Influence of Grouping Strategies for Cooperative Learning on the Learning Outcome of Programming Course

Hsu, Jih-Shih; Tsai, Wen-Lung

doi:10.1145/3572549.3572612

Cited by 1 publication

(2 citation statements)

References 8 publications

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“…For example, Boroujeni and Dillenbourg (2019) provided methods that can be applied during the course to detect changes in behavioral patterns while the course is running. On the other hand, our previous study (Hsu et al, 2023b) investigated learning one week ahead of regular math tests over the year using clustering analysis to extract clusters of learning patterns that suggest different types of learning habits. Therefore, it is potential to extend our work with existing methods to clarify learners' decisions between the types of learning habits.…”

Section: Discussionmentioning

confidence: 99%

“…For example, they calculated the entropy of the histogram of the learners' activities over time to identify whether their activities were concentrated around a particular hour of the day (P1) or a particular day of the week (P2). In contrast to calculating a value, our previous studies (Hsu et al, 2023a;Hsu et al, 2023b) used clustering analysis and presented the patterns by the clusters such as the learners who studied on weekday mornings (P1 and P2) or the learners who studied consistently throughout exam preparation (P6).…”

Section: Types Of Habitsmentioning

confidence: 99%

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Designing data-informed support for building learning habits in the Japanese K12 context

Hsu,

Horikoshi,

et al. 2024

RPTEL

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While building proper learning habits has been said to enhance academic performance, it is challenging to give long-term support for building habits in educational contexts due to the lack of continuous tracing of one’s habitual behaviors. With the accumulation of learning logs and the advancement of Learning Analytics (LA) techniques, this paper illustrates the data-informed support for building learning habits, which involves persuading one to change behaviors. Specifically, we tackled 2 research objectives following the Persuasive Systems Design (PSD) model. First, we defined indicators of learning habits from log data and analyzed 115,340 learning logs of 96 learners from a Japanese junior high school. As a result, the learners’ types of habits could be detected even though some might not be efficient and the stages of habits fluctuated over time. We also identified differences when comparing the learning habits extracted from the log data with those reported in the questionnaire. Second, based on the understanding of the learner profiles, we designed elements of an LA dashboard to support habit-building by applying the design principles from the PSD model. Overall, the learners recognized the feasibility of integrating data-informed support into their daily learning. Therefore, we look forward to the evidence of its effectiveness on the behavior change that can be depicted by the transition between stages of different types of learning habits.

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Section: Discussionmentioning

confidence: 99%

Section: Types Of Habitsmentioning

confidence: 99%