Educational data mining and learning analytics for 21st century higher education: A review and synthesis

Aldowah, Hanan; Al-Samarraie, Hosam; Fauzy, Wan Mohamad

doi:10.1016/j.tele.2019.01.007

Cited by 390 publications

(214 citation statements)

References 177 publications

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“…Finally, the most recent review devoted to EDM has been developed by Aldowah et al [9] in 2019. This study constrained the research to works applied in the context of higher education.…”

Section: Review Of Previous Surveysmentioning

confidence: 99%

A Systematic Review of Deep Learning Approaches to Educational Data Mining

et al. 2019

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Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

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“…Finally, the most recent review devoted to EDM has been developed by Aldowah et al [9] in 2019. This study constrained the research to works applied in the context of higher education.…”

Section: Review Of Previous Surveysmentioning

confidence: 99%

A Systematic Review of Deep Learning Approaches to Educational Data Mining

et al. 2019

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“…There inevitably generates a vast amount of learning-related data. Along with the rapid development of Internet and mobile technologies, learning analytics make use of big data for analyzing the vast amount of data obtained from the teaching and learning process (Aldowah et al 2019;Baker and Inventado 2014).…”

Section: Learning Analyticsmentioning

confidence: 99%

Learning analytics: current trends and innovative practices

2020

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“…Results have helped to improve student's performance, instructor's lectures delivery mechanism, and student's behavior. A 17 years review and synthesis from year 2000 to year 2017 [17] concludes that EDM in higher education has successfully been used in four dimensions. These dimensions includes computer supported learning analytics, computer supported predictive analytics, computer supported behavioral analytics and computer supported visualization analytics.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Trainees Enrollment Behavior and Course Selection Variables in Technical and Vocational Education Training (TVET) Program Using Education Data Mining

Hassan¹,

Awan²

2019

IJMECS

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Producing skilled workforce according to industry required skills is quite challenging. Knowledge of trainee's enrollment behavior and trainee's course selection variables can help to address this issue. Prior knowledge of both can help to plan and target right geographic locations and right audience to produce industry required skilled workforce. Globally Technical and Vocational Education Training (TVET) is used to provide skilled workforce for the industry. TVET is an educational stream which focus learning through more practicing with less theory knowledge. In this article, we have analyzed TVET actual enrollment data of 2017-2018 session from a TVET training provider organization of Punjab, Pakistan. The purpose of this analysis is to understand trainee's enrollment behavior and course selection variables which plays an important role in TVET course selection by the trainees. This enrollment behavior and course selection variables can be used to monitor and control industry required and produced skilled TVET workforce. We developed a framework which contain series of steps to perform this analysis to extract knowledge. We used educational data mining techniques of association, clustering and classification to extract knowledge. The analysis reveals that central Punjab youth is getting more TVET education as compare to south and north Punjab, Pakistan. Similarly, trainee's 'age group', 'qualification', 'gender', 'religion' and 'marital status' are potential variables which can play important role in TVET course selection. By controlling these variables and integrating TVET training provider institutes, funding agencies and industry, we can smartly produce TVET skilled workforce required for industry nationally and internationally.

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Educational data mining and learning analytics for 21st century higher education: A review and synthesis

Cited by 390 publications

References 177 publications

A Systematic Review of Deep Learning Approaches to Educational Data Mining

A Systematic Review of Deep Learning Approaches to Educational Data Mining

Learning analytics: current trends and innovative practices

Identification of Trainees Enrollment Behavior and Course Selection Variables in Technical and Vocational Education Training (TVET) Program Using Education Data Mining

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