# Educational Data Mining in Prediction of Students' Learning Performance: A Scoping Review

Chunping Li, Mingxi Li, Chuan-Liang Huang, Yi-Tong Tseng, Soo-Hyung Kim, Soonja Yeom

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Chunping Li, Mingxi Li, Chuan-Liang Huang, Yi-Tong Tseng, Soo-Hyung Kim, et al., Educational Data Mining in Prediction of Students' Learning Performance: A Scoping Review. IFIP World Conference on Computers in Education (WCCE), Aug 2022, Hiroshima, Japan. pp.361-372, 10.1007/978-3-031-43393-1_33. hal-05533342

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# Educational Data Mining in Prediction of Students' Learning Performance: A Scoping Review

Chunping Li¹(✉), Mingxi Li¹, Chuan-Liang Huang², Yi-Tong Tseng¹, Soo-Hyung Kim³, and Soonja Yeom¹

¹ University of Tasmania, Hobart, Australia
{Chunping.Li,Mingxil,Tsengyt,Soonja.Yeom}@utas.edu.au

² National Chung Hsing University, Taichung City, Taiwan
Lin210346@gmail.com

³ Chonnam National University, Gwangju, South Korea
Shkim@chonman.ac.kr

**Abstract.** Students' academic achievement is always a target of concern for educational institutions. Nowadays, the rapid development of digital transformation has resulted in huge amounts of data being generated by Learning Management Systems. The deployment of Educational Data Mining (EDM) is becoming increasingly significant in discovering ways to improve student learning outcomes. Those approaches effectively facilitate dealing with students' massive amounts of data. The purpose of this review is to evaluate and discuss the state-of-art EDM for predicting students' learning performance among higher education institutions. A scoping review was conducted on twelve peer-reviewed publications that were indexed in ACM, IEEE Xplore, Science Direct and Scopus between 2012 and 2021. This study comprehensively reviewed the final inclusion literature on EDM in terms of tools, techniques, machine learning algorithms and application schemes. We have found that WEKA (tool) and classification (technique) were chosen in most of the selected studies carried out in their EDM settings. This review suggested that Tree Structured algorithms as supervised learning approaches can better predict students' learning performance, as it has been validated in several comparative analyses of other algorithms. In the present study, we demonstrate a future trend toward improving the generalizability of prediction models that can deal with a diverse population and the predictive results can be easily interpreted and explained by educators in the general market.

**Keywords:** Student Learning Performance · Data Mining · EDM · Machine Learning · Algorithms
# 1 Introduction

In the era of Big Data, scientists and engineers have developed data mining techniques and algorithms to transform the data into understandable information [1]. Data mining improves efficiency, predicts future trends, and makes more informed operational decisions. This method can be used in many fields, especially universities because students' success is crucial for higher education. Moreover, higher educational institutions collect large amounts of data from their students, staff, and organisational system. Then, the data is transferred to information by the application of Educational Data Mining (EDM), which higher institutions can use to improve instructional and intervention strategies. EDM can be applied to various fields; it can be used to predict academic performance, assist instructors, recognise struggling students, improve online learning systems, etc.

This paper focuses on predicting students' academic performance through different methods of algorithms and techniques because it can figure out the possible challenges of students and guide students' career possibilities. Student academic performance is always a target of concern among educational institutions. EDM has been proven to be one of the useful methods to analyse educational data and then evaluate the performance of students [2, 3]. Fortunately, most scholars from different fields have been studying and developing techniques to improve students' learning performance, and this review provides one part of a solution to improve student learning outcomes by discussing the authors' points of view in detail and eventually identifying some effective analytical techniques.

EDM is becoming increasingly significant in discovering ways to improve student learning performance. Many scholars have undertaken their research and analysis on identifying the effective methods of students using such methods [4]. However, the employment, as well as the application, of educational data mining methods are still not getting enough attention. Most of higher educational institutions commonly increase their interest in further improving their academic quality [5]. Educational data, such as the student learning activities, objectives, preferences, participation, learning marks, and achievement, is found to impact students' performance and outcome [6, 7].

Therefore, the purpose of this review is to provide insights into the implementation of EDM in higher education by analysing currently published research outcomes to analyse and discover how EDM can be used to predict students' performance with higher predictive accuracy. Higher educational institutions can deploy this recommended EDM method to predict students' learning in the future.

# 2 Background

Academic achievement is a huge concern for institutions of higher learning around the world. Massive amounts of learning and teaching data are generated through the use of learning management systems [4]. Institutions can deploy data mining methods to improve students' learning performance by making better decisions, planning better directions for students, predicting future trends and individual behaviour, and maximising resources.
The benefit of applying EDM in the higher education domain has been well doc-
umented in recent research [2, 8–10]. Data mining methods were used to assist ac-
demic performance in two primary categories: prediction and detection of undesirable
learning outcomes. Most of the studies in this review focused on predicting students’
grades, achievement or performance based on historical data (personal, educational,
behavioural, and extra-curricular features); while Dabhade et al. [11] indicate that the
involvement of behavioural data would increase the accuracy for the prediction of results.
This statement could be the cause of the behavioural features consisting of traits related
to students’ learning process [12]. Some studies developed models based on data mining
techniques on learning behaviour analytics to discover students’ undesirable learning
patterns or poor learning habits. The importance of the pattern of studying activity and
learning habits is highlighted in EDM, which provides instructors with useful informa-
tion related to students’ acceptance of the current curriculum and learning behaviour.
With the assistance of EDM, adaptive learning materials can promptly be developed for
students, helping to correct study habits, improving academic performance, and facili-
tating students’ learning [6]. The possible reason for the lack of research in this aspect
is the differentiation in chosen parameters for data mining algorithms. In addition, there
is a steep cost involved in implementing data mining tools and techniques because they
deal with vast amounts of data [13].

This scoping paper is an attempt to gain insight into more learning challenges that prior studies have not been able to address as well as the effective methods to improve students’ learning from the ICT perspective. Data mining techniques and algorithms are used primarily for two schemes: prediction analytics (dropout, grade, and achievement) and learning behaviour analytics (learning pattern and behaviour). In most selected studies, predictive functions were utilised to suggest ways to enhance students’ performance and learn better in academic achievement.

**3 Methodology**

This review aims to explore the implementation and performance of educational data
mining methods in predicting students’ learning performance. Hence, this scoping review
was conducted in order to provide answers to the following two research questions:

Research Question 1: *What are the typical tools involved in EDM settings for predicting student performance?*

Research Question 2: *In student performance prediction studies, which data mining approaches and algorithms are mostly employed with high accuracy?*

**3.1 Search Protocol**

The scoping review was conducted to answer research questions based on PRISMA-ScR guidelines [14]. Based on the research question, search terms were selected to reflect the broad scope of the review. The same search strategy was used in both databases: ((educational data mining) OR (data mining)) AND ((student performance) OR (student outcome)) AND ((higher education) OR (university)). At the initial search stage, filters were used to limit articles written in English and published between 2012 to 2021.
Four international databases of authoritative academic resources were used to search for relevant studies, which are the Association for Computing Machinery (ACM) Digital Library, ScienceDirect, Institute of Electrical and Electronics Engineers (IEEE) Xplore and Scopus. The article selection process was detailed in Fig. 1. Two hundred and sixty-seven (267) articles were acquired after removing duplicates. Followed by screening of title and abstracts and full-text articles based on selection criteria, a total of twelve (12) articles were included in this scoping review.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Flow of article selection process following PRISMA-ScR guidelines.</figcaption></figure>

## 3.2 Selection Criteria

**Inclusion Criteria.** After the pre-screening process, the consideration of the advanced filters of paper selection was applied based on the employ of EDM methods, which are the clarity of the finding and the accuracy of the result. The inclusion of criteria to determine the relevant literature are listed below:

* Studies that were published from 2012 to 2021.

* Studies that were written in English.

* Studies that were peer-reviewed publications in journal or conference.

* Studies related to the usage of data mining techniques in students' learning performance prediction.

* Studies that clearly described the setting of EDM.

* Algorithms were applied in predicting learning performance along with empirical research evidence.
* Comparable prediction evaluation indicators were provided based on more than one applied algorithm.

**Exclusion Criteria.** Furthermore, the following criteria were used to exclude literature that is considered unsuitable for this review:

* Studies were published before 2012.

* Studies were written in language other than English.

* Studies were not providing a clear description of the use of methods/algorithms/techniques of education data mining.

* Studies did not provide relevant evidence to contribute to the research questions.

# 4 Results

This scoping review aims to explore the techniques and algorithms to obtain an accurate performance in the prediction of students' academic learning outcomes. Twelve eligible articles were considered directly related to the research questions and were comprehensively reviewed in terms of tools, prediction approaches, algorithms and evaluation methods used in the EDM setting. A summary of the included studies is presented in Table 1.

## 4.1 Data Mining Framework and Tools

There are three articles that followed the framework Cross Industry Standard Process for Data Mining (CRISP-DM) [3, 18, 20]. CRISP-DM is the data mining model that deals with DM from different perspectives with the aim of increasing knowledge of the data [24].

The tools mentioned in selected studies are listed in Fig. 2, two out of twelve articles have not mentioned the tools. The most common tool used to prepare data and implement the predictive model was Waikato Environment for Knowledge Analysis (WEKA) for machine learning approaches [3, 4, 15, 18–23]. Other machine learning tools included Jupyter Notebook [11] and Orange Data Mining [20]. Data storage and pre-processing tools were MATLAB, Minitab, Microsoft SQL Server and Excel.

## 4.2 EDM Approaches and Algorithms

From Table 1, it is observed that supervised machine learning approaches were employed in all twelve selected studies, then followed by deep learning approaches (5 studies). There are 20 different algorithms in use in all reviewed literature, which are mainly divided into classification, regression and deep learning techniques (Fig. 3).

*Classification techniques* are the most frequently used, specifically, tree structured algorithms which contain normal Decision Trees [16, 17, 22], Random Forest [3, 16, 17, 22, 23], J48 [3, 15, 19–21, 23], ID3 [4, 19, 20] and CART [18, 19]. Other applied classification techniques are Naïve Bayes (NB) [3, 4, 15, 18, 21, 23] and Bayesian
<table><caption>**Table 1.** Summary table of twelve included studies.</caption><thead><tr><td>Reference</td><td>Dataset Size</td><td>Tools</td><td>Approaches</td><td>Algorithms</td><td>Evaluation</td></tr></thead><tbody><tr><td>[15]</td><td>225</td><td>WEKA</td><td>Classification, Deep Learning</td><td>NB, Bayesian Network*, ID3, J48, NN</td><td>Precision, Recall, F-Measure, Accuracy</td></tr><tr><td>[4]</td><td>N/A</td><td>WEKA</td><td>Classification, Deep Learning</td><td>NB, ID3*, NN, SVM</td><td>Precision, Recall, F-Measure, Accuracy</td></tr><tr><td>[3]</td><td>441</td><td>WEKA, SQL</td><td>Classification</td><td>Random Tree*, J48, NB, OneR, Stacking</td><td>Precision, Kappa index, Absolute Error</td></tr><tr><td>[16]</td><td>480</td><td>N/A</td><td>Classification, Regression</td><td>Logistic Regression*, SVR, Decision Tree, Log-LR, Random Forest, PLS-R</td><td>Mean Absolute Error, Root Mean Squared Error, R-Squared</td></tr><tr><td>[17]</td><td>500</td><td>N/A</td><td>Classification, Regression</td><td>LR, Decision Tree, SVM, Random Forest*</td><td>R-Squared, Mean Square Error, Root Mean Square Error</td></tr><tr><td>[11]</td><td>85</td><td>MS-Excel, Jupyter</td><td>Classification, Regression</td><td>MLR, SVM*</td><td>Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, R-Squared</td></tr><tr><td>[18]</td><td>385</td><td>WEKA</td><td>Classification</td><td>NB, CART, IBK*</td><td>False positive rate, True positive rate, Accuracy</td></tr><tr><td>[19]</td><td>234</td><td>WEKA</td><td>Classification</td><td>ID3, J48*, CART*</td><td>Accuracy, Specificity, Precision, Recall, F-measure</td></tr></tbody></table>

(continued)
<table><caption>**Table 1.** (continued)</caption><thead><tr><td>Reference</td><td>Dataset Size</td><td>Tools</td><td>Approaches</td><td>Algorithms</td><td>Evaluation</td></tr></thead><tbody><tr><td>[20]</td><td>1374</td><td>WEKA, Orange, Minitab, MATLAB</td><td>Classification, Deep Learning</td><td>J48*, ANN</td><td>Accuracy, Root Mean Squared Error</td></tr><tr><td>[21]</td><td>120</td><td>WEKA</td><td>Classification, Deep Learning</td><td>NB*, J48, ML, Nearest Neighbours, SVM</td><td>Accuracy, F-measure</td></tr><tr><td>[22]</td><td>300</td><td>WEKA</td><td>Classification</td><td>Decision Tree*, Random Forest</td><td>Accuracy, Precision, Recall, F-measure</td></tr><tr><td>[23]</td><td>395</td><td>WEKA</td><td>Classification, Deep Learning</td><td>NB, MLP, J48*, Random Forest</td><td>Rescission Recall F-measure</td></tr></tbody></table>

*Denotes algorithms proven to have the best performance based on their evaluation criteria.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Tools used in selected EDM studies and the number of studies.</figcaption></figure>

Network [15], Support Vector Machine (SVM) [4, 11, 16, 17, 21], OneR classifier [3], Stacking [3], IBK [18] and Nearest Neighbours [21].

Regression techniques include Logistic Regression [16], Linear Regression (LR) [17], Log-Linear Regression (Log-LR) [16], Partial Least Squares Regression (PLS-R) [16], Multiple Linear Regression (MLR) [11].

Deep Learning contains Neural Network (NN) [4, 15], Artificial Neural Network (ANN) [20] and Multi-Layer Perceptron (MLP) [21, 23].

## 4.3 EDM Performance and Evaluation

Making a machine learning model effective depends largely on evaluating it. Different metrics were adopted in the use of model performance evaluation, which refers to the ability of the model to correctly predict student performance with the applied dataset. Table 1 illustrated the applied measures for each selected study. The algorithms' performance can be evaluated by the predictive classification accuracy (such as true positive rate, false positive rate, precision, recall, F-measure, accuracy), the error measurement (such as mean square error, relative absolute error and root relative squared error) and
<figure><img src="image_6.png" /><figcaption>Fig. 3. Frequency of applied machine learning algorithms in EDM.</figcaption></figure>

regression quality judgement (such as R-squared). Overall, performance indicators, accuracy [4, 15, 18–22], precision [3, 23] and R-squared [11, 16, 17] scores are the common measures in the comparative analysis to assess various algorithms’ performance.

The paper has reviewed clearly explain how EDM is used in predicting student performance. To identify the best algorithms in data mining based on educational data, the highest value of the evaluation method was exacted for analysis. Figure 4 summarised algorithms identified with more accurate performance and better quality of the predictive model in the reviewed studies. Seven of the 12 articles identified that tree structured algorithms outperformed other algorithms. Among the tree structured method, ID3 achieved 75.47% accuracy, which is greater than 52.83% in NB, 57.47% in NN and 66.04 in SVM [4]; Random Forest had better precision (96%) [3] and R-squared (88.59%) [17]; the best accuracy was attained by J48 with 98.30% compared with ID3 [19] and 98.10% compared with ANN [20], and the most precision of 92.40% in comparison of NB, MLP and random forest (below 90%) [23]. It is worth noting that Bayesian Network has only appeared in one article, and it reached an accuracy rate of 92%, superior to other algorithms, including J48, ID3, NB and NN [15].

<figure><img src="image_7.png" /><figcaption>Fig. 4. The frequency of accurate algorithms identified.</figcaption></figure>
# 5 Discussion

This paper aims to analyse and organise the methods of general techniques and algorithms usage in educational data mining and focuses on a higher accuracy rate in predicting students' learning performance. EDM methods can predict students' learning performance and refine academic decision-making for educational institutions.

**Research Question 1:** *What are the typical tools involved in EDM settings for predicting student performance?*

In the surveyed articles, the WEKA is found to be the most common tool used for machine learning approaches. WEKA is a Java software tool of the machine learning algorithm for data mining collection to analyse different datasets. It is an open source tool source tool includes a set of visualisation tools, which makes a smooth graphical user interface (GUI), together with a collection of machine learning algorithms [25]. Its easy accessibility makes it widely used with data miners working in the educational field [26].

**Research Question 2:** *In student performance prediction studies, which data mining approaches and algorithms are mostly employed with high accuracy?*

The purpose of using EDM algorithms is to acquire a higher accuracy rate in the dataset calculation. The prediction of students' academic performance is the main target to explore using the EDM algorithm in this review paper. It is apparent from this scoping review that classification approaches are employed extensively in EDM, in particular in Tree Structured algorithms with relative higher accuracy of predictive model.

A majority of studies have attempted to predict student performance using supervised machine learning. Nevertheless, research on other data mining techniques (such as unsupervised, semi-supervised and hybrid machine learning) are still understudied in identifying the best algorithms among them. It is possible to reap endless advantages from supervised and deep learning techniques. The generalisability and ease of interpretation of the results may be the possible reason for researchers' adoption [27]. The performance accuracy of supervised learning algorithms can benefit from the size of the training dataset and labelled data [28]. In general, supervised machine learning tend to be more accurate than unsupervised learning with a large training dataset. While the use of other techniques to understand student behaviour is generally lacking, such as clustering. Considering the overfitting risks in supervised learning approaches, other data mining approaches can be explored. Other researchers can take advantage of this finding in the future to fill a valuable research gap.

It is observed that tree structured (Decision Tree and Random Forest), Naïve Bayes and SVM are dominant in the selected studies for predicting student academic performance (Fig. 3). The possible reason may be due to the interpretability of these algorithms. The tree structured algorithms outperformed other applied algorithms in 7 out of 12 articles. Decision Tree is popular because it is one of the five top algorithms used, and the most popular supervised learning algorithm. It has often been discussed and compared with others algorithms [1, 3, 4, 15–17, 19, 21–23]. The articles mentioned the advantages of the Decision Trees, which are easy to understand, interpret, and handle noisy data and do not require normalisation of data [22]. In addition, J48 has the potential for data result illustration in visual classification [29]. Among the selected studies, J48 Decision Tree has been found to achieve the highest accuracy rate (98.10%) [20]. It
is worth noting that the predictive model was fitted with 1374 student records, which is the largest dataset compared to other selected studies with an 85–500 sample size (see Table 1). Predictive models need a lot of data to be trained accurately by machine learning. The model accuracy largely depends on the dataset range, the larger dataset has more precise results, therefore classifying more clear patterns for student learning behaviour and outcome [27, 28]. The limited sample size (below 1000 instances) could be one of the limitations identified in this review; it requires explored with sufficient students’ data to improve the training model and draw accurate conclusions in future research.

The results in this scoping review indicate that the use of data mining techniques could contribute a great deal of insight into students’ learning outcomes and reveal valuable patterns. Factors of academic performance analysis can be examined and analysed through EDM to decrease students’ academic performance failure or dropout [10]. Machine learning algorithms employed in Dabhade et al. [11], the evidence indicated that the most important factor when predicting future performance is recent past performance. EDM was also utilised to analyse learning behaviours to assist students in correcting study habits to enhance learning performance [6] and to identify at-risk students of academic failure in online learning environment [30, 31]. EDM supports decision-making in learning by providing insights into strategies that will help students to enhance overall learning outcomes [32]. The implementation of EDM has implications in improving students’ performance during the learning process, we emphasise the need to develop predictive models that have higher generalizability that can work with a diverse population, greater explainability which can be interpreted by stakeholders easily and an early prediction purpose instead of prediction made after the student has completed current courses.

# 6 Conclusion

This scoping review has analysed the current literature on Educational Data Mining (EDM) in student performance prediction to provide insight into how EDM was implemented in the research field. EDM provides insight into students’ performance and academic failure risk based on the evaluation of students’ personal data, historical and current academic performance and learning behavioural features. The study reveals that WEKA is among the most applied tools in implementing different EDM approaches. The findings imply that supervised learning, particularly classification techniques (Tree Structured algorithms), is commonly adopted in developing the predictive model with higher accuracy compared to other algorithms.

The application of EDM in a learning context allows one to uncover hidden patterns of student learning behaviour and knowledge relates to learning process in large amounts of data; thus, the prediction-making of outcomes or behaviours is crucial [4]. The findings of this paper can be used as guidance for EDM implementation aiming to develop predictive model with higher accuracy. These reviewed tools, approaches and algorithms can serve as a map to foster research in facilitating student academic performance.

This scoping review is limited by the scope of reviewing comparative analysis of machine learning algorithms in the EDM field. It is possible that valuable publications
have been omitted because employed due to the lack of evidence for the comparative
analysis form different algorithms. Additionally, as this is a scoping review, more theoret-
ical contributions should be explored than methodological implications in order to reach
a more in-depth conclusion. Therefore, these limitations can be addressed by expand-
ing this review by examining the sample dataset used to develop prediction models and
evaluating the factors and attributes that impact student performance in the EDM.

**Acknowledgement.** This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP). Grant funded by the Korean government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub).

References

1. Chalaris, M., Gritzalis, S., Maragoudakis, M., Sgouropoulou, C., Tsolakidis, A.: Improving quality of educational processes providing new knowledge using data mining techniques. In: 3rd International Conference on Integrated Information (IC-ININFO), vol. 147, pp. 390–397 (2014)

2. Khanna, L., Singh, S.N., Alam, M.: Educational data mining and its role in determining factors affecting students academic performance: a systematic review. In: Proceedings of the 2016 1st India International Conference on Information Processing (IICIP), Delhi, India. IEEE (2016)

3. Moscoso-Zea, O., Saa, P., Luján-Mora, S.: Evaluation of algorithms to predict graduation rate in higher education institutions by applying educational data mining. Australas. J. Eng. Educ. 24(1), 4–13 (2019)

4. Francis, B.K., Babu, S.S.: Predicting academic performance of students using a hybrid data mining approach. J. Med. Syst. 43(6), 162 (2019)

5. Alzafari, K., Kratzer, J.: Challenges of implementing quality in European higher education: an expert perspective. Qual. High. Educ. 25(3), 261–288 (2019)

6. Tsai, Y.R., Ouyang, C.S., Chang, Y.K.: Identifying engineering students’ English sentence reading comprehension errors: applying a data mining technique. J. Educ. Comput. Res. 54(1), 62–84 (2016)

7. Li, C., Herbert, N., Yeom, S., Montgomery, J.: Retention factors in STEM education identified using learning analytics: a systematic review. Educ. Sci. 12(11), 781 (2022)

8. Gupta, S.B., Yadav, R.K., Shivani: Analysis of popular techniques used in educational data mining. Int. J. Next-Gener. Comput. 11(2), 137–162 (2020)

9. Jin, Y., Yang, X., Yu, C., Yang, L.: Educational data mining: discovering principal factors for better academic performance. In: Proceedings of the 2021 the 3rd International Conference on Big Data Engineering and Technology (BDET), Singapore, Singapore (2021)

10. Pradeep, A., Das, S., Kizhekkethottam, J.J.: Students dropout factor prediction using EDM techniques. In: Proceedings of the 2015 International Conference on Soft-Computing and Networks Security (ICSNS), Coimbatore, India. IEEE (2015)

11. Dabhade, P., Agarwal, R., Alameen, K.P., Fathima, A.T., Sridharan, R., Gopakumar, G.: Educational data mining for predicting students’ academic performance using machine learning algorithms. Mater. Today-Proc. 47, 5260–5267 (2021)

12. Amrieh, E.A., Hamtini, T., Aljarah, I.: Mining educational data to predict student's academic performance using ensemble methods. Int. J. Database Theory Appl. 9(8), 119–136 (2016)

13. Zoric, A.B.: Benefits of educational data mining. In: Proceedings of the 44th International Scientific Conference on Economic and Social Development, Split, Croatia (2019)
14. Tricco, A.C., et al.: PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. *Ann. Int. Med.* **169**(7), 467–473 (2018)

15. Almarabeh, H.: Analysis of students' performance by using different data mining classifiers. *Int. J. Mod. Educ. Comput. Sci.* **9**(8), 9–15 (2017)

16. El Guabassi, I., Bousalem, Z., Marah, R., Qazdar, A.: Comparative analysis of supervised machine learning algorithms to build a predictive model for evaluating students' performance. *Int. J. Online Biomed. Eng.* **17**(2), 90–105 (2021)

17. El Guabassi, I., Bousalem, Z., Marah, R., Qazdar, A.: A recommender system for predicting students' admission to a graduate program using machine learning algorithms. *Int. J. Online Biomed. Eng.* (iJOE) **17**, 135–147 (2021)

18. Ayinde, A., Omidiora, E., Adetunji, A.: Comparative analysis of selected classifiers in mining students' educational data. *Commun. Appl. Electron. (CAE)* **1**(5), 5–8 (2015)

19. Saheed, Y., Oladele, T., Akanni, A., Ibrahim, W.: Student performance prediction based on data mining classification techniques. *Niger. J. Technol.* **37**(4), 1087–1091 (2018)

20. Blasi, A.H., Alsuwaiket, M.: Analysis of students' misconducts in higher education using decision tree and ANN algorithms. *Eng. Technol. Appl. Sci. Res.* **10**(6), 6510–6514 (2020)

21. Salal, Y., Abdullaev, S.: Optimization of classifiers ensemble construction: case study of educational data mining. *Comput. Technol. Autom. Control Radio Electron.* **19**(4), 139–143 (2019)

22. Kaunang, F.J., Rotikan, R.: Students' academic performance prediction using data mining. *In: Proceedings of the 2018 Third International Conference on Informatics and Computing (ICIC), Palembang, Indonesia. IEEE (2018)*

23. Kiu, C.-C.: Data mining analysis on student's academic performance through exploration of student's background and social activities. *In: Proceedings of the 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA), Subang Jaya, Malaysia. IEEE (2018)*

24. Chapman, P., et al.: CRISP-DM 1.0: step-by-step data mining guide. *SPSS inc.* **78**, 1–78 (2000)

25. Adekitan, A.I., Salau, O.: The impact of engineering students' performance in the first three years on their graduation result using educational data mining. *Heliyon* **5**(2), e01250 (2019)

26. Kabakchieva, D.: Predicting student performance by using data mining methods for classification. *Cybern. Inf. Technol.* **13**(1), 61–72 (2013)

27. Shafiq, D.A., Marjani, M., Habeeb, R.A.A., Asirvatham, D.: Student retention using educational data mining and predictive analytics: a systematic literature review. *IEEE Access* **10**, 72480–72503 (2022)

28. Chaovalit, P., Zhou, L.: Movie review mining: a comparison between supervised and unsupervised classification approaches. *In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA. IEEE (2005)*

29. Toivonen, T., Jormanainen, I.: Evolution of decision tree classifiers in open ended educational data mining. *In: Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality, León, Spain (2019)*

30. Rodrigues, M.W., Isotani, S., Zarate, L.E.: Educational data mining: a review of evaluation process in the e-learning. *Telematics Inform.* **35**(6), 1701–1717 (2018)

31. Baradwaj, B.K., Pal, S.: Mining educational data to analyze students' performance. *Int. J. Adv. Comput. Sci. Appl.* **2**(6), 63–66 (2012)

32. Parmar, K., Vaghela, D., Sharma, P.: Performance prediction of students using distributed data mining. *In: Proceedings of the 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India. IEEE (2015)*