Educational Data Mining Methods: A Survey

Aleem, Abdul; Gore, Manoj Madhava

doi:10.1109/csnt48778.2020.9115734

Cited by 25 publications

(17 citation statements)

References 41 publications

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“…Among the data mining techniques used in EDM, prediction methods, like classification and regression, and structure discovery methods, like clustering and factor analysis, are the most commonly used. Most recent research in EDM had been focused on the use of two or more methods [10]. Usual clustering and classification problems in EDM and LA can be extended to Social Network Analysis [11,12].…”

Section: Related Work 21 Learning Analytics and Educational Data Miningmentioning

confidence: 99%

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Exploring study profiles of Computer Science students with Social Network Analysis

Flores¹,

Íslind²,

Óskarsdóttir³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Information technology is widely adapted in all levels of education. The extensive information resources facilitate enhanced human capacity and the social environment to support learning. In particular, Social Network Analysis (SNA) has been broadly used in teaching and learning practices. In this paper, we perform community detection analysis to identify the learning behavior profiles of undergraduate computer science students in a Nordic university. The social network was created using 273 responses to an online survey. The students themselves provided their social connections at the university, and node attributes were created based on responses to questions regarding Educational Values, Goals Orientation, Self-efficacy, and the university teaching methods. We analyze the biggest communities to identify the factors that characterize the learning strategy and preferences of undergraduate computer science students.

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Section: Related Work 21 Learning Analytics and Educational Data Miningmentioning

confidence: 99%

“…Usual clustering and classification problems in EDM and LA can be extended to Social Network Analysis [11,12]. However, Social Network Analysis has been used less frequently to examine educational data [10].…”

Section: Related Work 21 Learning Analytics and Educational Data Miningmentioning

confidence: 99%

Exploring study profiles of Computer Science students with Social Network Analysis

Flores¹,

Íslind²,

Óskarsdóttir³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…It can be roughly divided into six categories: data extraction, prediction, association mining, structure discovery, model-based recognition, and hybrid methods. 5 Predicting students' performance is one of the most important issues in the field of EDM. By predicting students' performance, we can identify the risk of students' academic failure in the learning process as early as possible, so as to intervene and guide in advance.…”

Section: F I G U R Ementioning

confidence: 99%

“…Creating models: Design and create models for analysis. 5. Evaluation: The model is evaluated by experiments.…”

Section: Rq1-procedures Of Establishing Prediction Modelmentioning

confidence: 99%

A survey on educational data mining methods used for predicting students' performance

Xiao

2021

Engineering Reports

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Predicting students' performance is one of the most important issues in educational data mining (EDM), which has received more and more attention. By predicting students' performance, we can identify students' risk of academic failure and help instructors to take some actions such as guidance or interventions to help learners as early as possible, or carry out continual evaluation of learners as to optimize learning path or personalized learning resources recommendation. In this survey, we reviewed the 80 important studies on predicting students' performance using EDM methods in 2016-2021, synthesized the procedure of building prediction model of students' performance which contains four phases and 10 key steps, and compared and discussed the latest EDM methods used in all steps. We analyzed the challenges faced by previous studies in three aspects and put forward future suggestions on data collection, EDM methods used, and interpretation of prediction model. This survey provides a comprehensive understanding and practical guide for researchers in this field, and also provides direction for further research.

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“…EDM method is a combination of methods coming from statistics, machine learning, data mining, and other fields. It can be roughly divided into six categories: data extraction, prediction, association mining, structure discovery, modelbased recognition, and hybrid methods [3].…”

Section: Introductionmentioning

confidence: 99%

RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance

Xiao

2021

Scientific Programming

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Predicting students’ performance is one of the most concerned issues in education data mining (EDM), which has received more and more attentions. Feature selection is the key step to build prediction model of students’ performance, which can improve the accuracy of prediction and help to identify factors that have significant impact on students’ performance. In this paper, a hybrid feature selection method named rank and heuristic (RnkHEU) was proposed. This novel feature selection method generates the set of candidate features by scoring and ranking firstly and then uses heuristic method to generate the final results. The experimental results show that the four major evaluation criteria have similar performance in predicting students’ performance, and the heuristic search strategy can significantly improve the accuracy of prediction compared with forward search method. Because the proposed RnkHEU integrates ranking-based forward and heuristic search, it can further improve the accuracy of predicting students’ performance with commonly used classifiers about 10% and improve the precision of predicting students’ academic failure by up to 45%.

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Educational Data Mining Methods: A Survey

Cited by 25 publications

References 41 publications

Exploring study profiles of Computer Science students with Social Network Analysis

Exploring study profiles of Computer Science students with Social Network Analysis

A survey on educational data mining methods used for predicting students' performance

RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance

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