Does Difficulty even Matter? Investigating Difficulty Adjustment and Practice Behavior in an Open-Ended Learning Task

Author:

Schütt Anan1ORCID,Huber Tobias1ORCID,Nasir Jauwairia1ORCID,Conati Cristina2ORCID,André Elisabeth1ORCID

Affiliation:

1. University of Augsburg, Germany

2. University of British Columbia, Canada

Funder

DFG

Publisher

ACM

Reference46 articles.

1. Rakesh Agrawal, Ramakrishnan Srikant, 1994. Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, Vol. 1215. Santiago, Chile, 487–499.

2. Robert Belfer, Ekaterina Kochmar, and Iulian Vlad Serban. 2022. Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits. In International Conference on Artificial Intelligence in Education. Springer, 724–730.

3. Motor learning benefits of self-controlled practice in persons with Parkinson's disease

4. Student Modeling: Supporting Personalized Instruction, from Problem Solving to Exploratory Open Ended Activities

5. Selecting learning tasks: Effects of adaptation and shared control on learning efficiency and task involvement

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