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Computer Science Students Early Drop-Out Prediction Using Machine Learning: A Case Study

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Learning with Technologies and Technologies in Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 456))

Abstract

High dropout rates in computer science programs are a major global issue. In this chapter, we describe the results of the educational machine learning case study with the aim of early prediction of computer science students’ dropout in the Virumaa College of Tallinn University of Technology. For many years TalTech Virumaa College has faced a high dropout rate among first-year students. Among computer science students it is about 40%. In order to reduce the dropout rate, interviews and surveys with student candidates are carried out in the College admission process since the academic year 2019/20 to assess students’ motivation and readiness to study and ensure the suitability of the specialty. To determine factors that influence attrition rates we analyze the data collected during the admission process and apply the text mining techniques to analyze the students’ essays conducted at the end of the first-year study in the frame of the course Introduction to the Specialty. Using these factors, we evaluate predictive models and therefore make recommendations to improve the admission process and reduce the drop-out rate for first-year students.

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Author information

Authors and Affiliations

  1. Virumaa College of Tallinn University of Technology, Järveküla tee 75, 30322, Kohtla-Järve, Estonia

    Natalja Maksimova, Avar Pentel & Olga Dunajeva

Authors
  1. Natalja Maksimova
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  2. Avar Pentel
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  3. Olga Dunajeva
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Corresponding author

Correspondence to Olga Dunajeva .

Editor information

Editors and Affiliations

  1. CTI Global, Frankfurt, Germany

    Michael E. Auer

  2. Faculty of Informatics and Computer Science, The British University in Egypt, Cairo, Egypt

    Andreas Pester

  3. College of Engineering, University of Georgia, Athens, GA, USA

    Dominik May

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Maksimova, N., Pentel, A., Dunajeva, O. (2022). Computer Science Students Early Drop-Out Prediction Using Machine Learning: A Case Study. In: Auer, M.E., Pester, A., May, D. (eds) Learning with Technologies and Technologies in Learning. Lecture Notes in Networks and Systems, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-031-04286-7_25

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