How Well Do Teachers Predict Students’ Actions in Solving an Ill-Defined Problem in STEM Education: A Solution Using Sequential Pattern Mining

Abstract

Predicting students' line of actions helps educators give adequate guidance to students, but this remains a challenge in science, technology, engineering, and mathematics (STEM) education. Given this, there is a scarcity of related research that will help improve teachers' prediction capabilities on students' line of actions when tackling ill-defined problems (IDPs), as well as how emerging data mining techniques could contribute to such prediction. The present study aims to fill the gap by measuring the quality of teachers' predictions (labeled expert prediction), where 43 elementary teachers predict students' step-by-step actions when solving an IDP through the light path task (LPT), and then comparing its quality with that of machine prediction, executed via sequential pattern mining techniques. Data on students' lines of action were collected from 501 5th- and 6th-grade students, aged 11-12. The results showed the significantly lower accuracy of expert prediction compared to machine prediction, which highlights the advantages of using data mining in predicting students' actions and shows its possible application as a recommendation system to provide adaptive guidance in future STEM education.

Keywords

• Data mining
• ill-defined problem
• machine prediction
• problem-solving
• sequential pattern mining
• teacher effectiveness