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Developing a Risk Model to Control Attrition by Analyzing Students’ Academic and Nonacademic Data

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  • Vassilis Zakopoulos
  • Ioannis Georgakopoulos
  • Pelagia Kontaxaki

Abstract

Purpose: The research objective is to address the problem of students’ attrition by identifying students who are liable to fail their courses. Students’ behavioral engagement data along with students’ nonacademic data were analyzed in terms of a binary logistics regression with a view to developing a model to decide on the risk factors. Design/methodology/approach: A binary variable was modeled to describe students at risk and students not at risk. The students’ behavioral engagement data constituted the independent variables in our regression analysis whereas the variable describing students at risk was the dependent variable. The students’ behavioral engagement data was collected by students’ learning activities. The eLearning part was implemented by Moodle. The data was collected after the final test. The regression analysis outcome was a classification table indicating the correct classification percentage of our model. In parallel an econometric study was also carried out in order to examine liable nonacademic risk factors. Findings: Factors that are related to students’ engagement could be deemed to be decisive in the context of our study. The econometric study proved that governmental financial support could be viewed as a cardinal factor that could potentially deter students from dropping out of university. Originality/value: The originality of our research lies in the fact that the issue of controlling students’ attrition is not addressed in a fragmentary way by just carrying out a specific analysis and coming up with results, like many similar studies in the literature. Thereby, a concrete methodology was developed on the basis of an established generic risk management framework. Therefore, the control of students at risk is included in the phases of a potent framework. The added value of our research is centered on the fact that our risk model could potentially be applied to any course in order to come up with the respective risk factors.

Suggested Citation

  • Vassilis Zakopoulos & Ioannis Georgakopoulos & Pelagia Kontaxaki, 2022. "Developing a Risk Model to Control Attrition by Analyzing Students’ Academic and Nonacademic Data," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 350-366.
    • Handle: RePEc:ers:journl:v:xxv:y:2022:i:2:p:350-366
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    References listed on IDEAS

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    1. Ralph Stinebrickner & Todd Stinebrickner, 2008. "The Effect of Credit Constraints on the College Drop-Out Decision: A Direct Approach Using a New Panel Study," American Economic Review, American Economic Association, vol. 98(5), pages 2163-2184, December.
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    3. Gérard Lassibille & María Lucía Navarro Gómez, 2008. "Why do higher education students drop out? Evidence from Spain," Post-Print halshs-00324365, HAL.
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    7. Vassilis Zakopoulos, 2022. "A Framework to Identify Students at Risk in Blended Business Informatics Courses: A Case Study on Moodle," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 239-247.
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    More about this item

    Keywords

    Students’ engagement; risk model; risk factors; students’ attrition.;
    All these keywords.

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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