Success/Failure Prediction for Final Examination Using the Trend of Weekly Online Testing

Hirose, Hideo

doi:10.1109/iiai-aai.2018.00036

Cited by 7 publications

(11 citation statements)

References 16 publications

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“…The line separating both categories is sometimes very thin, as some of the predictors are able to offer results using only data available early in a course, which would easily allow them to serve as basis for an EWS. Examples of these situations can be observed in the works published by Thompson et al [11], Umer et al [13], and Hirose [15].…”

Section: Discussionmentioning

confidence: 94%

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Systematic Literature Review of Predictive Analysis Tools in Higher Education

et al. 2019

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The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis techniques are present in studies that estimate academic outcomes using different kinds of student-related data. Furthermore, predictive algorithms are the basis of tools such as early warning systems (EWS): applications able to foresee future risks, such as the likelihood of students failing or dropping out of a course, and alert of such risks so that corrective measures can be taken. The purpose of this literature review is to provide an overview of the current state of research activity regarding predictive analytics in higher education, highlighting the most relevant instances of predictors and EWS that have been used in practice. The PRISMA guidelines for systematic literature reviews were followed in this study. The document search process yielded 1382 results, out of which 26 applications were selected as relevant examples of predictors and EWS, each of them defined by the contexts where they were applied and the data that they used. However, one common shortcoming is that they are usually applied in limited scenarios, such as a single course, evidencing that building a predictive application able to work well under different teaching and learning methodologies is an arduous task.

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Section: Discussionmentioning

confidence: 94%

“…Hirose used item-response theory (IRT) to make estimations of students' abilities regarding the contents of a course [15]. This study targeted introductory calculus and algebra courses at a Japanese higher education institution.…”

Section: Predictorsmentioning

confidence: 99%

Systematic Literature Review of Predictive Analysis Tools in Higher Education

et al. 2019

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“…In the previous paper [19], we proposed the similarity via the nearest neighbor using the estimated ability trends in order to identify successful/failed students with much higher reliability than the one simple decision tree result using the full response matrix in prediction. In using the similarity, we used θ 1 (i, k) by incorporating the tentative response matrices M m,k (N, mk), k = 1, .…”

Section: Discrimination Methods Of Failed Studentsmentioning

confidence: 99%

“…As was shown in the previous paper [19], we dealt with typical three cases in using the LCT response results: 1) from LCT no.1 to LCT no.4, 2) from LCT no.1 to LCT no.7, 3) from LCT no.1 to LCT no.11. We will deal with the same cases treated similarity in [19].…”

Section: Identifying Successful/failed Students Using Similaritymentioning

confidence: 99%

“…In the previous paper [19], we showed that we predicted the success/failure status for the final examination to each student at early stages in courses using the current trends of estimated abilities to the learning check testing in terms of the item response theory (e.g., see [1,9,29]). However, we used the same testing results in prediction and in construction of the mathematical model.…”

Section: Introductionmentioning

confidence: 99%

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