Educational Data Mining: A Review

Mohamad, Siti Khadijah; Tasir, Zaidatun

doi:10.1016/j.sbspro.2013.10.240

Cited by 138 publications

(58 citation statements)

References 10 publications

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“…The category of statistics and visualization has received a prominent place in theoretical discussions and research in EDM [8], [7], [12]. Another point of view, proposed by Baker [3], classifies the work in EDM as follows: 1) Prediction.…”

Section: Edm Methodsmentioning

confidence: 99%

“…Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings and using those methods to better understand students and the settings which they learn in [3]. Different from data mining methods, EDM, when used explicitly, accounts for (and avail of opportunities to exploit) the multilevel hierarchy and lacks independent educational data [3].…”

Section: Educational Data Miningmentioning

confidence: 99%

“…Different from data mining methods, EDM, when used explicitly, accounts for (and avail of opportunities to exploit) the multilevel hierarchy and lacks independent educational data [3].…”

Section: Educational Data Miningmentioning

confidence: 99%

“…Similar to other DM techniques extraction processes, EDM extracts interesting, interpretable, useful, and novel information from educational data. However, EDM is specifically concerned with developing methods to explore the unique types of data in educational settings [3]. Such methods are used to enhance knowledge about educational phenomena, students, and the settings in which they learn [4].…”

Section: A Data Used In Edmmentioning

confidence: 99%

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Data Mining in Education

Algarni¹

2016

ijacsa

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Abstract-Data mining techniques are used to extract useful knowledge from raw data. The extracted knowledge is valuable and significantly affects the decision maker. Educational data mining (EDM) is a method for extracting useful information that could potentially affect an organization. The increase of technology use in educational systems has led to the storage of large amounts of student data, which makes it important to use EDM to improve teaching and learning processes. EDM is useful in many different areas including identifying at-risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, and optimizing subject curriculum renewal. This paper surveys the relevant studies in the EDM field and includes the data and methodologies used in those studies.

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Section: Edm Methodsmentioning

confidence: 99%

Section: Educational Data Miningmentioning

confidence: 99%

“…Different from data mining methods, EDM, when used explicitly, accounts for (and avail of opportunities to exploit) the multilevel hierarchy and lacks independent educational data [3].…”

Section: Educational Data Miningmentioning

confidence: 99%

Section: A Data Used In Edmmentioning

confidence: 99%

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Data Mining in Education

Algarni¹

2016

ijacsa

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“…In a survey, Jindal and Borah (2013) identified three articles using ANNs for EDM in the period -2013[Dutta et al, 2011, Wang and Liao 2011, Yu et al 2010, on enrolment decision, student retention and language learning. In their review on EDM, Mohamad and Tasir (2013) reported just one paper using ANN for predicting the pattern of behaviour of students [Blagojevic and Micic 2013]. In a systematic literature review of empirical work in LA and EDM, Papamitsiou and Economides (2014) only managed to find one paper using ANN techniques -for prediction of student performance, retention and satisfaction -, only one of them published in the current decade [Guo 2010].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning applied to Learning Analytics and Educational Data Mining: A Systematic Literature Review

Coelho

Silveira

2017

Anais Do XXVIII Simpósio Brasileiro De Informática Na Educação (SBIE 2017)

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Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review

Alalawi

Athauda

Chiong

2023

Engineering Reports

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Today, educational institutions produce large amounts of data with the deployment of learning management systems. These large datasets provide an untapped potential to support and enhance decision‐making and operations. In recent times, machine learning (ML) has been applied to develop models utilizing this “big” data to assist in decision‐making. This study presents a systematic literature review into the application of ML to predict student performance. A total of 162 research articles from January 2010 to October 2022 were critically reviewed and analyzed by applying Kitchenham's systematic literature review approach. Our analysis categorized the literature predicting students' academic performance into two categories: (i) predicting student performance in assessments, courses or programs, and identifying students at‐risk of failing their course/program (129 studies); and (ii) predicting student dropout or retention in a course or program (33 studies). Classification is the most commonly used approach for predicting student performance (138 studies), followed by regression (25 studies) and clustering (9 studies). Supervised learning methods are used more often than semi‐supervised learning. Five most popular ML algorithms include the Decision Tree, Random Forest, Naïve Bayes, Artificial Neural Network, and Support Vector Machine. Historical records of students' grades and class performance, academic related data from learning management systems, and students' demographics are the most common features used for predicting students' performance. The most common methods used for feature selection are Information Gain‐based selection algorithms, Correlation‐based feature selection, and Gain Ratio. The general platforms/tools/libraries used in the studies include WEKA, Python, R, Rapid Miner, and MATLAB. We also investigated possible actions considered in the literature to help at‐risk students. We only found very few studies that deployed remedial actions and evaluated their impact on students' performance. In conclusion, ML has shown great potential in the prediction of student performance, but also has many areas of further research.

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Educational Data Mining: A Review

Cited by 138 publications

References 10 publications

Data Mining in Education

Data Mining in Education

Deep Learning applied to Learning Analytics and Educational Data Mining: A Systematic Literature Review

Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review

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