Survey Data on AI Adaptive Learning and Gamification for Pre-Service Mathematics Teachers

East Java, Indonesia, 110 pre-service mathematics teachers participated in a quantitative study comparing AI-based adaptive learning and gamification strategies. Vera Septi Andrini collected data via Likert-scale questionnaires and open-ended cognitive tasks, analyzed using PLS-SEM with 5,000 bootstrapping resamples. The dataset, last updated in 2026, explains 45.61% of variance in mathematical reasoning.

Use Cases

• Compare the effectiveness of AI-based adaptive learning versus gamification strategies based on Likert-scale survey data.
• Analyze the relationship between instructional approaches and mathematical reasoning scores based on rubric-scored cognitive tasks.
• Validate structural equation models for educational research based on PLS-SEM analysis results.
• Study the development of mathematical reasoning among pre-service teachers based on indicator scores for justification and reflection.

Strengths

• Includes data from 110 participants, providing a sample size for statistical analysis.
• Assessment uses a validated analytical rubric with a maximum total score of 24 for mathematical reasoning.
• Model validation includes discriminant validity (HTMT ratio 0.6822) and predictive accuracy metrics (Q²=0.259).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect geographic bias inherent to a single private university in East Java.

Provenance

Source

Vera Septi Andrini via figshare.

Collection Method

Data collected via a five-point Likert scale questionnaire and open-ended cognitive task questions.

Freshness

Last updated 2026-05-22 10:55:03; freshness should be verified.

Geography

East Java, Indonesia.