DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Kabdo | ko |
dc.contributor.author | Shin, Hyungyu | ko |
dc.contributor.author | Xia, Meng | ko |
dc.contributor.author | Kim, Juho | ko |
dc.date.accessioned | 2022-09-30T07:00:16Z | - |
dc.date.available | 2022-09-30T07:00:16Z | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.issued | 2022-05-04 | - |
dc.identifier.citation | 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 | - |
dc.identifier.uri | http://hdl.handle.net/10203/298795 | - |
dc.description.abstract | Designing solution plans before writing code is critical for successful algorithmic problem-solving. Novices, however, often plan on-the-fly during implementation, resulting in unsuccessful problem-solving due to lack of mental organization of the solution. Research shows that subgoal learning helps learners develop more complete solution plans by enhancing their understanding of the high-level solution structure. However, expert-created materials such as subgoal labels are necessary to provide learning benefits from subgoal learning, which are a scarce resource in self-learning due to limited availability and high cost. We propose a learnersourcing workflow that collects high-quality subgoal labels from learners by helping them improve their label quality. We implemented the workflow into AlgoSolve, a prototype interface that supports subgoal learning for algorithmic problems. A between-subjects study with 63 problem-solving novices revealed that AlgoSolve helped learners create higher-quality labels and more complete solution plans, compared to a baseline method known to be effective in subgoal learning. © 2022 ACM. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | AlgoSolve: Supporting Subgoal Learning in Algorithmic Problem-Solving with Learnersourced Microtasks | - |
dc.type | Conference | - |
dc.identifier.wosid | 000890212501047 | - |
dc.identifier.scopusid | 2-s2.0-85130557345 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1145/3491102.3501917 | - |
dc.contributor.localauthor | Kim, Juho | - |
dc.contributor.nonIdAuthor | Choi, Kabdo | - |
dc.contributor.nonIdAuthor | Xia, Meng | - |
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