Dataset on Programming Competencies Development Using Scratch and a Recommender System in a Non-WEIRD Primary School Context
Abstract
1. Introduction
2. Data Description
2.1. Participant Overview
2.2. Dataset Content
- Logical Reasoning Test Results: Baseline logical reasoning skills assessed through a custom-designed multiple choice test.
- Computational Thinking Test (CTT V2) Scores: Pre- and post-intervention assessments evaluating students’ computational thinking abilities using a validated instrument [21].
- Scratch Activity Logs: Sequential records of programming exercises completed by students using Scratch.
- CARAMBA Recommendation Histories: Data on personalized exercise suggestions generated through collaborative filtering algorithms implemented in CARAMBA [20].
- Tutor Survey Responses: Reflections and qualitative feedback from students in Systems Engineering at the university who supported the intervention. For example, one tutor noted: “Students who struggled initially became more confident when tasks were personalized.” Another reported: “CARAMBA helped identify the ideal starting point for each student, which improved motivation and autonomy.”
2.3. Availability of Data and Tools
- GitHub Repository for study instruments and datasets: https://github.com/cvidalmsu/UNEMI_1 (accessed on 10 May 2025)
- Institutional Repository for the CARAMBA recommendation system: https://github.com/nvalerod/carambaNew (accessed on 20 May 2025)
2.4. Ethical Considerations
3. Methods
3.1. Educational Intervention Design
- Phase I: Introduction to Scratch and Basic Computing ConceptsStudents participated in instructor-led sessions focused on basic computing skills, file management, and fundamental programming concepts using the Scratch platform [14]. Core computational concepts such as sequencing, conditionals, loops, and event handling were progressively introduced.
- Phase II: Personalized Learning with CARAMBAAfter completing foundational training, students engaged with the CARAMBA recommendation system, which provided personalized exercise pathways based on collaborative filtering techniques [20]. This adaptive phase aimed to foster autonomy and tailor difficulty progression to individual student performance.
3.2. Data Collection Procedures
- Logical Reasoning Diagnostic Test: At the beginning of the study, the students completed a custom-designed multiple choice test assessing the skills of logical, mathematical, and abstract reasoning.
- Pre-Intervention Computational Thinking Test (CTT V2): To establish baseline computational thinking skills, students were administered the validated CTT V2 instrument [21].
- Activity Logs: Throughout the instructional and personalized phases, activity logs were recorded from students’ interactions with Scratch and CARAMBA.
- Post-Intervention Computational Thinking Test (CTT V2): After the intervention, the CTT V2 was reapplied to measure learning gains.
- Tutor Reflection Surveys: Systems Engineering tutors provided qualitative feedback through surveys assessing instructional dynamics and technological support effectiveness.
3.3. Statistical Validation
- The pre-test and post-test CTT V2 scores were analyzed for normality using the Shapiro–Wilk test.
- Paired t-tests were used for normally distributed data and Wilcoxon signed rank tests for nonparametric distributions.
- The mean pre-test score was 45.2, and the post-test score was 67.8. There was a statistically significant difference () between the the pre-test and post-test scores.
- The effect size, calculated using Cohen’s d, was 1.38, indicating a large practical effect.
- Rank-biserial correlation was also computed where applicable.
3.4. Educational and Data Collection Workflow
3.5. CTT Score Distribution Analysis
4. User Notes
4.1. Potential Applications of the Dataset
- Educational Data Mining and Learning Analytics: Activity logs from Scratch and CARAMBA provide rich sequential data ideal for mining patterns, predicting learning trajectories, and evaluating the impact of personalized recommendations [17].
- Development of Adaptive Learning Systems: The recommendation interaction histories can be used to train and validate new models for adaptive learning environments, particularly in early programming education settings [22].
- Design of Inclusive Educational Curricula: Insights from the intervention can inform the creation of more inclusive curricula that incorporate personalization strategies and accessible technologies like Scratch [14].
4.2. Reuse Guidelines
- Proper Citation: Any publication or derived work should acknowledge this study as the original source of the dataset.
- Ethical Use: Although the dataset is anonymized, users must avoid any attempt to re-identify participants and must ensure compliance with ethical standards.
- Contextualization: Any interpretation of the results should take into account the educational, socioeconomic, and technological characteristics of the Ecuadorian primary school setting from which the data originate.
5. Conclusions
- Conducting longitudinal studies to assess the sustainability of computational thinking skills over time.
- Exploring enhancements to the recommendation system, such as the integration of reinforcement learning approaches.
- Applying the intervention model to different socioeconomic contexts, educational levels, or countries for comparative analysis.
- Investigating the impact of personalization strategies on students with disabilities or diverse learning needs.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Category | Number of Students | Percentage (%) |
---|---|---|
Males | 236 | 55.1 |
Females | 192 | 44.9 |
Students with disabilities | 32 | 7.5 |
Ecuadorian nationality | 419 | 97.9 |
Foreign nationality | 9 | 2.1 |
File Name | Format | Description |
---|---|---|
diagnostic_test.csv | CSV | Results from logical reasoning diagnostic test |
ctt_pre_post.csv | CSV | Pre- and post-CTT V2 computational thinking assessments |
tutor_survey.csv | CSV | Reflections from university student tutors |
caramba_logs.json | JSON | Personalized recommendation history and activity tracking |
metadata.pdf | Complete description of variables and codebook |
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Share and Cite
Cárdenas-Cobo, J.; Vidal-Silva, C.; Máquez, N. Dataset on Programming Competencies Development Using Scratch and a Recommender System in a Non-WEIRD Primary School Context. Data 2025, 10, 86. https://doi.org/10.3390/data10060086
Cárdenas-Cobo J, Vidal-Silva C, Máquez N. Dataset on Programming Competencies Development Using Scratch and a Recommender System in a Non-WEIRD Primary School Context. Data. 2025; 10(6):86. https://doi.org/10.3390/data10060086
Chicago/Turabian StyleCárdenas-Cobo, Jesennia, Cristian Vidal-Silva, and Nicolás Máquez. 2025. "Dataset on Programming Competencies Development Using Scratch and a Recommender System in a Non-WEIRD Primary School Context" Data 10, no. 6: 86. https://doi.org/10.3390/data10060086
APA StyleCárdenas-Cobo, J., Vidal-Silva, C., & Máquez, N. (2025). Dataset on Programming Competencies Development Using Scratch and a Recommender System in a Non-WEIRD Primary School Context. Data, 10(6), 86. https://doi.org/10.3390/data10060086