents, the relationship among their parents, socioeconomic situation, and the number of children. Prediction systems need to quantize all these attributes to evaluate future student performance properly. If we look into literature studies, a minimal contribution can be evidenced toward mathematical modeling of student performance for a better educational system.


## 4.2.2. Emotional Factors

Emotional attributes play a fundamental role in impacting student performance during cognitive tasks. The current study discusses severity levels of frustration, anxiety, depression, and stress. The impact of frustration is the natural part of learning as well as the engaging session (for references, see the literature review section). Such emotion is always found during comprehensive cognitive activities. Literature saturated with many qualitative findings focused on the statistical association between student performance and frustration. However, the study has not been evidenced a comprehensive approach to solve the challenges produced by frustration during cognitive tasks.We need to analyze the performance of excellent, average, and at-risk students while mathematically modeling the relationship between institution-related attributes, students’ emotional factors, and family-related attributes. Also, the teacher can help frustrated students’ through collaborative exercises, group activities, and group assignments [123]. It will help students easily share their confusion and problems with group members to overcome their frustrations in a comprehensive learning environment. An individual can learn better in offline mode with face-to-face interaction as compared to online interaction [124]. Additionally, the COVID-19 outbreak has accelerated the influence of negative emotions on students’ performance. COVID-19 has created a more critical situation for students’ learning and adjusted them to the online environment with fewer resources. Thus, we are in dire need to evaluate the academic development of students while statistically associating the aforementioned factors and mathematically modeling the proposed relationship to prepare for the critical situation [125].


## 4.2.3. Gender Description

In the literature review section, the study has shown that earlier studies statistically associated students’ performance with emotional attributes and gender description. Students perform differently while considering aging and gender [126]. Both emotion and gender need to evaluate differently during cognitive activities. Literature studies are evidenced with many contributions on gender differences. They show that different gender individuals perform differently during cognitive activities, solving assignments, attempting quizzes, and examinations. Earlier studies depict that gender difference is an independent biological factor whose magnitude is sometimes dependent on other factors such as cultures, socioeconomic condition, language, age, etc. Gender differences play a crucial role in influencing mental abilities and cognitive processing in mathematical tasks, physics, research, reading, and writing. These issues create a big gap between male and female individuals, referred to as natural and biological differences.


## 4.2.4. Institution-Related Factors

Different institutional factors are directly or indirectly involved in influencing students’ performance. These factors include but are not limited to instructor teaching methodology, interaction with a student advisor, extracurricular activities in the institution, student complaint platform, the distance between the institution and students’ residence, transport facility, and the behaviors of the friends. These all factors have merits and demerits for student performance. The literature studies of psychology and data analysis have enormous contributions to student performance analysis; however, insignificant contributions have been reported in the form of algorithms and mathematical models in students’ performance prediction.


## 4.3. Q3: What Is the Intensity of Research Findings in the field of Student Performance Prediction Systems Optimization?

Literature reported many challenges because the students’ performance prediction overlaps psychology, data analysis, and mathematical and algorithmic contributions. The intensity of publications in the student performance prediction area is reported below.


### 4.3.1. Intensity of Psychological Findings

As discussed earlier, we can find many psychological research contributions in the field of student performance analysis, which show that emotional attributes always affect students’ performance during cognitive activities. So, to provide an efficient solution for student performance prediction, the study must need to evaluate the psychological findings that directly or indirectly focus on student performance evaluation.


### 4.3.2. Intensity of Data Analysis Findings

Data analysis contribution provides a quantitative measurement for student performance prediction. Such research findings pave the way for an accurate mathematical model to better contribute to the performance prediction area of research.


### 4.3.3. Intensity of Students’ Performance Prediction Systems

The literature is also saturated with student performance prediction techniques focusing on students’ performance prediction in critical cognitive tasks; nevertheless, these findings are not synchronized and linked toward a significant student performance model. So, the main objective of this review paper is to provide an effective platform for future researchers in student performance prediction. It will pave the way for an effective system to predict at-risk students and excellent student performances, which ultimately provides us with the opportunity to enhance their skills and performance.


## 4.3.1. Intensity of Psychological Findings

As discussed earlier, we can find many psychological research contributions in the field of student performance analysis, which show that emotional attributes always affect students’ performance during cognitive activities. So, to provide an efficient solution for student performance prediction, the study must need to evaluate the psychological findings that directly or indirectly focus on student performance evaluation.


## 4.3.2. Intensity of Data Analysis Findings

Data analysis contribution provides a quantitative measurement for student performance prediction. Such research findings pave the way for an accurate mathematical model to better contribute to the performance prediction area of research.


## 4.3.3. Intensity of Students’ Performance Prediction Systems

The literature is also saturated with student performance prediction techniques focusing on students’ performance prediction in critical cognitive tasks; nevertheless, these findings are not synchronized and linked toward a significant student performance model. So, the main objective of this review paper is to provide an effective platform for future researchers in student performance prediction. It will pave the way for an effective system to predict at-risk students and excellent student performances, which ultimately provides us with the opportunity to enhance their skills and performance.


## 4.4. Q4: Are the Findings of Psychological Studies, Data Mining, and Contribution in Algorithms Are Synchronized with Each Other for the Viability of Pilot Project?

The intensity of publications contributing to student performance prediction is quite good, but these contributions are not synchronized with each other to mathematically model emotional, family, and institution-related attributes. One of the main objectives of the current review is to highlight the lack of coordination and synchronization of the literature from different research fields. This review would allow future readers of deep learning to collaborate with other research fields.


## 4.5. Q5: How Synchronization and Coordination of Prior Psychological, Data Mining, and Algorithmic Findings Contribute to the Effective Educational System via Student Performance Prediction Algorithm

Psychological literature produces both qualitative and quantitative findings in students’ performance prediction; nevertheless, the data analysis field highlights the association among students’ factors, i.e., emotional, family, and institutional attributes. If these findings are linked with the objective of qualitative data repositories and algorithms, then, we can move toward an efficient student performance prediction system. The psychological work produces accurate students’ emotional data focusing on their performance. On the other hand, the data analysis field makes the meaningful statistical association and correlation information. The data analysis field of research provides a couple of tests to find the correlation between student emotional attributes and their performance, i.e., Pearson correlation and regression. These tests verify the correlation among different factors.We are in dire need to have the abovementioned psychological and data analysis findings to propose a comprehensive algorithm. Every part of the student performance prediction area of research is interlinked. The psychological result verifies the emotional change during the evaluations of the frustration, severity, anxiety, and stress. Second, the data analysis findings associate the student attributes. Third, the student performance prediction algorithm mathematically model the statistical association among the student influencing factors and their performance outcome.


## 4.6. Specific Keywords-Wise Publications

This section intensively discusses the specific keyword-wise research output focusing on students’ performance, emotional factors, and prediction algorithms. The list of keywords is illustrated in Table1. The study collected articles based on these keywords for further technical assessment. The specific domain for the technical evaluation includes but is not limited to new methods, modifications in prior work, data analysis, psychological findings, application analysis, review work, and comparison. The self-explanatory Table 1 illustrated the intensity of publications in the domain above using the list of keywords.


## 4.7. Yearly Publications

Literature studies deliver thousand of research findings that directly or indirectly contribute to students’ performance analysis and prediction. As illustrated in Figure2, 37 published articles were evaluated (1990 to 1994). About 110 articles mainly focus on student performance and students’ study-related factors assessment. They have evaluated those factors that affect students’ performance during cognitive activities (1995 to 1999). From 2000 to 2004, the study included 144 articles on students’ performance and emotional attributes. The number of featured articles increases with time. From 2005 to 2009, we have assessed 310 articles that contributed to performance prediction.Figure 2
Yearly research contributions.Furthermore, we have collected 557 research studies that mainly focused on prediction algorithms. The researchers delivered a considerable amount articles from 2010 to 2014. We have found a slight decrease in analysis and psychological studies production until 2019; however, an increase was observed in algorithmic work from 2015 to 2019. Thus, 321 studies were considered during this segment of time. Eventually, the study reviewed 18 articles on students’ performance prediction algorithms published from 2020 to 2022. We have collected 1497 articles during the review process of the current study.


## 4.8. Domain-Wise Evaluation

The review paper carries out a perceptive analysis and literature-based survey to draw out an inclusive representation of the famous publishers who publish the most highly cited research papers. It involves overall 1497 publications, which are selected after searching on Google Scholar. These studies were evaluated upon their relevant findings, such as new methods, modified approaches, statistical findings, and psychological results (for more information see Table2 and Figure 3). The major part of this review was to assess the existing students’ performance prediction approaches. So, the study analytically assessed new students’ performance prediction measures, modifications in state-of-the-art techniques, and comparative analysis. Additionally, Figure 4 and Table 3 represent the detailed domain-wise and factors-wise analysis.Figure 3
Domain-wise and publishers-wise outcomes.Figure 4
Factors-wise and domain-wise outcomes.


## 5. Potential Future Challenges

A large number of factors are involved in influencing students’ performance; therefore, the prediction system needs to be optimized to consider the impacts of different human factors categories. Such factors categories include but are not limited to emotional attributes, study schedule, family attributes, and institutional attributes. Each category consists of multiple factors impacting students’ performance, either negatively or positively. In the literature section, the study provides a detailed discussion of these factors.


### 5.1. Potential Pilot Projects Based on the Assumption-Based Dataset

The comprehensive synchronization between the earlier studies is still a black box, which increases systems’ dependency on a real-world dataset. The importance of a real-world dataset cannot be avoided; however, the data collection process is time-consuming and need a list of human resources. It delays the optimization of existing approaches, such as modeling students’ emotional attributes. The data collection process could have various anomalies if the researcher does not follow the analysis of earlier studies. The earlier studies offer excellent opportunities to understand the effectiveness of emotional attributes for optimization. Therefore, pilot projects perform key roles in optimizing the existing students’ performance prediction systems. They provide useful ideas during the data collection process. They also pave the way for an assumption-based dataset to prove the viability of novel ideas in students’ performance prediction.


## 5.1. Potential Pilot Projects Based on the Assumption-Based Dataset

The comprehensive synchronization between the earlier studies is still a black box, which increases systems’ dependency on a real-world dataset. The importance of a real-world dataset cannot be avoided; however, the data collection process is time-consuming and need a list of human resources. It delays the optimization of existing approaches, such as modeling students’ emotional attributes. The data collection process could have various anomalies if the researcher does not follow the analysis of earlier studies. The earlier studies offer excellent opportunities to understand the effectiveness of emotional attributes for optimization. Therefore, pilot projects perform key roles in optimizing the existing students’ performance prediction systems. They provide useful ideas during the data collection process. They also pave the way for an assumption-based dataset to prove the viability of novel ideas in students’ performance prediction.


## 6. Additional Points of Earlier Studies

Data analysis findings explore the hidden patterns and statistical correlation between students’ performance and influential factors. Such opportunities introduce new challenges for students’ performance prediction systems, e.g., conditional probabilities, correlation, and inferencing. Also, data mining studies are evidenced with many findings in students’ performance prediction area of research; nevertheless, they have different limitations, e.g., lack of in-depth investigation of students’ performance based on selected study-related factors, limited scalabilities, limited dataset, and inadequate qualitative approach of data analysis and psychological studies.Finally, the review shows that various prior students’ performance prediction methods have been proposed in the last decade; however, meagre studies have highlighted the basic need for synchronization among the abovementioned field’s contributions. Therefore, this review provides an exclusive picture of the future challenges in students’ performance prediction (see Table1 to 4 and Figure 2 to 4). On one hand, Table 4 depicts the intensity of various optimization techniques, review works, and new students’ performance prediction methods. On the other hand, Table 5 represents the acronyms of the selected studies. Remarks and recommendations against each research question are given in the self-explanatory Table 6.Table 5
Abbreviation and acronym.
AbbreviationAcronymSRIThe dimensionality of student ratings of instruction: what we know and what we do notPSP-PRPPredicting student performance on post-requisite skills using prerequisiteAS-EDMAn approachable analytical study on big educational data miningMAR-LDMining association rules between sets of items in large databasesRFAClarify of the random forest algorithm in an educational fieldMAR-KDKnowledge discovery from academic data using association rule miningAF-TMHow automated feedback through text mining changes plagiaristic behavior in online assignmentsPSP-GPAPredicting students final GPA using decision treesPSP-MCAnalyzing students performance using multicriteria classificationEDU-DMCRData mining in educational technology classroom researchUPSP-EDUAnalyzing undergraduate students’ performance using educational data miningPSP-OCSStudent performance predicition and optimal course selectionPC-SDProbabilistic classifiers and statistical dependencyEDU-DMAEducational data mining: an advance for intelligent systems in educationEDU-DMLAEducational data mining and learning analyticsSEDU-DMThe state of educational data mining in 2009EDU-DMAEducational data mining applications and tasksSRCTStudent ratings of college teachingPSBSPredicting drop-out from social behavior of studentsEITEEnsemble learning for estimating individualized treatment effects in student success studiesICAPIdentifying the comparative academic performance of secondary schoolsTEOTaxonomy of educational objectivesLADThe design, development, and implementation of student-facing learning analytics dashboardsIEDU-PMClustering for improving educational process miningSAFPDetermining students’ academic failure profile founded on data mining methodsSPIEStudent perceptions and instructional evaluationsPSS-TEDUPredicting student success using data generated in traditional educational environmentsDMA-SDData mining application on students’ dataEDU-DMPCEducational data mining for prediction and classification of engineering students achievementPSP-CAA comparative analysis of techniques for predicting student performancePSS-CFPredicting students success in courses via collaborative filteringDMM-SCData mining models for student careersPMSABlending measures of programming and social behavior into predictive models of students achievement in early computing coursesQACLQuantitative approach to collaborative learningHSEWill teachers receive higher student evaluations by giving higher grades and less course work?PPM-ASStudent performance prediction model for early-identification of at-risk students in traditional classroom settingsRAERegression analysis by exampleICASPMining the impact of course assignments on student performancePSP-TDFPredicting student performance in an ITS using task-driven featuresSSCSoft subspace clustering of categorical data with probabilistic distanceEDPLCEarly detection prediction of learning outcomes in online short-courses via learning behaviorsKPS-EDUTracking knowledge proficiency of students with educational priorsPSP-NBTExploration of classification using NB tree for predicting students’ performanceSMAStudent modeling approaches: a literature review for the last decadeSMCSAn ontological approach for semantic modeling of curriculum and syllabus in higher educationPSP-LMSPredicting student performance from LMS dataTLROrganizing knowledge syntheses: a taxonomy of literature reviewsARICMAnalysis of academic results for informatics course improvement using association rule miningPSP-ALAPredicting student performance using advanced learning analyticsSARLSeeding the survey and analysis of research literature with text miningEDMA systematic review of educational data miningTRI-PAPDo the timeliness, regularity, and intensity of online work habits predict academic performance?PSP-PAPredicting student performance using personalized analyticsAWPAutomated analysis of aspects of written argumentationTSBCPPredicting performance form test scores using back propagation and counter propagationAUDThe text mining handbook: advanced approaches in analyzing unstructured data,cambridgeCPUCell phone usage and academic performanceDDCLearning analytics: drives, developments and challengesEDMD-APSEducational data mining discovery standards of academic performance by studentsEDM-PAAPEducational data mining: predictive analysis of academic performanceHGS-AFSDo high grading standards affect student performance?RHSRetrieving hierarchical syllabus items for exam question analysisSE-TEAre student evaluations of teaching effectiveness valid for measuring student learning outcomes in business related classes?DAS-AARMDrawbacks and solutions of applying association rule mining in learning management systemsMPAPModel prediction of academic performance for first year studentsEPMSSEvaluating predictive models of student success: closing the methodological gapLALearning analytics should not promote one size fits allDLADetecting learning strategies with analytics: links with self-reported measures and academic performanceSGP-NNExplaining student grades predicted by a neural networkPAPPredicting academic performanceMTQMeasuring teaching quality in higher educationAD-SLSTowards automatically detecting whether student learning is shallowCMPLAn application of classification models to predict learner progression in tertiary educationUSWTUtilizing semantic web technologies and data mining techniques to analyze students learning and predict final performanceLAPA model to predict low academic performance at a specific enrollment using data miningPSPPredicting students performance in educational data miningNSP-KDHEDA new student performance analysing system using knowledge discovery in higher educational databases.MLMComparison of machine learning methods for intelligent tutoring systemsID-CSIndividual differences related to college students’ course performance in calculus ‖SAPStudent academic performance prediction by using a decision tree algorithm.PP-PSPPerformance prediction based on particle swarm optimizationPSP-MPoverty and student performance in MalaysiaPA-PSPhysical activity is not related to performance at schoolPFThe power of feedback, review of educational researchIDF-SAPIdentifying key factors of student academic performance by subgroup discoverySC-NFStudent classification for academic performance prediction using neuro fuzzy in a conventional classroomOEP-TRFOnline education performance predication via time-related featuresPCSProgramming content semantics: an evaluation of visual analytics approachSVA-PCSemantic visual analytics for today’s programming coursesPSLA systematic review of studies on predicting student learning outcomes using analyticsSAP-EDCPredicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical modelsPRD-APPredicting student’s academic performance: comparing artificial neural network, decision tree, and linear regressionSSP-CLAnalyzing student spatial deployment in a computer laboratoryQE-ELCQuality enhancement for e-learning courses: the role of student feedbackGRP-OEWBImproving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling techniqueSP-DMCStudent performance prediction by using data mining classification algorithmsPPRD-DTPerformance prediction of engineering students using decision treesSUR-MSRA survey and taxonomy of approaches for mining software repositories in the context of software evolutionSPRD-ARMBAA review and performance prediction of students’ using an association rule mining based approachEXP-HPFExploring the high potential factors that affects students’ academic performanceIPT-SPAnalysing the impact of poor teaching on student performanceDM-ETSPData mining based analysis to explore the effect of teaching on student performanceSPP-DLGritnet: student performance prediction with deep learningDM-EData mining and educationPSM-HOUPredicting students marks in hellenic open universityPSP-MLPredicting postgraduate students’ performance using machine learning techniquesPA-EDMReview on prediction algorithms in educational data miningLS-PRDELiterature survey on student’s performance prediction in education using data mining techniquesPRD-APPredicting student academic performanceHSC-SAOnline self-paced high-school class size and student achievementPRI-MPPPredictor relative importance and matching regression parametersSE-OESFinding similar exercises in online education systemsFCD-EPFuzzy cognitive diagnosis for modeling examine performanceEB-PSPAn ensemble-based semi-supervised approach for predicting students’ performanceMSM-ENBMeasuring the (dis-) similarity between expert and novice behaviors as serious games analyticsM-KMEMining for topics to suggest knowledge model extensionsEPRD-BLApplying learning analytics for the early prediction of students’ academic performance in blended learningMA-TEWhose feedback? A multilevel analysis of student completion of end-of-term teaching evaluationsPRD-SF-GPPredicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced dataSE-UTStudents’ evaluations of university teaching: Dimensionality, reliability, validity, potential biases and usefulnessSE-DRVPUStudents’ evaluations of university teaching: Dimensionality, reliability, validity, potential biases and usefulnessPRD-DFAPredicting student outcomes using discriminant function analysisAANL-PISAAn overview of using academic analytics to predict and improve students’ achievement: a proposed proactive intelligent interventionINT-INFConstructing interpretive inferences about literary text: the role of domain-specific knowledgePRD-GRPredicting gradesESEG-APEarly segmentation of students according to their academic performance: a predictive modeling approachSAG-EDMA framework for smart academic guidance using educational data miningMSD-PRDMining students’ data for prediction performancePRI-SRMAPreferred reporting items for systematic reviews and meta-analyses: the PRISMA statementSRS-ALA semantic recommender system for adaptive learningSET-IFPStudents evaluating teachers: exploring the importance of faculty reaction to feedback on teachingSRL-HYPMSelf-regulated learning with hypermedia: the role of prior domain knowledgeSAP-DMModeling and predicting students’ academic performance using data mining techniquesLAS-TELLexical analysis of syllabi in the area of technology enhanced learningDMKMSStudent data mining solution-knowledge management systemDSS-LEDecoding student satisfaction: how to manage and improve laboratory experienceFGCACStudent ability best predicts final grade in a college algebra courseSAPMStudent academic performance monitoring and evaluationDM-PSPData mining approach for predicting student performanceOPCAOptimizing partial credit algorithmsHESSP-PPIs alcohol affecting higher education students' performance: searching and predicting patternIOMCTowards the integration of multiple classifier pertaining to the student’s performance predictionDM-CRTLA data mining view on classroom teaching languageDM-EDApplication of data mining in educational databases for predicting academic trends and patternsFGSK-SPUsing fine-grained skill models to fit student performanceEDM-ARWEducational data mining: a survey and a data mining-based analysis of recent worksGP-SSMGrade prediction with course and student specific modelsFENTPFeature extraction for next-term prediction of poor student performanceTE-LMSFTeaching evaluation using data mining on moodle LMS forumRGTEThe role of gender in students’ ratings of teaching quality in computer science and environmental engineeringDOF-DTTDrop out feature of student data for academic performance using decision tree techniquesP-CSIProgramming: predicting student success early in CSIDTDMDecision trees and decision-makingPSP-SDMAPredicting student performance: a statistical and data mining approachSASA sentiment analysis system to improve teaching and learningODF-AFQPOntology driven framework for assessing the syllabus fairness of a question paperPSP-OLDFPredicting students’ final performance from participation in on-line discussion forumsEDM-SEducational data mining: a survey from 1995 to 2005EDM-RSAEducational data mining: a review of the state of the artASP-DCBCAnalyzing student performance using sparse data of core bachelor coursesCSP-LCVCentralized student performance prediction in large courses based on low-cost variables in an institutional contextEEDM-IPCEvaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming coursesPSP-CPrediction of students’ academic performance using clusteringPSP-DMTA review on predicting students’ performance using data mining techniquesWUGCWeb-based undergraduate chemistry problem-solving: the interplay of task performance, domain knowledge and web-searching strategiesSEDM-PSPA survey on various aspects of education data mining in predicting student performanceLAEDM-CCLearning analytics and educational data mining: towards communication and collaborationPSP-EDTPredictive modeling of students performance through the enhanced decision treeTQSA-ESWhat is the relationship between teacher quality and student achievement? An expletory studyPMTPA predictive model for standardized test performance in Michigan schoolsDFUSDetermination of factors influencing the achievement of the first-year university studentsSPP-CSNext-terms student performance prediction: a case studyMED-CSMining educational data to improve students’ performance: a case studyIAPPImproving academic performance prediction by dealing with class imbalanceMM-SNProposing stochastic probability-based math model and algorithms utilizing social networking and academic dataTQ-CSTeaching quality matters in higher education: a case studyMA-FTEMeta-analysis of faculty’s teaching effectiveness: student evaluation of teaching ratings and student learningFGAMAnalysis of the impact of action order on future performance: the fine-grain action modelMRF-CAMap-reduce framework based cluster architecture for academic students’ performance predictionGSMGoogle Scholar coverage of a multidisciplinary fieldOCMThe opportunity count model: a flexible approach to modeling student performanceLRMPPredicting students’ performance in final examination using linear regression and multilayer perceptronIDKFast searching for information on the internet to use in a learning context: the impact of domain knowledgeEDMEducational data mining acceptance among undergraduate studentsILA-EDMParticipation-based student final performance prediction model through interpretable genetic programming: integrating learning analytics, educational data mining and theoryRPPImproving retention performance prediction with prerequisite skill featuresSP-RBFNN&PCAPredicting honors student performance using RBFNN and PCA methodSP-MLR&PCAPredicting students’ academic performance using multiple linear regression and principal component analysisWTMWeb-based collaborative writing in L2 contexts: methodological insights from text miningSCSChinese undergraduates’ perceptions of teaching quality and the effects on approaches to studying and course satisfactionLF-PPCan online discussion participation predict group project performance? Investigating the roles of linguistic features and participation patternsSAImproving early prediction of academic failure using sentiment analysis on self-evaluated commentsSETThe use and misuse of student evaluations of teachingWBLCA multivariate approach to predicting student outcomes in web-enabled blended learning coursesMRCMendeley: creating communities of scholarly inquiry through research collaborationMBA-GLA model-based approach to predicting graduate-level performance using indicators of undergraduate-level performanceSPMStudents performance modeling based on behavior patternS-GPAPredicting students’ GPA and developing intervention strategies based on self-regulatory learning behaviorsPFDMTowards parameter-free data mining: mining ‘educational data with yacareeDMT-SNA survey of data mining techniques for social network analysisPPS-COVID19New realities for polish primary school informatics education affected by COVID-19ATI-FAffect-targeted interviews for understanding student frustrationNA-FD-COVID19Unhappy or unsatisfied: distinguishing the role of negative affect and need frustration in depressive symptoms over the academic year and during the COVID-19 pandemicCOVID19-ASCOVID-19 disruption on college students: academic and socioemotional implicationsPI-COVID19The psychological impact of COVID-19 on the mental health of the general populationSD-COVID19Social distancing in covid-19: what are the mental health implications?Edu-COVID19Education and the COVID-19 pandemicNCAS-COVID19Negative emotions, cognitive load, acceptance, and self-perceived learning outcome in emergency remote education during COVID-19Imp-COVID19The impact of COVID-19 on education insights from education at a glance 2020SS-PPP-DMStudy on student performance estimation, student progress analysis, and student potential prediction based on data miningA-EDM-TDApplication of educational data mining approach for student academic performance prediction using progressive temporal dataRPSP-DMTA review on predicting students’ performance using data mining techniquesSPP-CLStudent performance analysis and prediction in classroom learning: a review of educational data mining studiesER-KCPExercise recommendation based on knowledge concept predictionSDPStudent dropout predictionEDP-DMEarly dropout prediction using data mining: a case study with high school studentsPAP-SHPredicting academic performance by considering student heterogeneityHMRSHelping university students to choose elective courses by using a hybrid multicriteria recommendation system with genetic optimizationIGR-PSPInductive Gaussian representation of user-specific information for personalized stress-level predictionPSPP-MLPre-course student performance prediction with multi-instance multi-label learningECE-RLWhat students want? Experiences, challenges, and engagement during emergency remote learning amidst COVID-19 crisisSML-OCA survey of machine learning approaches for student dropout prediction in online coursesInf-COVID19Covid-19 and student performance, equity, and us education policy: lessons from pre-pandemic research to inform relief, recovery, and rebuildingPEEP-COVID19COVID19 and student performance equity, and us education Policy: Lessons from pre-pandemic research to inform relief, recovery, and rebuliding.ATI-F“Affect-targeted interviews for understanding student frustration”, in international conference on artificial intelligence in educationCCI-OCCommon challenges for instructors in large online course: strategies to mitigate student and instructor frustrationETES-COVID19Effective teaching and examination strategies for undergraduate learning during COVID-19 school restrictionsTFL-SFTeacher feedback literacy and its interplay with student feedback literacyOC-BLChallenges in the online component of blended learning: a systematic reviewNP-PSPFeature extraction for next-term prediction of poor student performanceSPP-BLStudent performance prediction based on blended learningRSNLRobust student network learningDN-CSDeep network for the iterative estimations of students’ cognitive skillsPR-MSParents’ role in the academic motivation of students with gifts and talentsDSF-HBDetecting student frustration based on handwriting behaviorVFP-CThe validity of a frustration paradigm to assess the effect of frustration on cognitive control in school-age childrenEAK-PEkt: exercise-aware knowledge tracing for student performance predictionSP-EG-MMPredicting student performance in an educational game using a hidden Markov modelLMS-CAPMassive lms log data analysis for the early prediction of course-agnostic student performanceFDGFrustration drives me to growBFEBetween frustration and education: transitioning students’ stress and coping through the lens of semiotic cultural psychologyAD-CSAutomatic discovery of cognitive skills to improve the prediction of student learningSP-ALAPredicting student performance using advanced learning analyticsTVL-CATime-varying learning and content analytics via sparse factor analysisEAG-CSCEmotions, age, and gender based cognitive skills calculationsML-CSCMachine learning based cognitive skills calculations for different emotional conditionsSP-DM-LATPredicting student performance using data mining and learning analytics techniques: a systematic literature reviewS-GCShould I grade or should I comment: links among feedback, emotions, and performanceMR-PCQModeling the relationship between students’ prior knowledge, causal reasoning processes, and quality of causal mapsMPA-MA multilayer prediction approach for the student cognitive skills measurementMCA-EA meta-cognitive architecture for planning in uncertain environmentsT-PRThe influence of teacher and peer relationships on studentsNS-SENational Society for the Study of EducationARFEAutomatically recognizing facial expression: predicting engagement and frustrationCSMAA biologically inspired cognitive skills measurement approachNT-PPCSA novel technique for the evaluation of posterior probabilities of student cognitive skillsMSG-ICMedical student gender and issues of confidenceGD-ATCGender differences in student attitudes toward computersGD-AT-SCIGender differences in student attitudes toward science: a meta-analysis of the literature from 1970 to 1991LS-ESP-RA longitudinal study of engineering student performance and retention III. Gender differences in student performance and attitudesGD-SEGender differences in student ethics: Are females really more ethical? Gender differences in teacher-student interactions in science classroomsGD-AT-ITGender differences in attitudes towards information technology among Malaysian student teachers: a case study at University Putra MalaysiaGD-RCGender differences in the response to competitionGD-LTSGender differences in the learning and teaching of surgery: a literature reviewSG-TM-CAPStudent gender and teaching methods as sources of variability in children’s computational arithmetic performanceGDSLGender difference and student learningGD-MS-SLGender difference in student motivation and self-regulation in science learning: a multigroup structural equation modeling analysisGD-MRGender differences in the influence of faculty-student mentoring relationships on satisfaction with college among African-AmericansDSS-CPDifferences of students’ satisfaction with college professors: the impact of student gender on satisfactionGD-TETGender differences in teachers’ perceptions of students’ temperament, educational competence, and teachabilityGD-HSSGender differences in factors affecting academic performance of high school studentsTP-MAInfluence of elementary student gender on teachers’ perceptions of mathematics achievementGD-NCSGender differences in alcohol-related non-consensual sex, cross-sectional analysis of a student populationGD-SP-ECGender differences in students’ and parents’ evaluative criteria when selecting a collegeSSGSocial influences, school motivation, and gender differences: an application of the expectancy-value theoryGD-DSSGender differences in the dimensionality of social supportETP-SSAEarly teacher perceptions and later student academic achievementGES-EGender, ethnicity, and social cognitive factors predicting the academic achievement of students in engineeringPSDPredicting students drop out: a case studyIQ-PAPSelf-discipline outdoes IQ in predicting academic performance of adolescentsTSI-SSCObservations of effective teacher-student interactions in secondary school classrooms: predicting student achievement with the classrooms assessment scoring system-secondaryBFP-MARole of the big five personality traits in predicting college students’ academic motivation and achievementESF-SSUsing emotional and social factors to predict student successFPP-AUSWho succeeds at university? Factors predicting academic performance in first-year Australian university studentsACAPredicting academic achievement with cognitive abilityAAGTAdvancing achievement goal theory: using goal structures and goal orientations to predict students’ motivation, cognition, and achievementSLC-AShort-term and long-term consequences of achievement goals: predicting interest and performance over timeRHASRole of hope in academic and sports achievementPSO-LPSPrediction of school outcomes based on early language production and socioeconomic factors


## 7. Conclusions

The proposed review highlights the potential research opportunities to optimize the students’ performance prediction systems while exploring earlier contributions of different research fields, i.e., cognitive computing, data mining, data analysis, and psychology. The previous studies are still limited in synchronization between the existing contributions of various fields, which negatively impacted the mathematical modeling of emotional attributes. It increased the systems’ dependencies on real-world datasets. Thus, to investigate the potential challenges thoroughly, the study is split into three sections.1.
The data mining discoveries, psychological findings, and data analysis results are examined.2.
The study performs a domain-wise investigation of the existing methods focusing on students’ performance prediction, i.e., the domain includes new students’ performance prediction techniques, modifications in existing techniques, and comparisons analysis.3.
Eventually, future direction and potential pilot project viability are highlighted.Table 6
Summary of potential research challenges and recommendation.
S.NoResearch questionRemarksRecommendations1What are the applications of student performance prediction systems?Prediction of at-risk students for special treatment and counseling sessions.Mathematically model emotional attributes, family issues, study schedules, and institutional attributes all together to develop a significant prediction system.If students cannot achieve an excellent academic score, then the performance prediction system assists students in observing the main reason behind the low performance.If the prediction system considers a large number of influential factors, then the academic achievement of excellent can also be advanced.Advance students’ academic achievements.Modulates the relationship between behavior and students’ performanceMonitor students’ behavior such as interaction and attitude towards teacher, seriousness, and unseriousness in the classroom2What are the factors that can optimize student performance prediction?They include but are not limited to family-related factors, emotional factors, gender description, and institution-related factors.Initiate pilot projects with an assumption-based dataset. The assumptions should be based on earlier studies of psychology, data analysis, and data mining.Emotional factors, such as frustration, anxiety, stress, and depression.Analyze the performance of at-risk students while mathematically modeling the association among students’ emotional, family, and institution-related attributes.Quantize family factors, i.e., parents’ positive and negative roles, including overexpectation of parents and positive involvement of parents in children’s daily cognitive activities.Perform factorization of gender because earlier studies depict that gender difference magnitude is sometimes dependent on other factors such as cultures, socioeconomic condition, language, age, etc.Literature studies are evidenced with many contributions to gender differences. They show that different gender individuals perform differently during cognitive activities, solving assignments, attempting quizzes, and examinations studies.Explore instructor teaching methodology, interaction with a student advisor, extra curriculum activities in the institution, student complaint platform, the distance between the institution and students’ residence, transport facility, and the behavior of the friends.Different institutional factors directly or indirectly influence students’ performance.3What is the intensity of research findings in the field of student performance prediction systems optimization?Intensity of psychological findingsThese findings are not synchronized and linked toward a significant student performance prediction model.Intensity of data analysis findingsSo, the main challenge is to provide an effective platform where future researchers can collaborate and synchronize the prior findings. Also, pilot projects based on the assumption-based dataset are highly recommended. Successful pilot project implementation will pave the way for quick optimization of existing systems.Intensity of students’ performance prediction systems4Are the findings of psychological studies, data mining, and contribution in algorithms synchronized with each other for the viability of the pilot project?The intensity of publications contributing to student performance prediction is quite good, but these contributions are not synchronized with each other.Mathematically model emotional, family, and institution-related attributes.5How do synchronization and coordination of prior psychological, data mining, and algorithmic findings contribute to the effective educational system via student performance prediction algorithm?Every part of the student performance prediction area of research is interlinked. The psychological result verifies the emotional change during the evaluations of the frustration, severity, anxiety, and stress. The data analysis findings associate the student attributes. The student performance prediction algorithm mathematical model the statistical association among the student influencing factors and their performance outcome.If these findings are linked with the objective of qualitative data repositories and algorithms, then, we can move toward an efficient student performance prediction system.



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