Traits versus Grades—The Incremental Predictive Power of Positive Psychological Factors over Pre-Enrollment Achievement Measures on Academic Performance

Séllei, Beatrix; Stumphauser, Nóra; Molontay, Roland

doi:10.3390/app11041744

Cited by 10 publications

(7 citation statements)

References 58 publications

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“…Moreover, it is important to note that while student drop-out is a thousand-factor problem, we only have a few, mostly high school performance-related measurements. Other variables have also been associated with student success such as socio-economic status (Freitas et al, 2020;Zwick & Himelfarb, 2011), on-campus living (Zeleny et al, 2021) and various psychological factors (Séllei et al, 2021).…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention

Nagy

Molontay²

2023

Int J Artif Intell Educ

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“…Based on future research directions, the evidence-based use of positive psychology on campus (Chodkiewitz, Boyle, 2016;Martin, 2009), it would be worth developing a training curriculum special for management and economics students, with three main focuses: (1) emotional independence, (2) stress management, and (3) grit. As can be seen from the literature, stress management itself is important for university success (Séllei et al, 2021) but the others could be as important in the level of personnel well-being. These together foster optimal performances.…”

Section: Discussionmentioning

confidence: 99%

“…T he emotional competencies of university students are related to academic success (Parker et al, 2004;Séllei et al, 2021) and professional performance (Pertegal-Felices et al, 2017). This paper investigates whether students of management and economics have an emotional profile.…”

Section: Introductionmentioning

confidence: 99%

“…Theoretically, the Bologna Declaration (EU, 1999) emphasizes the importance of the development of social competencies and emotional-related skills in higher education. The development of emotional skills is not directly related to the curriculum in most of the universities just in terms of competencies (Boddy et al, 1995;Jaeger, 2003) and there is only a little research evidence on how exactly the emotional profile of students enhance the academic performance (Low and Nelson, 2006;Séllei et al, 2021). It is already known that there are differences between different faculties and gender (Sánchez-Ruiz et al, 2010;Kafetsios et al, 2009;Pertegal-Felices et al, 2014) or depending on culture (Min et al, 2018), for example, social sciences students are better in emotional skills as technical students, and gender has a different effect depending on faculty (Larson et al, 2007;Sánchez-Ruiz et al, 2010) but the clear emotional profile of different students is not available yet (Ganotice, et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Management and Economics Students’ Emotional Profile

Séllei¹,

Hungary²

2021

International Scientific Conference EMAN – Economics and Management: How to Cope With Disrupted Times

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Although emotionally resonant leadership is widely known, higher education doesn’t focus on the development of emotional-social competencies of students. In this paper, the author presents the positive psychological profile of management and economics students and suggests renewing higher education. Data came from the 1st semester of economics and management students (n=826). Students in each year have a homogenous profile. They are extroverted, with an interesting positive psychological profile. Based on their emotional profile, students are good in many intrapersonal emotional intelligence factors, interpersonal skills, or mood. But, they have lower scores in stress-related emotional competencies. Their relatively low Grit score shows that they are likely to lose focus, although this is one key to work-flow and longtime perseverance.

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“…Emellett fontos megjegyezni, hogy míg a hallgatói lemorzsolódást számos tényező befolyásolja, addig mi csak néhány, főként a középiskolai teljesítményhez kapcsolódó változókkal rendelkezünk. Más változókat is összefüggésbe hoztak a hallgatói sikerrel, mint például a társadalmi-gazdasági státusz (Zwick-Himelfarb 2011;Freitas et al 2020), a kollégiumi lét (Zeleny-Molontay-Szabó 2021) és különböző pszichológiai tényezők (Séllei-Stumphauser-Molontay 2021).…”

Section: öSszefoglalás éS Konklúzióunclassified

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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Traits versus Grades—The Incremental Predictive Power of Positive Psychological Factors over Pre-Enrollment Achievement Measures on Academic Performance

Cited by 10 publications

References 58 publications

Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention

Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention

Management and Economics Students’ Emotional Profile

Lemorzsolódás előrejelzése személyre szabott értelmezhető gépi tanulási módszerek segítségével

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