Abstract
Curriculum mining is research area that assess students’ learning behavior and compare it with the curriculum guideline. Previous work developed sequence matching alignment approach to check the conformance between students’ learning behavior and curriculum guideline. Considering only the sequence matching alignment is insufficient to understand the patterns of group of students. Another work proposed an approach by aggregating the students’ profile to represent students’ learning behavior and investigate the impact of the learning behavior to their learning performance. However, the aggregate profile approach considers the entire period of study rather than segmented period. This study proposes a methodology to assess students’ learning path with segmented period i.e. the semester of the related curriculum. The segmented-period profile generated would be the input for sequence matching alignment approach to assess the conformity of students’ behavior with the prior curriculum guideline. Real curriculum data has been used to test the effectivity of the methodology. The results show that the students can be grouped into various cluster per semesters that have different characteristic with respect to their learning behavior and performance. The results can be analyzed further to improve the curriculum guideline.
Highlights
Educational Process Mining (EPM) is an emerging research area aiming at constructing a complete and compact educational process model that represents students’ learning behavior, checking whether the modeled learning behavior matches the observed behavior and projecting information from logs onto the model to gain knowledge about the process (Třcka and Pechenizkiy [1])
This study proposed a methodology for both curriculum assessment and students’ learning behavior analysis using segmented-trace profiles
The segmented-trace profiles were an extension of sequence matching alignment in the domain of curriculum assessment
Summary
Educational Process Mining (EPM) is an emerging research area aiming at constructing a complete and compact educational process model that represents students’ learning behavior, checking whether the modeled learning behavior matches the observed behavior and projecting information from logs onto the model to gain knowledge about the process (Třcka and Pechenizkiy [1]). Some of the applications of EPM in the academic institutions are to predict the drop out (Dekker et al [2]), to recommend relevant courses to the students (Wang and Zaïane [3]), and to improve the current curriculum (Wong and Lavrencic [4]). Curriculum mining, as a part of EPM, aims to explore and analyze the students’ learning behavior from student database. Regarding the property of student database, there are two aspects of analysis (van der Aalst [7]). The time granularity of event log is always a time point with an item (i.e., activity performed in a specific time point) while the student database comprises of data with multiple items in a time point (i.e., some courses taken in a semester). The variability of instance attributes in different timestamp has never been considered in process mining (i.e., grade point average (GPA) of a student until
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