research-article

Discovering Hidden Course Requirements and Student Competences from Grade Data

Authors:

Mara Houbraken,

Chang Sun,

Evgueni Smirnov,

Kurt DriessensAuthors Info & Claims

UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 147 - 152

https://doi.org/10.1145/3099023.3099034

Published: 09 July 2017 Publication History

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Abstract

This paper presents a data driven approach to autonomous course-competency requirement and student-competency level discovery starting from the grades obtained by a sufficiently large set of students. The approach relies on collaborative filtering techniques, more precisely matrix decomposition, to derive the hidden competency requirements and levels that together should be responsible for observed grades. The discovered hidden features are translated into human understandable competencies by matching the computed values to expert input. The approach also allows for grade prediction for so far unobserved student course combinations, allowing for personalized study planning and student guidance. The technique is demonstrated on data from a "Data Science and Knowledge Engineering" Bachelor study, Maastricht University.

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Cited By

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Rahul Katarya R(2024)A Systematic Review on Predicting the Performance of Students in Higher Education in Offline Mode Using Machine Learning TechniquesWireless Personal Communications10.1007/s11277-023-10838-x133:3(1643-1674)Online publication date: 26-Jan-2024
https://doi.org/10.1007/s11277-023-10838-x
Kaur ABhatia M(2023)A Survey of Machine Learning for Assessing and Estimating Student PerformanceProceedings of International Conference on Recent Innovations in Computing10.1007/978-981-19-9876-8_48(633-648)Online publication date: 3-May-2023
https://doi.org/10.1007/978-981-19-9876-8_48
Yu RPardos ZChau HBrusilovsky P(2021)Orienting Students to Course Recommendations Using Three Types of ExplanationAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3464483(238-245)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450614.3464483
Show More Cited By

Index Terms

Discovering Hidden Course Requirements and Student Competences from Grade Data
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
      2. Learning latent representations

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Domain-Aware Grade Prediction and Top-n Course Recommendation
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Automated course recommendation can help deliver personalized and effective college advising and degree planning. Nearest neighbor and matrix factorization based collaborative filtering approaches have been applied to student-course grade data to help ...
The effects of a computer mini-course in test-taking skills on student achievement in general mathematics
Effects of computer technology on student outcomes for low-performing ninth-grade students

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Information

Published In

UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

456 pages

ISBN:9781450350679

DOI:10.1145/3099023

Editors:
Marko Tkalcic
Free University of Bolzano
,
Dhaval Thakker
University of Leeds
,
Panagiotis Germanakos
SAP SE & University of Cyprus
,
Kalina Yacef
University of Sydney
,
Cecile Paris
CSIRO ICT Centre
,
Olga Santos
Spanish National University for Distance Education
,
General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Michel Desmarais
Polytechnique University, Montreal, Canada
,
Federica Cena
University of Torino, Italy

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2017

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Worldwide Universities Network

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UMAP '17

Sponsor:

UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

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Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

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Cited By

View all

Rahul Katarya R(2024)A Systematic Review on Predicting the Performance of Students in Higher Education in Offline Mode Using Machine Learning TechniquesWireless Personal Communications10.1007/s11277-023-10838-x133:3(1643-1674)Online publication date: 26-Jan-2024
https://doi.org/10.1007/s11277-023-10838-x
Kaur ABhatia M(2023)A Survey of Machine Learning for Assessing and Estimating Student PerformanceProceedings of International Conference on Recent Innovations in Computing10.1007/978-981-19-9876-8_48(633-648)Online publication date: 3-May-2023
https://doi.org/10.1007/978-981-19-9876-8_48
Yu RPardos ZChau HBrusilovsky P(2021)Orienting Students to Course Recommendations Using Three Types of ExplanationAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3464483(238-245)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450614.3464483
Rastrollo-Guerrero JGómez-Pulido JDurán-Domínguez A(2020)Analyzing and Predicting Students’ Performance by Means of Machine Learning: A ReviewApplied Sciences10.3390/app1003104210:3(1042)Online publication date: 4-Feb-2020
https://doi.org/10.3390/app10031042
Morsomme RSmirnov E(2020)Valid Prediction Intervals for Course Grades with Conformal Prediction2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA51294.2020.00152(936-941)Online publication date: Dec-2020
https://doi.org/10.1109/ICMLA51294.2020.00152

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Index Terms

Recommendations

Domain-Aware Grade Prediction and Top-n Course Recommendation

The effects of a computer mini-course in test-taking skills on student achievement in general mathematics

Effects of computer technology on student outcomes for low-performing ninth-grade students