Improving Code Comprehension Through Scaffolded Self-explanations
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| Názov: | Improving Code Comprehension Through Scaffolded Self-explanations |
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| Autori: | Oli, Priti, Banjade, Rabin, Lekshmi Narayanan, Arun Balajiee, Chapagain, Jeevan, Tamang, Lasang Jimba, Brusilovsky, Peter, Rus, Vasile |
| Zdroj: | Faculty Publications |
| Informácie o vydavateľovi: | University of Memphis Digital Commons |
| Rok vydania: | 2023 |
| Predmety: | Computer Science Education, Intelligent Tutoring System, Java Programming, Program Comprehension, Scaffolding, Computer Sciences |
| Popis: | Self-explanations could increase student’s comprehension in complex domains; however, it works most efficiently with a human tutor who could provide corrections and scaffolding. In this paper, we present our attempt to scale up the use of self-explanations in learning programming by delegating assessment and scaffolding of explanations to an intelligent tutor. To assess our approach, we performed a randomized control trial experiment that measured the impact of automatic assessment and scaffolding of self-explanations on code comprehension and learning. The study results indicate that low-prior knowledge students in the experimental condition learn more compared to high-prior knowledge in the same condition but such difference is not observed in a similar grouping of students based on prior knowledge in the control condition. |
| Druh dokumentu: | text |
| Jazyk: | unknown |
| Relation: | https://digitalcommons.memphis.edu/facpubs/20165 |
| DOI: | 10.1007/978-3-031-36336-8_74 |
| Dostupnosť: | https://digitalcommons.memphis.edu/facpubs/20165 https://doi.org/10.1007/978-3-031-36336-8_74 |
| Prístupové číslo: | edsbas.4D561904 |
| Databáza: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | Self-explanations could increase student’s comprehension in complex domains; however, it works most efficiently with a human tutor who could provide corrections and scaffolding. In this paper, we present our attempt to scale up the use of self-explanations in learning programming by delegating assessment and scaffolding of explanations to an intelligent tutor. To assess our approach, we performed a randomized control trial experiment that measured the impact of automatic assessment and scaffolding of self-explanations on code comprehension and learning. The study results indicate that low-prior knowledge students in the experimental condition learn more compared to high-prior knowledge in the same condition but such difference is not observed in a similar grouping of students based on prior knowledge in the control condition. |
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| DOI: | 10.1007/978-3-031-36336-8_74 |