Abstract
Evaluating student learning effect plays an essential role in education, which is typically done by assessing student’s final deliverables. However, the student’s learning process has not been properly explored in the past.In this paper, we propose an interactive student learning effect evaluation framework which focuses on in-process learning effect evaluation. In particular, our proposal analyzes students modeling assignment based on their operation records by using techniques of frequent sequential pattern mining, user behavior analysis, feature engineering, and process mining. A comprehensive online modeling platform has been developed to enable data collection. We have carried out a case study, in which we applied our approach to a real teaching scenario, consisting of student online modeling behavior data collected from 24 students majoring in computer science. We also associate our process mining results with the numeric evaluation values. The preliminary result of case analysis has shown good potential to mine student modeling patterns and interpret their behaviors, contributing to student learning effect evaluation.
Highlights
With a growing concern in student learning effect evaluation, traditional evaluation methods like paper exam, oral presentations, and practical experiments are no longer showing good performance (Struyven et al 2005)
Final grade can not meet the current demand in learning effect evaluation, so we are focusing on the in-process learning data analysis in this paper
Data collection In order to get first-hand data, we build up an online modeling platform, whose details will be discussed in “Online modeling platform” section
Summary
With a growing concern in student learning effect evaluation, traditional evaluation methods like paper exam, oral presentations, and practical experiments are no longer showing good performance (Struyven et al 2005). Final grade can not meet the current demand in learning effect evaluation, so we are focusing on the in-process learning data analysis in this paper. A comprehensive and impactful in-process student learning effect evaluation method enables a more precise and reasonable evaluation. The first challenge in this topic is the data collection. The in-process data has been overlooked by traditional evaluation methods for a long time, so we need to first obtain useful in-process data efficiently. The other challenge is to find the proper in-process learning data analysis methods, where identifying meaningful user behavior patterns is the core problem
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