# Leveraging Data Mining Technique to Enhancing Online Education and Its Efficiency Study

Authors: Xiaoli Zhao
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The previously conducted assessment of university education facilitated an evaluation model depending on small-scale data mining. A cloud-platform-based data mining algorithm was constructed for university education informatization to handle. Hence, we established an evaluation index system, integrated the global evaluation perspective, and subsequently obtained the three-dimensional structure of the evaluation index. The criterion was calculated by the postmining probability of the data, which was further leveraged to score each index in the evaluation system, and finally, the original index data matrix was constructed. Afterward, the membership degree vector of the index was calculated concerning which type of benefit of education informatization is evaluated. We investigated the online university education mechanism based on educational data mining. According to the computer-aided learning platform, we collected information and data related to the situation and resource browsing of students on the platform. We preprocessed these H log data. Then, the statistics of the students’ login information and resource browsing were collected. Hence, we analyzed the influencing factors through the decision tree that enabled instructors to effectively grasp the students’ learning conditions based on the mining results. The above integration of teaching content and the construction of a teaching model could substantially improve the quality of online learning. Experimental results showed that our data-mining-based evaluation model exhibited a much higher accuracy when the university evaluation was compared with the conventional model. This observation claimed that the university education evaluation by leveraging bulk data was quite useful. Besides, the cost consumption of the proposed method was superior to its competitors.


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## Body

## 1. Introduction

To implement the developmental strategy of “rejuvenating the country through science and education”, China's higher education institutions invested huge resources in the digitization of information in the past decade to support teaching, research, and management. University educational informatization plays an indispensable role in social informatization. Thus, improving both the quality of teaching and updating the scientific research level is quite significant [1]. While China's university education informatization has been developing, how to evaluate the benefits of college education informatization has also attracted wide public attention nowadays. The traditional university education informatization relies on an evaluation model requiring a small amount of data analysis [2]. Thus, we construct a novel education informatization evaluation model by leveraging a cloud model-based data mining in this paper. More specifically, an evaluation index system [2] is constructed, and the overall evaluation perspective is integrated to calculate the three-dimensional structure within the evaluation index. Thereafter, the criterion is determined by the postmining probability of the data. Hence, the criterion is utilized to score each index in the evaluation system. Finally, the original index data are calculated to decide the vector of the index membership degree. This vector guides the evaluation process of the benefit of education informatization. Notably, our constructed cloud is called an uncertain conversion model that handles the qualitative concepts and quantitative descriptions [3]. Thus, applying the cloud model to data mining can decompose massive-scale data. When the university informatization benefits are evaluated, a large amount of informatization benefit data are collected. Education informatization benefits can be divided into three categories: educational benefits, organizational benefits, and external benefits [4]. One real-world example can be given as follows. Each type of benefit contains a large amount of data, which need to be calculated. The existing evaluation model is limited by data processing and data calculation, and it is infeasible to extract effective information from the massive data that results in poor evaluation results. We apply comprehensively and objectively the cloud model data mining algorithm for the benefit evaluation model of the university informatization to tackle the problem [5].Thus, first we propose an evaluation index system for the summary of educational informatization benefits. Second we propose the corresponding evaluation indexes for each subject in the overall evaluation perspective, and subsequently refine them based on these evaluation indexes [6, 7]. Third, we built an evaluation index system for the benefit of education informatization. Fourth, we analyze the benefits of educational informatization in the higher education institutions in China. We comprehensively studied the global evaluation perspective of the balanced scorecard theory to construct the evaluation system [8, 9] and further construct the three-dimensional structure of the evaluation model for the benefit of college education informationization at the final stage. The three dimensions of the constructed evaluation model are called perspective, development, and role dimensions. In the perspective dimension, we evaluated the benefits from different perspectives related to education informatization. Thereby, the development dimension was utilized to describe the four stages of the benefits of education informatization. Thus, the ultimate goal is to achieve a reasonable and planned implementation of both infrastructure and resources. Construction and application update, information technology can eventually become an indispensable part of teaching by leveraging the data mining technique to build a new learning environment. Afterward, the role dimension indicated the degree of development of education informatization using multiple roles. Thus, we constructed the evaluation index system for the benefit of education informatization.In practice, transforming the raw data related to education (from various education systems) into valuable information is termed the data mining of education. Instructors and students can make use of such information to enhance the teaching quality. It can also be utilized for educational researchers and the development of educational software systems. We provide support for online university education that is gradually developed and improved in this work. For a conventional face-to-face classroom, instructors can evaluate the learning effect and status of related courses through on-site questioning and classroom tests. Conventional evaluation methods will be inapplicable in this context. However, online university educational methods and data mining as a new module of the education system interact with various educational elements to ensure the realization of improved teaching goals. Currently, education-aware data mining has provided instructors with high-quality and objective feedback by realizing the organization and innovation of teaching content. Thus, the proposed method can assist in the adjustment and optimization of teaching strategies. Based on the different learners' conditions, the teaching process and curriculum development can be continuously optimized. Thereby, an online teaching model can effectively meet actual teaching requirements. Educational data mining mainly includes data mining content in teaching, management, and scientific research concerning data mining techniques. The implementation of data mining concerning online teaching (ELearning) is focused on in this work. We analyzed different learning behaviors based on the log data of university students that are collected by the information-based learning guidance platform. Figure1 shows the pipeline of the proposed method.Figure 1
The pipeline of the proposed method.


## 2. Preliminary

Effective and efficient learning with more focus on learning to learn and how to learn smartly is the aim of online university education that improves learning behavior. To investigate the problems related to effective and efficient online university education utilizing data mining approaches, they help analyze five different issues, which are called 1. to extract learning rules, 2. to analyze learning attributes and habits, 3. to assess learning status, 4. to estimate the effects of the learning process, and 5. to personalize learning services. Then, data mining methods are employed to conduct studies based on available online data. Hence, both objective learning and cognitive rules are aimed at extracting. Previous research on factors affecting learning is the topic that interests researchers more. The association between various factors and academic performance has been investigated by a great number of scholars. Mitchell et al. [6], for instance, implemented an algorithm based on a decision tree to categorize students. Hence, the link between the information of personal characteristics and the teaching performance of students with different grades was obtained. The various analysis [7–10] have concluded that the factors affecting the final grades of the course are assessed as learning types and acting performances in the learning process, test scores in peace, etc. An overview of the common applications and methods was summarized in Figure 2.Figure 2
Typical applications and methods for online university education.To extract the association between the learning management system and final grades concerning the usage behaviors of learners, the association rule mining and a grouping approach based on a decision tree were conducted by Yang et al. [7]. The questions and answers as well as chat records of students were investigated by employing a text mining approach to examine the process of online learning by Jia et al. [11]. To investigate the association between the implemented strategies to search for help among students and the grades in the intelligent tutoring system, a discrete Markov model with a combination of K-means clustering and Logistics regression was employed by Hao et al. [12]. To characterize the associations among communication styles, social networks, and academic performance, the method called social network analysis was designed by Zhao et al. [13] when learning communities utilize a computer-based collaboration. A collaborative learning platform utilizing a computerized-based framework was designed by Wei et al. [14] to run experiments. Hence, the conversations exchanged among students to figure solutions out on real problems collaboratively were analyzed by employing stepwise regression and other approaches. They observed the impact of sex or interactions between different sex on the collective learning model and result. Wei et al. [15] employed two different regression methods called Tobit and Logistic to analyze attributes that have an impact on the completion of courses by students. Investigating patterns in the research of learning behavior has been one of the subjects that several researchers have more focused on currently. Wang et al. [16], for instance, utilized attributes such as time spent for online reading and solving questions to determine the behaviors of students. Besides, fuzzy association rule mining approaches were employed to model the association among the patterns of learning behavior. An algorithm called maximum expectation (EM) was conducted to run clustering and determine behavior patterns of students by Ccsds et al. [14] when cooperative learning is under consideration. Song et al. [17] conducted a clustering based on the data set that contains a large number of participants joining an online mathematics game. Consequently, students' teamwork law was proposed. A set of tools called content, task, and code managements utilized generally in the real projects concerning software development were employed by Jia et al. [11] to determine how the well-performing and poorly performing groups differ by employing clustering and mined sequential patterns when they worked for software development projects. Jian et al. [18] employed methods called association rule mining to discover misunderstood points regarding knowledge students since this will influence the misunderstandings of other specific knowledge. Jie et al. [19] uniformly encoded the learners' learning behaviors and utilized algorithms that detect sequential patterns to attain diagrams that provide pathways of newtworks of students. They analyzed the learning paths with higher frequencies to discover efficient patterns in the learning process. Meanwhile, they employed the well-known clustering algorithm to divide students into five categories according to the sequence characteristics of their behaviors and defined these five categories as low-input, shallow investment, performance investment, and gradual and random participation.


## 3. The Proposed Method

We established an evaluation index system to classify the benefits of education informatization. First, we listed the factors relating to students’ online education in Figure3. Second, we proposed the corresponding evaluation indexes for each subject and further refined them based on these evaluation indexes concerning the overall evaluation attributes [6, 7]. Finally, we established the evaluation index system for the benefit of education informatization. It is observable that the evaluation system of educational informatization benefited from the proposed framework. The construction of the evaluation system can also reflect the evaluator's understanding of the benefits of college education informatization. By utilizing the constructed evaluation system comprehensively that absorbed the global evaluation perspective of the balanced scorecard theory [8, 9], we subsequently built the three-dimensional structure of the evaluation model for the benefit of college education informationization. Then, the three-dimensional structure of the evaluation index of educational informationization benefits from the construction of university education.Figure 3
Approaches for improving the efficiency of online university learning for students.Moreover, the three dimensions of the evaluation model constructed are called perspective dimension, development dimension, and role dimension. In the perspective dimension, it mainly evaluates the benefits from different perspectives related to education informatization. The development dimension is leveraged to describe the four stages of the benefits of education informatization. Meanwhile, the ultimate goal is to achieve a reasonable implementation of infrastructure and resources. Construction, update of the application, and information technology have become indispensable parts of teaching. We leverage the data mining technique to build a new learning environment. The role of the dimension is to examine the degree of development of education informatization through different roles. To our best knowledge, an assessment index system was constructed for the benefit of education informatization has been fulfilled. After obtaining the evaluation system of education informatization, it is necessary to calculate the criteria to score the indicators in the evaluation system. The implementation of the algorithms based on cloud data mining model for calculations [10] is mainly related to a large amount of data in the evaluation indicators of the educational informatization benefits.In the literature, the mining algorithms of conventional evaluation models are all conducted by high-performance computers or large-scale computing platforms. The server's performance is limited and cannot complete all the calculations of massive-scale data.To compute the above-mentioned criterion, a data set with special requirements, though hard, is needed due to the environment of data mining that extracts key insights from bulky data. Thus, it calculates the underlying connections and provides the implementation and dissemination of information by utilizing a data set. We can present the calculation of the cloud model that is run for data mining. In the pattern classification method when mining firstly probability by knowing the probability of the class condition, the clustering outcomes of the sample data rely upon the entire sample of each type [12]. Supposing there exist a total of M categories in the sample set denoted by C=c1…ci…cM, and the first mining probability of each category is denoted by Pci, then we can obtain (1)pcix=pxcit⋅pcpx,where pci/x denotes the postprobability of category ci. After the above criteria are constructed, the criteria are leveraged to score each indicator in the evaluation system. It can provide a basis for the overall benefit evaluation. Overall the maximum postprobability is given by(2)pcix=maxjpcic.After the scoring of each indicator is completed based on the judging criteria, the original indicator data matrix is standardized and transformed to complete the entire evaluation process. Assuming that there arem samples to be evaluated and n evaluation indicators, the original data matrix is then standardized and transformed by(3)jt,w=nwj⋅vj+hci,where wj and vj are the average and the standard deviation of the sample concerning the jth indicator, respectively. After standardized transformation, the values of the mean and variance of each sample data become zero and one, respectively. According to the n variables in the evaluation model, statistical methods are constructed. Different roles have different requirements for informatized campuses. The quantitative indicators within the university informatization evaluation index system are leveraged to determine the membership degree vector of each index [14, 15]. Under the five-gradient standard, the comprehensive assessment vector of the assessment index is normalized concerning the calculated result of the comprehensive evaluation vector. Thereby, the comprehensive evaluation of the efficiency of college education informatization is conducted according to the above calculation process. To construct the evaluation model based on the informationization benefit of college education, an algorithm based on the cloud model for data mining is conducted.Since the data conversion is successful, the model-driven crowd behavior modeling is utilized to design the task flow for online education data mining [4]. More specifically, we build a model-driven crowd behavior model, including a data-driven module. The generic structure of data-driven modeling is elaborated as follows: the data-driven module includes video collection and the characteristic information of the crowd behavior. The model-driven module is composed of attribute, behavior, and path algorithm modules [5]. To effectively eliminate the invalid data, a well-known crawler software is used to obtain the text stream, and the data are subsequently preprocessed [6]. Then, we treat the converted text as a computer processing object. The data preprocessing steps are presented as follows: segmentation of word text, removal of stop words, statistics of word frequency, and text vectorization. After finishing the preprocess of the text, we extract user behavior data from the textual data, which is the extraction of the topic-related data.Subject mining algorithms are different in various application scenarios. In our implementation, subject mining schemes should be combined to obtain data sets with similar subject characteristics [7]. If the topic still cannot be generated automatically, the model driver will preserve the underlying relationship and adopt a concise topic description of the document corpus. To complete the tasks of different topic sets, clustering algorithms are used to process when topic mining is conducted [8]. The feature weight vector in the feature space is found whose expression is given by(4)P=PT1,W1;T2,W2;…;Tn,Wn,where p, Tn, Wn, and n represent the feature vector, the keyword attribute, the topic vector, and the clustering target, respectively. Assuming that Tn and Wn have the same keyword attributes, the similarity between Tn and Wn is calculated by(5)JTn,Wn=cosTn,Wn=nW⋅V,where J represents the similarity and V represents the number of incremental clusters. Herein, we apply (4) to find the similarity between Tn and Wn. Next, we substitute the results of Tn and Wn into (5), the final clustering result is denoted by(6)C=∪rkCj,where the number of clusters is represented by k; C represents the mined clusters obtained during the incremental clustering change; and r represents the feature vector calculated by machine learning. The final clustering result is defined as our objective. Then, the optimization model drives the key technology of data mining to calculate the optimal feature subset and realize the efficient data mining from the terminal online education. The objective of optimizing the data mining operation is to eliminate invalid data from the original feature space and improve the accuracy of the mined targets.


## 4. The Results and Analysis of the Experiment

To verify the validity of the designed evaluation model, it is necessary to design an experiment to compare the proposed method with the conventional evaluation model. We select the educational information benefit index data of five colleges and universities and further use the evaluation model designed in this manuscript and the conventional evaluation model to process and evaluate the data and finally compare the evaluation results of the two systems. The results are presented in Figure4.Figure 4
Comparison of different universities concerning online learning.In our experiment, XSimStudio is selected as the experimental platform, and some experimental parameters involved in the experiment are shown in Table1. Five colleges and universities are selected one by one using two evaluation systems. It is then utilized to evaluate the benefits of educational informatization. When the evaluation results are obtained, Table 1 presents the data processed by the two evaluation models that are calculated. Figure 4 depicts the amount of data processed by the evaluation model is designed. After computing some statistics, the average amount of data processed by the five universities becomes the full set of the data when the conventional evaluation model obtains the evaluation results. Besides, the average of the data processing of the 5 universities is 93% in total when the results of the assessment model are obtained. Hence, this observation indicates that the evaluation model designed by exhibits better performance than the conventional model when processing bulk data. Therefore, the target range of experimental data cannot be calculated. The logarithmic value of the square root means error shows the error rate of the experimental results. To predict experimental results, a model-driven linear regression is utilized. The number of experimental iterations is 10. While the proposed method based on model-driven mining algorithm is denoted by L, and the conventional terminal online education data mining algorithm is denoted by R. The comparison results of the mining speed of the two technologies are shown in Table 2. It can be seen from Table 2 that when the proposed technology uses a small data set, the mining speeds become similar. When the data set is large, the mining speed gradually increases, which is faster than the conventional one. It can also be concluded that when the conventional data mining algorithms leverage a small data set, the mining speeds become similar. However, they take a longer time. When the data set is large, the increase is not significant. Hence, it does not excel the proposed algorithm. Therefore, the proposed method performs better than the other available in the literature.Table 1
Experimental software/hardware parameter settings.
ParametersCriterion nodesModel nodePlatformsXSimStudioXSimStudioVirtual core712E. memory32 GB48 GBHardware1 TB512 GBProgramPythonPythonLinuxUbuntu 2.1Ubuntu 2.1Byte typeDynamicDynamicTable 2
Browsing conditions of courses module.
Resource moduleBrowsing timesBrowsing frequencyAverage browsing timesBrowsing member #Member participation #Average browsing frequencyAverage resource browsing timesScenarios3213323311821.217430.7468.763.5Video resources132132445343.313320.9873.233.1Flash inter.7898778981.447680.7883.432.8Questions4566576546.78870.6445.652.5Auxiliary resources9587668345.25650.8536.561.9Common software2565454114.53550.7931.231.6As an effective classification and regression algorithm, the Microsoft decision tree algorithm is suitable for predicting educational data. The educational data exhibit both discrete and continuous attributes, which are based on the trend with specific results and utilize the relationship between the input columns in the data set. Thus, the basic theory is to predict discrete attributes; continuous attributes are used by leveraging the linear regression methods to fulfill the determination of the split position of the decision tree. Noticeably, the principle of the decision tree by Microsoft is presented as follows: we complete the creation of a series of splits in the tree and then use “Nodes” to represent these splits. Therefore, the data mining model is generated. Once the algorithm finds the predictable column that is closely related to the input column, it adds a node to the model. The algorithm determines the splitting strategy according to the type of predicted object (continuous column or discrete column). The decision tree then adopts a two-layer structure, and the analysis efficiency is improved by constructing the data mining middleware (between the tree algorithm and the database). Thus, the decision tree algorithm is leveraged to comprehensively consider the three main factors namely, level, profession, gender of the students, completes the construction of the mining structure and the data mining model, and analyzes the factors that affect the student's login behavior (in days). We take the number of login days as the predicted value. Herein, the input value is level, profession, and gender. The ratio indicates the proportion of students logging in more than five days and less than five days.The constructed decision is shown in Figure5. The analysis result shows the impact on the number of login days. Thus, the factors listed from the strongest to the weakest are called level, profession, and gender. When compared with undergraduate students, the number of login days for pilots and noncommissioned officers is significantly lower. Meanwhile, the number of login days for noncommissioned students is lower than that of pilots. The number of login days for trainees is significantly influenced by different professions within the same level.Figure 5
The constructed decision tree based on different positions/majors/genders.Instructors can reasonably guide and adjust online learning behaviors for students of different levels and majors. The reason for the large differences in login behavior between different majors comes from the same level such as the undergraduate. On the other hand, different professional orientations and different management of the student teams of each major lead to large differences in the learning behavior of the students. Moreover, student managers can manage different student teams in a targeted manner based on the actual situation. Therefore, the learning effect of the students can be significantly improved.The popularization of network learning architecture and the wide coverage of network signals are obtained by online learning. For example, we notice the necessary conditions. “Access Database Practical Technology” is a highly practical course, and its effective learning requires computer assistance. This research has indicated that the situations of students own the learning equipment. This result is shown in Table2. That the vast majority of students rely on the hardware during online university education learning is found. In the process of online learning, students have a certain tendency towards different types of resources. While 38.7% of students like the PPT format of electronic texts, 22.6% of students like video presentations. On the other hand, 38.7% of students do not pay attention to the form of resources.Regarding the factors that affect the learning experience, 54.7% of students believe that the efficiency of the network operation is low, 22.9% of students believe that there is a lack of effective communication between teachers and students, and 19.4% of students believe that they lack appropriate learning resources. Table2 indicates the percentage of students’ opinions in a response to “the ways to improve the effectiveness of learning”. Regarding the solutions to problems that occurred in the process of learning, 35.5% of learners choose to check the teaching resources recommended by teachers, 32.3% of students are prone to think and resolve problems independently, another 16.1% of students will interact with classmates online, and the 16.1% of students will ask teachers consult. Most of the students realize that their factors are the key to the effectiveness of learning behaviors and can actively use various resources to resolve practical problems. In terms of the learning effect, 61.3% of students think that the effect of online learning is average, 29% of students think that the impact is called uncertain, and only 6.5% of students think that the impact is called very good. Regarding the preference between conventional classrooms and online teaching, 35.5% of students would choose conventional classrooms, 58% of students would prefer to combine conventional classrooms with online teaching, and only 6.5% would choose full online teaching. It can be observed that although online teaching has some advantages over conventional teaching, the methods such as resource sharing and flexible learning forms popularize applications that still need continuous exploration and practice.


## 5. Conclusions

This paper researches the mechanism of online university education based on educational data mining. By utilizing the learning platform based on computer-aided information, we collect information and data related to the student’s login into the platform, resource browsing, and preprocessing these log data. Thus, the login and resource browsing conducted by the students are statistically examined with a mining process to determine the factors that affect them. For this purpose, an algorithm called the decision tree was employed. We observe that the level, professional, and gender performances are found different. The experiment suggests that the findings are presented as follows: the online learning behavior is shown in the 6th and 7th weeks. The login rate is the highest, that is, the distribution of time is relatively balanced. The online learning of students is relatively small, and the specific internal influencing factors include student level (main factor), major, and gender. For the students at the same level, the attribute called major affects their online learning investment. The main factors of learning needs, the distribution of resources on the course page, and the characteristics of learning resources are the main reasons that affect the different learning levels of students with various resources. Thus, instructors can more effectively grasp the learning situation of the students according to results, which can integrate the targeted teaching content with the construction of teaching models to improve the quality and efficiency of online university education learning. Our extensive experimental results have shown that the efficiency of data mining is better than that of conventional algorithms.



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