Recognizing patterns of student’s modeling behaviour patterns via process mining

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.

A constantly improving model for universities readiness in the application of e-learning practices during the COVID-19 pandemic: a qualitative approach

“…Furthermore, the dynamicity and elegance of learning processes in social networks can enhance several advantageous qualities. As Wang et al ( 2019 ) discovered, modeling student behaviors are an essential factor that facilitates learning.…”

“…Another group of studies (Wang and Wu 2008 ) conducted in different environments with various research methodologies suggested that students who receive effective, timely feedback in e-learning environments exhibit better and higher-quality performance in e-learning. These studies showed the importance of providing a framework to assess student learning impact by emphasizing assessment during the learning process (Wang et al 2019 ). The literature on e-learning environments points to the need for further systematization requires the development of models for designing several constructs, such as user-centered design, educational diversity, blended learning spaces, and facilitating mixed or blended learning (Freigang et al 2018 ).…”

“…This environment enables addressing weaknesses of the learning environment through interventions while addressing problems in physical environments (Dron 2018 ). More satisfactory methods can be used as a reference to develop models that can agreeably display student behaviors (Wang et al 2019 ). Those methods represent “instructors’ use of web-based computer-aided tools for learning” (Tatnall 2020 ).…”

“…Wang et al ( 2019 ) tried to present a framework to assess the impact of student learning. Using a framework consisting of four sections, namely data collection, data extraction, behavioral analysis, and process extraction, they found that students often complete all actions of one type first and then start the next type.…”

Reviewing Process Mining Applications and Techniques in Education

“…Bogarín et al [35] based on data from LMS logs managed to obtain more specific and accurate models of students' bahaviour. The online modeling platform BJUTModeling was used in studies [36] and [37] as a tool for student learning evaluation effect. Based on PM, authors achieved student modeling patterns mining and their behaviors interpretation.…”