Dataset for Measuring Metacognitive Skills and Self-Efficacy in Statistics and Data Science Learning
Description
This dataset contains responses from 142 Police Science College students participating in a Statistics and Data Science course in Indonesia. The data include measurements of metacognitive skills (25 multiple-choice items) and self-efficacy (25 Likert-scale items). The dataset was collected for psychometric evaluation using the Rasch Model and can be used for instrument validation, educational assessment, metacognition research, self-efficacy studies, and replication of the associated publication.
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Steps to reproduce
Step to Produce the Dataset 1. Instrument Development A research instrument was developed to measure students' metacognitive skills and self-efficacy in Statistics and Data Science courses. The instrument consisted of: 25 multiple-choice items for metacognitive skills. 25 Likert-scale items for self-efficacy. The metacognitive instrument covered three dimensions: knowledge, experience, and regulation. 2. Content Validation The instrument blueprint was developed based on metacognitive theory (Flavell, 1979; Schraw & Dennison, 1994) and self-efficacy theory (Bandura, 1997). Items were reviewed and refined before administration. 3. Data Collection Data were collected from students enrolled in the Statistics and Data Science course at a Police Science College in Jakarta, Indonesia, during the 2024–2025 academic year. The questionnaire was administered online using Google Forms during the final instructional meetings after students had completed all course materials. A total of 142 valid responses were obtained. 4. Data Preparation Google Forms responses were exported to Microsoft Excel. The dataset was cleaned by: Checking missing values. Verifying response completeness. Coding multiple-choice responses as dichotomous variables (1 = correct, 0 = incorrect). Coding self-efficacy responses using a Likert scale. 5. Rasch Analysis The cleaned dataset was analyzed using: Winsteps Jamovi ConQuest The analysis evaluated: Item reliability Person reliability Item fit statistics Unidimensionality Local independence Parameter invariance according to Rasch model assumptions. 6. Dataset Organization The final dataset was organized into spreadsheets containing: Respondent identification codes Metacognitive item responses Self-efficacy item responses Scoring keys Variable descriptions The dataset was then prepared for public sharing and replication purposes. 7. Data Availability The dataset supports the publication: Earlyanti, N. I., & Sandy, A. (2025). Analysis of Measurement of Metacognitive Skills and Students' Self-Efficacy Using the Rasch Model. Journal of Education and e-Learning Research, 12(4), 636–647.