• Combining Domain Modelling and...

Treffer: Combining Domain Modelling and Student Modelling Techniques in a Single Automated Pipeline

Title:
Combining Domain Modelling and Student Modelling Techniques in a Single Automated Pipeline
Language:
English
Authors:
Picones, Gio, PaaBen, Benjamin, Koprinska, Irena, Yacef, Kalina
Source:
International Educational Data Mining Society. 2022.
Availability:
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Peer Reviewed:
Y
Page Count:
11
Publication Date:
2022
Document Type:
Konferenz Speeches/Meeting Papers<br />Reports - Research
Education Level:
High Schools
Secondary Education
Descriptors:
Prediction, Academic Achievement, Learning Analytics, Concept Mapping, Mastery Learning, Scores, Programming Languages, Computer Science Education, Accuracy, Least Squares Statistics, Evaluation Methods, High School Students, Models, Computer Software
Entry Date:
2022
Accession Number:
ED624081
Database:
ERIC

Weitere Informationen

In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a concept. We conducted an evaluation using six large datasets from a Python programming course, evaluating the performance of different domain and student modelling techniques. The results showed that it is possible to develop a successful and fully automated pipeline which learns from raw data. The best results were achieved using alternating least squares on hill-climbing Q-matrices as domain modelling and exponential moving average as student modelling. This method outperformed all baselines in terms of accuracy and showed excellent run time. [For the full proceedings, see ED623995.]

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