Gaining Insights into Course Difficulty Variations Using Item Response Theory-Reference-Cited by-同舟云学术

Gaining Insights into Course Difficulty Variations Using Item Response Theory

Published:2024-03-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 14th Learning Analytics and Knowledge Conference
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Author:

Baucks Frederik¹^ORCID,Schmucker Robin²^ORCID,Wiskott Laurenz¹^ORCID

Affiliation:

1. Institute of Neural Computation, Ruhr-Universität Bochum, Germany

2. Machine Learning Department, Carnegie Mellon University, USA

Funder

Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3636555.3636902

Reference34 articles.

1. Terry A Ackerman. 1994. Using multidimensional item response theory to understand what items and tests are measuring. Applied measurement in education 7, 4 (1994), 255–278.

2. Measurement and analysis of student engagement in university classes where varying levels of PBL methods of instruction are in use

3. Evaluation of Student Performance through a Multidimensional Finite Mixture IRT Model

4. A classification of university courses based on students’ satisfaction: an application of a two-level mixture item response model

5. Michael Backenköhler and Felix Scherzinger et al.2018. Data-Driven Approach towards a Personalized Curriculum. In Proceedings of the 11th International Conference on Educational Data Mining. International Educational Data Mining Society, Raleigh, NC, 246–251.

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