Machine Learning-Based App for Self-Evaluation of Teacher-Specific Instructional Style and Tools

Fedor Duzhin,Anders Gustafsson

doi:10.3390/educsci8010007

Abstract

Course instructors need to assess the efficacy of their teaching methods, but experiments in education are seldom politically, administratively, or ethically feasible. Quasi-experimental tools, on the other hand, are often problematic, as they are typically too complicated to be of widespread use to educators and may suffer from selection bias occurring due to confounding variables such as students’ prior knowledge. We developed a machine learning algorithm that accounts for students’ prior knowledge. Our algorithm is based on symbolic regression that uses non-experimental data on previous scores collected by the university as input. It can predict 60–70 percent of variation in students’ exam scores. Applying our algorithm to evaluate the impact of teaching methods in an ordinary differential equations class, we found that clickers were a more effective teaching strategy as compared to traditional handwritten homework; however, online homework with immediate feedback was found to be even more effective than clickers. The novelty of our findings is in the method (machine learning-based analysis of non-experimental data) and in the fact that we compare the effectiveness of clickers and handwritten homework in teaching undergraduate mathematics. Evaluating the methods used in a calculus class, we found that active team work seemed to be more beneficial for students than individual work. Our algorithm has been integrated into an app that we are sharing with the educational community, so it can be used by practitioners without advanced methodological training.

Highlights

There exists a gap between educational research and teaching practice
John Hattie, the author of [1]—a synthesis of more than 800 meta-studies: We have a rich educational research base, but rarely is it used by teachers, and rarely does it lead to policy changes that affect the nature of teaching
In Quasi-experiment 2, we have seen that during an introductory calculus course taught with a TBL approach, high student involvement in team activities as measured by Peer Evaluations and Team Leader responsibilities was more indicative gain than high performance at individual readiness assessment quizzes

Summary

Introduction

There exists a gap between educational research and teaching practice. according toJohn Hattie, the author of [1]—a synthesis of more than 800 meta-studies: We have a rich educational research base, but rarely is it used by teachers, and rarely does it lead to policy changes that affect the nature of teaching.One of the reasons is perhaps that, according to William E. There exists a gap between educational research and teaching practice. John Hattie, the author of [1]—a synthesis of more than 800 meta-studies: We have a rich educational research base, but rarely is it used by teachers, and rarely does it lead to policy changes that affect the nature of teaching. One of the reasons is perhaps that, according to William E. Becker (see [2]), Quantitative studies of the effects of one teaching method versus another are either not cited or are few in number. There are reasons why trustworthy quantitative studies are scarce in education. The gold standard of a quantitative study is a randomized controlled trial. Proper randomized clinical trials are seldom available in the classroom because educators are given resources to teach rather than

Methods

Results

Conclusion