Data-driven intervention-level prediction modeling for academic performance

Mgala, Mvurya; Mbogho, Audrey

doi:10.1145/2737856.2738012

Cited by 15 publications

(9 citation statements)

References 24 publications

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“…Only through understanding both what works and what does not can we form a holistic understanding of the topic. [7, 22, 46, 47, 62, 63, 71-73, 83, 87, 89, 94, 104, 107, 147, 149, 166, 172, 173, 177, 184, 186, 206, 208, 215, 218, 220, 231, 232, 244, 279, 296, 299, 315, 319, 327, 329, 337, 341, 358, 359, 373, 374, 407, 411, 424] Exam / Post-test Grade or Score [9,10,21,24,33,35,52,59,67,68,77,80,81,85,90,96,109,114,116,127,136,152,162,163,169,193,195,199,202,205,214,217,224,233,238,241,270,274,275,277,284,287,301,302,309,314,…”

Section: Calls To the Communitymentioning

confidence: 99%

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

245

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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Section: Calls To the Communitymentioning

confidence: 99%

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

245

171

View full text Add to dashboard Cite

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“…Besides, the minority class usually represents the most important concept to be learned, it is difficult to identify it due to exceptional and significant cases (López et al, 2013). Since accuracy as a widely used metric has less effect on minority class than majority class (Longadge et al, 2013;Lin and Chen, 2013), several researchers applied other metrics such as F-measure (Mgala and Mbogho 2015;Rovira et al, 2017;Aulck et al, 2017), Mean Absolute Error (MAE) (Ameri et al, 2016;Elbadrawy et al, 2016;Lakkaraju et al, 2015;Rovira et al, 2017), Area Under the curve (AUC) (Liang et al, 2016;Fei and Yeung, 2015;Aulck et al, 2016;Prieto et al, 2017;Mgala and Mbogho, 2015;Halland et al, 2015), mean squared error (Iam-On and Boongoen 2017; Xu et al 2017), Root-Mean-Square Error (RMSE) (Elbadrawy et al, 2016), error residuals (Poh and Smythe 2015), and misclassification rates (Hung et al, 2017) on addressing the problem of student dropout.…”

Section: Machine Learning In Educationmentioning

confidence: 99%

“…Furthermore, MOOC and Moodle are among the most used platforms which offer public datasets to be used on addressing the student dropout problem. The limitation of public datasets from developing countries (Mgala and Mbogho, 2015), brought the need to develop more datasets from different geographical location. This may include transforming registration information of students with ongoing academic progress from paper based approach into electronic storage.…”

Section: Open Challenges For Future Researchmentioning

confidence: 99%

A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction

Mduma

Kalegele

Machuve

2019

Data Science Journal

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School dropout is absenteeism from school for no good reason for a continuous number of days. Addressing this challenge requires a thorough understanding of the underlying issues and effective planning for interventions. Over the years machine learning has gained much attention on addressing the problem of students dropout. This is because machine learning techniques can effectively facilitate determination of at-risk students and timely planning for interventions. In order to collect, organize, and synthesize existing knowledge in the field of machine learning on addressing student dropout; literature in academic journals, books and case studies have been surveyed. The survey reveal that, several machine learning algorithms have been proposed in literature. However, most of those algorithms have been developed and tested in developed countries. Hence, developing countries are facing lack of research on the use of machine learning on addressing this problem. Furthermore, many studies focus on addressing student dropout using student level datasets. However, developing countries need to include school level datasets due to the issue of limited resources. Therefore, this paper presents an overview of machine learning in education with the focus on techniques for student dropout prediction. Furthermore, the paper highlights open challenges for future research directions.

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“…Apart from that, an experiment was conducted by researchers to build an intervention prediction models focusing on rural schools in a developing country. The results from the experiment demonstrate that it is possible to attain reasonably accurate intervention prediction models even with a reduced dataset [15]. It has been observed in a research study that a collaborative multi-regression model provide better accuracy in predicting student performance than single linear regression as it consider personal student differences [9].…”

Section: Related Workmentioning

confidence: 81%

Where students go and how they do: Wi-Fi location data versus academic performance

Sarkar

Carpenter

Bader-El-Den

et al. 2016

2016 9th International Conference on Human System Interactions (HSI)

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Arguably students' academic performance is one of the key areas that a university should focus on and perform further analysis on available data. One of the primary objectives of this research is to analyse the Wi-Fi AMP (Access Module Processor) data gathered by the Information Services Department of University of Portsmouth and determine how students are spending their time on university campus. Later these data have been used to examine if this information can be related to the academic performance of the student. With the analysed Wi-Fi data and academic records of the students a preliminary model has been created which tries to show how the time spent by the students in different types of location are correlated.

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Data-driven intervention-level prediction modeling for academic performance

Cited by 15 publications

References 24 publications

Predicting academic performance: a systematic literature review

Predicting academic performance: a systematic literature review

A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction

Where students go and how they do: Wi-Fi location data versus academic performance

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