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Evaluating ICT literacy: Physics ICT test based on Scratch Programming for high school students Rusilowati, Ani; Negoro, Ridho Adi; Subali, Bambang; Aji, Mahardika Prasetya
REID (Research and Evaluation in Education) Vol. 8 No. 2 (2022)
Publisher : Graduate School of Universitas Negeri Yogyakarta & Himpunan Evaluasi Pendidikan Indonesia (HEPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/reid.v8i2.49093

Abstract

The integration of various learning content is important in the education process to develop the multi-competences of graduates. This is a response to the Industrial Revolution 4.0. ICT helps graduates compete and master the competencies needed in various scientific fields, especially physics. Efforts to build ICT literacy in learning Physics must complete an evaluation tool containing comprehensive Physics content. The ICT literacy test instrument as an evaluation tool can be packaged with scratch programming activities to help uncover ICT literacy based on computational physics concepts. This study aims to develop an ICT literacy test instrument with physics content based on Scratch programming for middle-level students. This research is development research with a research stage adapted from the development stage by Borg and Gall, including (1) Potential and problem analysis; (2) Data collection; (3) Product design; (4) Design validation and design revision; (5) Product trial; (6) Analysis and reporting. The test subjects were 106 students. The Physics ICT questions were developed to consist of 20 questions. Based on the validity test, a total percentage of 85.33% is obtained with proper criteria. The reliability of the test is shown by the results of calculations with Alpha Cron of 0.716 high categories. Overall, the PICT test is feasible to be used as an ICT literacy test for Physics content.
Technology-enhanced learning for statistical graph interpretation: An item response theory analysis of learning outcomes Subali, Bambang; Negoro, Ridho Adi; Ellianawati; Dwijananti, Pratiwi; Anandita, Aulia Silvina; Setyaningsih, Natalia Erna; Siswanto
REID (Research and Evaluation in Education) Vol. 11 No. 2 (2025)
Publisher : Graduate School of Universitas Negeri Yogyakarta & Himpunan Evaluasi Pendidikan Indonesia (HEPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/reid.v11i2.89666

Abstract

This study aimed to examine the effectiveness of technology-enhanced learning (TEL) in improving students’ statistical graph interpretation skills through a rigorous Item Response Theory (IRT) analysis. Employing a quasi-experimental pretest–posttest control group design, the research involved 120 undergraduate students from four classes, equally divided into experimental and control groups. The experimental groups received TEL-based instruction featuring interactive graph visualizations and automated feedback, while the control groups followed conventional lectures and exercises over seven sessions. Data were collected using a 60-item multiple-choice test covering bar charts, histograms, boxplots, and scatterplots, which was content-validated by experts and trialed for clarity, yielding high reliability (Cronbach’s α = 0.833). Construct validity was ensured through unidimensionality and invariance testing, confirmed by eigenvalue and DETECT analysis. Data analysis applied IRT to calibrate item parameters discrimination (a), difficulty (b), and guessing (c) and to estimate students’ latent abilities (θ). Model comparison identified the 3PL model as the best fit, capturing both difficulty variation and guessing behavior. Calibration results showed that most items exhibited satisfactory psychometric quality, supporting the robustness of the instrument. Findings revealed that TEL groups achieved a nearly one-logit gain in ability from pretest to posttest, significantly higher than the minimal improvement observed in the control groups, as confirmed by independent t-tests and normalized gain analysis. These results indicate that TEL substantially strengthens students’ ability to interpret statistical graphs while demonstrating the diagnostic value of IRT in evaluating both item quality and learning effectiveness.