Handball Skill and Attitude Data from AI Feedback Study

A 15-session instructional program involving 56 university students with no prior handball training compared ChatGPT-based feedback to peer feedback. The dataset includes pre-test and post-test scores for seven fundamental handball skills and student attitude scales across cognitive, affective, and behavioral dimensions.

Use Cases

• Analyze pre-test and post-test scores for seven fundamental handball skills to measure skill acquisition differences between feedback groups.
• Model student attitude scale scores across cognitive, affective, and behavioral dimensions as a function of feedback type.
• Compare effect sizes (partial η²) across the seven fundamental handball skills between experimental and control conditions.

Strengths

• Data from a randomized pre-test–post-test control group design with 56 participants.
• Performance assessed using expert-validated checklists for seven fundamental handball skills.
• Results showed statistically significant differences with medium to very large effect sizes (partial η² = .33–.87).

Limitations

• Small sample size of 56 undergraduate students limits generalizability.
• Data scope is limited to a single university handball course and one 15-session program.
• Dataset is 23.4 KB, indicating it contains summary or processed results, not raw observation-level data.

Provenance

Source

Ahmed Hassan Rakha

Collection Method

Data collected via standardized physical fitness tests, expert-validated performance checklists, and a students' attitudes scale during a structured instructional program.

Time Range

One academic semester.

Freshness

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Geography

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