English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Educational process mining is one of the research domains that utilizes students&#x2019; learning behavior to match students&#x2019; actual courses taken and the designed curriculum. While most works attempt to deal with the case perspective (i.e., traces of the cases), the temporal case perspective has not been discussed. The temporal case perspective aims to understand the temporal patterns of cases (e.g., students&#x2019; learning behavior in a semester). This study proposes modified cluster evolution analysis, called profile-based cluster evolution analysis, for students&#x2019; learning behavior based on profiles. The results show three salient features: (1) cluster generation; (2) within-cluster generation; and (3) time-based between-cluster generation. The cluster evolution phase modifies the existing cluster evolution analysis with a dynamic profiler. The model was tested on actual educational data of the Information System Department in Indonesia. The results showed the learning behavior of students who graduated on time, the learning behavior of students who graduated late, and the learning behavior of students who dropped out. Students changed their learning behavior by observing the migration of students from cluster to cluster for each semester. Furthermore, there were distinct learning behavior migration patterns for each category of students based on their performance. The migration pattern can suggest to academic stakeholders to understand about students who are likely to drop out, graduate on time or graduate late. These results can be used as recommendations to academic stakeholders for curriculum assessment and development and dropout prevention.

Profile-Based Cluster Evolution Analysis: Identification of Migration Patterns for Understanding Student Learning Behavior