Two tasks of learning analytics: identifying university students at risk of failing and deriving study trajectories leading to success

Many first-year university students do not complete the study plan and drop out. By investigating how students earn ECTS credits we create a model that makes it possible to predict students who are at risk of failure and drop out of the university. Weekly analysis of student data allows us to identi...

Full description

Saved in:
Bibliographic Details
Main Authors: Huptych, Michal, Zdrahal, Zdenek
Format: Conference Proceeding
Language:English
Subjects:
Cybernetics
data processing
knowledge discovery
learning analytics
Optimization
Predictive models
Probability
studying data analyze
Task analysis
Trajectory
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many first-year university students do not complete the study plan and drop out. By investigating how students earn ECTS credits we create a model that makes it possible to predict students who are at risk of failure and drop out of the university. Weekly analysis of student data allows us to identify patterns important for prediction. Early predictions inform students about the potential danger of failure and also allow tutors to intervene. On the other hand, from the data of successful students, it is possible to derive study trajectories leading to the successful completion of the academic year and offer these trajectories to students. The described techniques for student support are demonstrated by examples.
ISSN:2577-1655
DOI:10.1109/SMC53654.2022.9945325