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

Abstract

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.

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

“…Evaluation of teaching Four studies used data mining algorithms to evaluate lecturer performance through course evaluations (Agaoglu, 2016;Ahmad & Rashid, 2016;DeCarlo & Rizk, 2010;Gutierrez, Canul-Reich, Ochoa Zezzatti, Margain, & Ponce, 2018), with Agaoglu (2016) finding, through using four different classification techniques, that many questions in the evaluation questionnaire were irrelevant. The application of an algorithm to evaluate the impact of teaching methods in a differential equations class, found that online homework with immediate feedback was more effective than clickers (Duzhin & Gustafsson, 2018). The study also found that, whilst previous exam results are generally good predictors for future exam results, they say very little about students' expected performance in project-based tasks.…”

Predictive Models of Student College Commitment Decisions Using Machine Learning

“…Duzhin and Gustafsson [24] used machine learning to control the effect of confounding variables in "quasi-experiments" that seek to determine which teaching methods have the greatest effect on student learning. The authors considered teaching methods such as clickers, handwritten homework and online homework with immediate feedback and included the confounding variables of student prior knowledge and various student characteristics such as diligence, talent and motivation.…”

“…The authors considered teaching methods such as clickers, handwritten homework and online homework with immediate feedback and included the confounding variables of student prior knowledge and various student characteristics such as diligence, talent and motivation. Our work differs in that it tries to predict student college commitment decisions using multiple machine learning techniques and its potential benefit is to the school's finances, while the work in [24] seeks to assist instructors in identifying teaching methods that work best for them.…”

Using Mentimeter to enhance learning and teaching in a large class

“…Clickers provide a more effective teaching strategy than the traditional assessment (Duzhin,and Gustafsson, 2018). Using the Clicker technology, students can answer the questions displayed on the classroom screen anonymously by using the hand-held 3 device.…”