Comparing the effectiveness of two remedial mathematics courses using modern regression discontinuity techniques

Baranyi, Máté; Molontay, Roland

doi:10.1080/10494820.2020.1839506

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…Since the examined university is a technical university, it is not so surprising that math skill is a great predictor of final academic success. It is a well-known global problem that mathematics is the main reason for dropout and delayed completion in STEM education (Baranyi & Molontay, 2021). There is a similar relationship between the chosen subject and the probability of graduation.…”

Section: Global Interpretationmentioning

confidence: 99%

Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention

Nagy

Molontay²

2023

Int J Artif Intell Educ

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Student drop-out is one of the most burning issues in STEM higher education, which induces considerable social and economic costs. Using machine learning tools for the early identification of students at risk of dropping out has gained a lot of interest recently. However, there has been little discussion on dropout prediction using interpretable machine learning (IML) and explainable artificial intelligence (XAI) tools.In this work, using the data of a large public Hungarian university, we demonstrate how IML and XAI tools can support educational stakeholders in dropout prediction. We show that complex machine learning models – such as the CatBoost classifier – can efficiently identify at-risk students relying solely on pre-enrollment achievement measures, however, they lack interpretability. Applying IML tools, such as permutation importance (PI), partial dependence plot (PDP), LIME, and SHAP values, we demonstrate how the predictions can be explained both globally and locally. Explaining individual predictions opens up great opportunities for personalized intervention, for example by offering the right remedial courses or tutoring sessions. Finally, we present the results of a user study that evaluates whether higher education stakeholders find these tools interpretable and useful.

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Section: Global Interpretationmentioning

confidence: 99%

Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention

Nagy

Molontay²

2023

Int J Artif Intell Educ

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“…Mivel a vizsgált egyetem egy műszaki egyetem, nem annyira meglepő, hogy a matematikai készség nagymértékben képes előre jelezni a végső tanulmányi sikert. Jól ismert globális probléma, hogy a STEM-képzéseken a matematika készségek hiánya a lemorzsolódás és a képzésidőn túli befejezés fő oka (Baranyi-Molontay 2021). Hasonló összefüggés van a választott tantárgy és diplomaszerzés valószínűsége között is.…”

Section: Lemorzsolódás Előrejelzéseunclassified

Lemorzsolódás előrejelzése személyre szabott értelmezhető gépi tanulási módszerek segítségével

Nagy

2023

ScientSec

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Összefoglalás. A hallgatói lemorzsolódás az egyik legégetőbb probléma a felsőoktatásban. Ebben a munkában a lemorzsolódás előrejelzésén keresztül bemutatjuk, hogyan tudják segíteni a felsőoktatás résztvevőit a magyarázható mesterséges intelligencia (XAI) eszközök, mint például a permutációs fontosság, a parciális függőségi ábra és a SHAP. Végül pedig kitérünk a kutatás gyakorlati hasznosulásának lehetőségeire, például, hogy az egyéni előrejelzések magyarázata hogyan teszi lehetővé a személyre szabott beavatkozást. Az elemzések során azt találtuk, hogy a középiskolai tanulmányi átlag bír a legnagyobb prediktív erővel a végzés tényére vonatkozóan. Továbbá annak ellenére, hogy egy műszaki egyetem adatait elemeztük, azt találtuk, hogy a humán tárgyaknak is nagy inkrementális prediktív erejük van a végzés tényére vonatkozóan a reál tárgyakhoz képest. Summary. Delayed completion and student drop-out are some of the most critical problems in higher education, especially regarding STEM programs. A high drop-out rate induces both individual and economic loss, hence a detailed investigation of the main reasons for dropping out is warranted. Recently, there has been a lot of interest in the use of machine learning methods for the early detection of students at risk of dropping out. However, there has not been much debate on the use of interpretable machine learning (IML) and explainable artificial intelligence (XAI) technologies for dropout prediction. In this paper, we show how IML and XAI techniques can assist educational stakeholders in dropout prediction using data from the Budapest University of Technology and Economics. We demonstrate that complex black-box machine learning algorithms, for example CatBoost, are able to effectively detect at-risk student using only pre-enrollment achievement measures, but they lack interpretability. We demonstrate how the predictions can be explained both globally and locally using IML methods including permutation importance (PI), partial dependence plot (PDP), LIME, and SHAP values. Using global interpretations, we have found that the factor that has the greatest impact on academic performance is the high school grade point average, which measures general knowledge by taking into account grades in history, mathematics, Hungarian language and literature, a foreign language and a science subject. However, we also found that both mathematics and the subject of choice are among the most important variables, which suggests that program-specific knowledge is not negligible and complements general knowledge. We discovered that students are more likely to drop out if they do not start their university studies immediately after leaving secondary school. Using a partial dependence plot, we showed that humanities also have incremental predictive power, despite the fact that this analysis is based on data from a technical university. Finally, we also discuss the potential practical applications of our work, such as how the explanation of individual predictions allows for personalized interventions, for example by offering appropriate remedial courses and tutoring sessions. Our approach is unique in that we not only estimate the probability of dropping out, but also interpret the model and provide explanations for each prediction. As a result, this framework can be used in several fields. By predicting which majors they could be most successful in based on high school performance indicators, it might, for instance, assist high school students in selecting the appropriate programs at universities and hence this way it could be used for career assistance. Through the explanations of local predictions, the framework provided can also assist students in identifying the skills they need to develop to succeed in their university studies.

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“…Four geographic areas supplied most of the research projects, and a large number of these were on a modest scale. Articles are arranged by the nation of origin, with four subgroups (the number in brackets refers to the number of articles): Europe (7), the United States (2), Australia (5), and Asia (1). Most of the research and development money went toward personalized e-learning, which is crucial to its development.…”

Section: An Overview Of Related Researchmentioning

confidence: 99%

A System Literature Review of Personalized e-Learning

2021

IJFS

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This paper investigates earlier research publications on personalized learning, exceptionally personalized e-learning, appearing in the World of Science Education and Educational Research categories. The researcher initially consults HistCite to identify the range of relevant articles and their citation links. The HistCite technique finds and isolates personalized e-learning studies. In addition, several articles are selected based on criteria and the EPPI guide for in-depth analysis to discover trends, design principles, and future research prospects.

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Comparing the effectiveness of two remedial mathematics courses using modern regression discontinuity techniques

Cited by 5 publications

References 35 publications

Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention

Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention

Lemorzsolódás előrejelzése személyre szabott értelmezhető gépi tanulási módszerek segítségével

A System Literature Review of Personalized e-Learning

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