Characterization of Auxiliary Problems for Automated Generation in Error-Based Simulation

Aikawa, Nonoka; Koike, Kento; Tomoto, Takahito; Horiguchi, T.; Hirashima, Tsukasa

doi:10.1007/978-3-030-78361-7_1

Cited by 2 publications

(4 citation statements)

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“…In a previous study, Aikawa et al (2020) conducted a laboratory-scale experiment comparing the proposed system to one with auxiliary problems but no automatic presentation function. As a result, there was a large difference in the effect size between the pre-test and post-test (1.70 for the experimental group and 0.82 for the control group).…”

Section: Test Resultsmentioning

confidence: 99%

“…It is also necessary to examine whether the 30% of learners who could not solve the original problem in the 64-minute system practice time failed to do so because they did not have enough time or because they could not have even if they did. Additionally, the current problem graph was manually generated by focusing on each force individually, but we plan to adopt a method for automatically generating auxiliary problems from original problems (Aikawa et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…The learner's errors are then identified, and simple error-specific auxiliary problems are presented. Aikawa et al (2020) conducted research on providing adaptive auxiliary problems in mechanics EBS. They developed a system that targets complex problems and the associated impasse and presents auxiliary problems to learners who are stuck.…”

Section: Scaffolding In Learning Support Systemsmentioning

confidence: 99%

“…Here, we describe the differences between our system and that developed by Aikawa et al (2020), specifically regarding the interface, problem graph, and transition method. In Aikawa et al's system, the interface was drawn by inputting certain elements, such as the starting point, direction, and size.…”

Section: Comparison With the System Of Aikawa Et Al (2020)mentioning

confidence: 99%

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Classroom implementation of an auxiliary problem presentation system for mechanics adapted to learners’ errors

Aikawa,

Maeda,

Mogi

et al. 2023

RPTEL

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Error-based Simulation (EBS) is a learning support framework that visualizes learners’ errors and encourages trial and error. However, when a learner is stuck, EBS has difficulty in helping them overcome the impasse. Additionally, giving a correct answer to a learner who is stuck may interfere with the trial-and-error activity that EBS is oriented toward. Therefore, it is necessary to encourage learners during trial-and-error activities without giving them correct answers. In this study, we confirm the effectiveness of our system, which is based on conventional mechanics EBS and provides adaptive auxiliary problems based on learners’ errors. Furthermore, we analyze force-based self-overcoming to evaluate our system. Self-overcoming means that the learner can eliminate errors by using the system without the intervention of the teacher. If self-overcoming occurs, the learner can continue trial-and-error with the auxiliary problems, even if they are stuck. To verify the learning effectiveness of such a system, we conducted a classroom implementation with 86 third-year junior high-school learners and analyzed the results. The system logs from the exercises revealed that self-overcoming was taking place, and that it was reflected in the test results.

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Section: Test Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Scaffolding In Learning Support Systemsmentioning

confidence: 99%

Section: Comparison With the System Of Aikawa Et Al (2020)mentioning

confidence: 99%

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