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The purpose of this study was to determine the attitude of in-service school teachers towards their Teaching and Leaning conceptions. The teaching and learning conceptions questionnaire (TLCQ) was used to collect data from 241 teachers teaching in various school boards. Results depicted that irrespective of the school boards, school teachers preferred a traditional approach to teaching when compared to constructivist approach. In other words, school teachers have a Traditionalist Conception towards teaching and learning. This information is further used in ensemble algorithms to verify the results and to understand if the same prediction is attained using machine learning techniques. For this, Random Forest, XG-Boost Regression Algorithm and an Ensemble learning algorithm called Ensemble-Random Forest–Support Vector Machine is implemented. The results obtained using the ensemble learning algorithm is most promising.

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Predicting In-Service Teachers’ Epistemological Beliefs Based on Their TL Conception Using Ensemble Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Pooja Manghirmalani Mishra,
  • Rabiya Saboowala

摘要

The purpose of this study was to determine the attitude of in-service school teachers towards their Teaching and Leaning conceptions. The teaching and learning conceptions questionnaire (TLCQ) was used to collect data from 241 teachers teaching in various school boards. Results depicted that irrespective of the school boards, school teachers preferred a traditional approach to teaching when compared to constructivist approach. In other words, school teachers have a Traditionalist Conception towards teaching and learning. This information is further used in ensemble algorithms to verify the results and to understand if the same prediction is attained using machine learning techniques. For this, Random Forest, XG-Boost Regression Algorithm and an Ensemble learning algorithm called Ensemble-Random Forest–Support Vector Machine is implemented. The results obtained using the ensemble learning algorithm is most promising.