Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps
Saved in:
| Title: | Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps |
|---|---|
| Language: | English |
| Authors: | Meng Xia (ORCID |
| Source: | Grantee Submission. 2025Paper presented at the Annual Meeting of the European Conference on Technology Enhanced Learning (EC-TEL 2025) (20th, Newcastle and Durham, UK, Sep 2025). |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| Publication Date: | 2025 |
| Sponsoring Agency: | Institute of Education Sciences (ED) |
| Contract Number: | R305A220386 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Junior High Schools Middle Schools Secondary Education Elementary Education Grade 6 Intermediate Grades Grade 7 Grade 8 |
| Descriptors: | Mastery Learning, Skill Development, Intelligent Tutoring Systems, Technology Uses in Education, Learning Processes, Equations (Mathematics), Problem Solving, Mathematics Instruction, Middle School Students, Grade 6, Grade 7, Grade 8, Mathematics Skills, Drills (Practice), Middle School Mathematics |
| DOI: | 10.1007/978-3-032-03870-8_37 |
| Abstract: | Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundamental challenge for tutoring systems. Previous research has reduced overpractice through the development of better problem selection algorithms and the authoring of focused practice tasks. However, few efforts have concentrated on reducing overpractice through step-level adaptivity, which can avoid resource-intensive curriculum redesign. We propose and evaluate Fast-Forwarding as a technique that enhances existing problem selection algorithms. Based on simulation studies informed by learner models and problem-solving pathways derived from real student data, Fast-Forwarding can reduce overpractice by up to one-third, as it does not require students to complete problem-solving steps if all remaining pathways are fully mastered. Fast-Forwarding is a flexible method that enhances any problem selection algorithm, though its effectiveness is highest for algorithms that preferentially select difficult problems. Therefore, our findings suggest that while Fast-Forwarding may improve student practice efficiency, the size of its practical impact may also depend on students' ability to stay motivated and engaged at higher levels of difficulty. [This paper was published in: "Two Decades of TEL. From Lessons Learnt to Challenges Ahead. EC-TEL 2025. Lecture Notes in Computer Science, vol 16063," edited by K. Tammets et al., Springer, 2026.] |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2025 |
| Accession Number: | ED676615 |
| Database: | ERIC |
| Abstract: | Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundamental challenge for tutoring systems. Previous research has reduced overpractice through the development of better problem selection algorithms and the authoring of focused practice tasks. However, few efforts have concentrated on reducing overpractice through step-level adaptivity, which can avoid resource-intensive curriculum redesign. We propose and evaluate Fast-Forwarding as a technique that enhances existing problem selection algorithms. Based on simulation studies informed by learner models and problem-solving pathways derived from real student data, Fast-Forwarding can reduce overpractice by up to one-third, as it does not require students to complete problem-solving steps if all remaining pathways are fully mastered. Fast-Forwarding is a flexible method that enhances any problem selection algorithm, though its effectiveness is highest for algorithms that preferentially select difficult problems. Therefore, our findings suggest that while Fast-Forwarding may improve student practice efficiency, the size of its practical impact may also depend on students' ability to stay motivated and engaged at higher levels of difficulty. [This paper was published in: "Two Decades of TEL. From Lessons Learnt to Challenges Ahead. EC-TEL 2025. Lecture Notes in Computer Science, vol 16063," edited by K. Tammets et al., Springer, 2026.] |
|---|---|
| DOI: | 10.1007/978-3-032-03870-8_37 |
