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Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data

Author

Listed:
  • Adriano Bressane

    (Environmental Engineering Department, Institute of Science and Technology, São Paulo State University, São José dos Campos 12245-000, Brazil)

  • Marianne Spalding

    (Environmental Engineering Department, Institute of Science and Technology, São Paulo State University, São José dos Campos 12245-000, Brazil)

  • Daniel Zwirn

    (Environmental Engineering Department, Institute of Science and Technology, São Paulo State University, São José dos Campos 12245-000, Brazil)

  • Anna Isabel Silva Loureiro

    (Civil and Environmental Engineering Graduate Program, Faculty of Engineering, São Paulo State University, Bauru 17033-360, Brazil)

  • Abayomi Oluwatobiloba Bankole

    (Civil and Environmental Engineering Graduate Program, Faculty of Engineering, São Paulo State University, Bauru 17033-360, Brazil)

  • Rogério Galante Negri

    (Environmental Engineering Department, Institute of Science and Technology, São Paulo State University, São José dos Campos 12245-000, Brazil)

  • Irineu de Brito Junior

    (Environmental Engineering Department, Institute of Science and Technology, São Paulo State University, São José dos Campos 12245-000, Brazil)

  • Jorge Kennety Silva Formiga

    (Environmental Engineering Department, Institute of Science and Technology, São Paulo State University, São José dos Campos 12245-000, Brazil)

  • Liliam César de Castro Medeiros

    (Environmental Engineering Department, Institute of Science and Technology, São Paulo State University, São José dos Campos 12245-000, Brazil)

  • Luana Albertani Pampuch Bortolozo

    (Environmental Engineering Department, Institute of Science and Technology, São Paulo State University, São José dos Campos 12245-000, Brazil)

  • Rodrigo Moruzzi

    (Environmental Engineering Department, Institute of Science and Technology, São Paulo State University, São José dos Campos 12245-000, Brazil)

Abstract

Understanding the key factors that play an important role in students’ performance can assist improvements in the teaching-learning process. As an alternative, artificial intelligence (AI) methods have enormous potential, facilitating a new trend in education. Despite the advances, there is an open debate on the most suitable model for machine learning applied to forecast student performance patterns. This paper addresses this gap, where a comparative analysis between AI methods was performed. As a research hypothesis, a fuzzy inference system (FIS) should provide the best accuracy in this forecast task, due to its ability to deal with uncertainties. To do so, this paper introduces a model proposal based on AI using a FIS. An online survey was carried to collect data. Filling out a self-report, respondents declare how often they use some learning strategies. In addition, we also used historical records of students’ grades and retention from the last 5 years before the COVID pandemic. Firstly, two experimental groups were composed of students with failing and passing grades, compared by the Mann-Whitney test. Secondly, an association between the ‘frequency of using learning strategies’ and ‘occurrence of failing grades’ was quantified using a logistic regression model. Then, a discriminant analysis was performed to build an Index of Student Performance Expectation (SPE). Considering the learning strategies with greater discriminating power, the fuzzy AI-based model was built using the database of historical records. The learning strategies with the most significant effect on students’ performance were lesson review (34.6%), bibliography reading (25.6%), class attendance (23.5%), and emotion control (16.3%). The fuzzy AI-based model proposal outperformed other AI methods, achieving 94.0% accuracy during training and a generalization capacity of 91.9% over the testing dataset. As a practical implication, the SPE index can be applied as a tool to support students’ planning in relation to the use of learning strategies. In turn, the AI model based on fuzzy can assist professors in identifying students at higher risk of retention, enabling preventive interventions.

Suggested Citation

  • Adriano Bressane & Marianne Spalding & Daniel Zwirn & Anna Isabel Silva Loureiro & Abayomi Oluwatobiloba Bankole & Rogério Galante Negri & Irineu de Brito Junior & Jorge Kennety Silva Formiga & Liliam, 2022. "Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14071-:d:956481
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    References listed on IDEAS

    as
    1. Róbert Csalódi & János Abonyi, 2021. "Integrated Survival Analysis and Frequent Pattern Mining for Course Failure-Based Prediction of Student Dropout," Mathematics, MDPI, vol. 9(5), pages 1-17, February.
    2. Diego Buenaño-Fernández & David Gil & Sergio Luján-Mora, 2019. "Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
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