five

Supporting data for "Designing a 5E flipped teaching approach to enhance elementary schoolers' computational thinking concepts, problem-solving and debugging skills"

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DataCite Commons2022-04-14 更新2025-04-16 收录
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https://datahub.hku.hk/articles/dataset/Supporting_data_for_Designing_a_5E_flipped_teaching_approach_to_enhance_elementary_schoolers_computational_thinking_concepts_problem-solving_and_debugging_skills_/19481603/1
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Study One used a mixed-methods research approach to investigate student performance under different teaching approaches and their perceptions of the 5E-supported flipped classroom approach. Both quantitative and qualitative data were collected and analyzed. The quantitative data (e.g., test scores, project evaluation) can be used to indicate the student differences in learning outcomes between two instructional approaches. The qualitative data (e.g., student interviews, the instructor interview) can be used to explain the results obtained based on the quantitative data by identifying cases that describe the experiences of participants. Similar to the approach adopted in Study One, a mixed-methods research approach was also used in Study Two to examine student performance in program debugging under two different types of instructional approaches and students’ perceptions of the program debugging and the scaffolding tool that shows the systematic debugging process. This means that both quantitative and qualitative data needed to be collected and analyzed. The student performance differences in program debugging and cognitive load between the two flipped debugging training groups (i.e., the flipped debugging training approach combined with the systematic debugging process and the modeling method vs. the unassisted flipped debugging training approach) can be indicated through quantitative data (i.e., test scores in program debugging tasks and rating scores in the cognitive load questionnaire). The cases that described students’ experiences can be obtained through qualitative data (e.g., student interviews).
提供机构:
HKU Data Repository
创建时间:
2022-04-14
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