Identifying Predictors of Academic Success for Part-time Students at Polytechnic Institutes in Malaysia

Ibrahim, Norhayati; Freeman, Steven A.; Shelley, Mack

doi:10.4018/javet.2011100101

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…[17] Institution [40] Institution [47] Institution [11] Institution [51] Institution [21] Program [44] Program [1] Program [19] Program [42] Program [27] Course [41] Course [53] Course [6] Course [20] Course [52] Course [8] Course [4] Course [24] Course [2] Course [10] Course…”

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confidence: 99%

A Robust Machine Learning Technique to Predict Low-performing Students

Liao

Zingaro

Thai

et al. 2019

ACM Trans. Comput. Educ.

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As enrollments and class sizes in postsecondary institutions have increased, instructors have sought automated and lightweight means to identify students who are at risk of performing poorly in a course. This identification must be performed early enough in the term to allow instructors to assist those students before they fall irreparably behind. This study describes a modeling methodology that predicts student final exam scores in the third week of the term by using the clicker data that is automatically collected for instructors when they employ the Peer Instruction pedagogy. The modeling technique uses a support vector machine binary classifier, trained on one term of a course, to predict outcomes in the subsequent term. We applied this modeling technique to five different courses across the computer science curriculum, taught by three different instructors at two different institutions. Our modeling approach includes a set of strengths not seen wholesale in prior work, while maintaining competitive levels of accuracy with that work. These strengths include using a lightweight source of student data, affording early detection of struggling students, and predicting outcomes across terms in a natural setting (different final exams, minor changes to course content), across multiple courses in a curriculum, and across multiple institutions.

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confidence: 99%

A Robust Machine Learning Technique to Predict Low-performing Students

Liao

Zingaro

Thai

et al. 2019

ACM Trans. Comput. Educ.

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Predicting academic performance: a systematic literature review

Hellas

Ihantola

Petersen

et al. 2018

Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education

244

171

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The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

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Identifying Predictors of Academic Success for Part-time Students at Polytechnic Institutes in Malaysia

Cited by 2 publications

References 43 publications

A Robust Machine Learning Technique to Predict Low-performing Students

A Robust Machine Learning Technique to Predict Low-performing Students

Predicting academic performance: a systematic literature review

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