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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Computech & Bisnis (e-Journal) Information System for Educators and Professionals : Journal of Information System Informatics for Educators and Professional : Journal of Informatics CCIT (Creative Communication and Innovative Technology) Journal Jurnal Ilmiah Edutic : Pendidikan dan Informatika Information Technology Education Journal Jurnal Guru Komputer Journal of Computer Science and Information Technology Journal of Computers for Society
Erlangga, Erlangga
Universitas Pendidikan Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Library & Information Science Mathematics Social Sciences Other

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MOBILE LEARNING ANALYSIS IN COMPUTER SCIENCE: A SYSTEMATIC REVIEW OF THE IMPACT AND FACTORS ON STUDENT LEARNING OUTCOMES Erlangga Erlangga; Alya Nurfaridah; Erna Piantari; Muhamad Dzikri Gumilang; Muhammad Riza Abqari; Raka Ibnu Firdaus
Journal of Computer Science and Information Technology Vol. 3 No. 1 (2025): Desember
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v3i1.3472

Abstract

Limited access and flexibility in higher education, particularly in the field of Computer Science which demands analytical and technical skills, necessitate a more adaptive learning approach. This study aims to examine the influence of M-Learning on student learning outcomes and identify critical success factors (CSFs) that affect its implementation. The method used is a Systematic Literature Review (SLR) through the planning, implementation, and reporting stages, with a publication range of 2020–2025. Analysis of ten studies showed that M-Learning significantly improved learning outcomes, motivation, and independence, especially for programming-based and applied science materials. This effectiveness is influenced by the quality of the pedagogical design, active learning approaches, and adequate technological support. This finding confirms that M-Learning is a relevant and effective learning strategy, with its success heavily dependent on pedagogical factors and infrastructure readiness.
Generative Artificial Intelligence Use, AI Self-Efficacy, and Higher Order Thinking Skills Among Education Students in Indonesia Erlangga Erlangga; Dedi Rohendi; Eki Nugraha; M. Miftach Fakhri
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12693

Abstract

Purpose – This study examined the association between generative artificial intelligence (GenAI) use and Higher Order Thinking Skills (HOTS) among education students. It also investigated whether students' confidence in using AI, operationalized as AI self-efficacy,  significantly predicts the probability of high HOTS attainment. Design/methods/approach – A quantitative explanatory research design was employed. Data were collected through a five-point Likert-scale questionnaire administered to education students at a single Indonesian university. Binary logistic regression was applied to model the probability of high HOTS attainment, with HOTS dichotomized at the median composite score into two categories: high HOTS (coded 1) and low HOTS (coded 0), using AI use frequency (X1) and AI self-efficacy (X2) as predictors. Findings – The binary logistic regression model (logit(π) = −0.741 − 0.134X1 + 1.891X2) reveals that AI use frequency (X1) is not a statistically significant predictor of high HOTS attainment (Wald = 0.073, p = 0.787). In contrast, AI self-efficacy (X2) emerges as the sole significant predictor, with students reporting higher AI self-efficacy showing significantly greater odds of being classified in the high-HOTS category (OR = 6.623, p = 0.001). With an overall classification accuracy of 73.3% and a balanced accuracy of 61.6%, the Hosmer-Lemeshow goodness-of-fit test confirms adequate model fit, though the model's limited specificity for low-HOTS cases reflects a class imbalance in the sample. Research implications/limitations – The findings suggest that higher education institutions should focus on developing students' AI self-efficacy rather than merely expanding access to GenAI tools, since self-efficacy not access alone is the factor associated with higher-order thinking. Lecturers are encouraged to design learning activities that promote critical and reflective engagement with AI rather than passive use. However, reliance on self-report data introduces the risk of perceptual bias, as students' self-assessed HOTS may not fully reflect actual cognitive performance. Further, because the design is cross-sectional, the findings cannot be used to infer long-term causal relationships. Special attention should also be paid to the generalization of the results as it is narrowed to a limited number of students of education at a single institution. Originality/value – This study is original in its application of binary logistic regression to probabilistically model high HOTS attainment in the Indonesian AI-education context, an approach that has not been widely adopted in prior research in this setting to predict HOTS on the scenario of AI-based higher education in Indonesia.
Web-Based Computational Thinking Assessment Using K-Nearest Neighbor and Rule-Based Adaptive Treatment Muhammad Izzuddin Al Fatih; Erlangga Erlangga; Yuli Sopianti
EDUTIC Vol 13, No 2: 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v13i2.35292

Abstract

Conventional database-learning assessments generally provide aggregate scores without showing which computational thinking components require further support. This study developed a web-based adaptive assessment system for vocational high school students by integrating K-nearest neighbor classification, adaptive hints, and rule-based treatment. The research applied a development approach using the Smart Learning Environment Establishment Guideline through analysis, design, development, implementation, and evaluation stages. The KNN model was developed using 64 empirical records from students in XI RPL 2 and XI RPL 3. Four weighted computational thinking indicators—decomposition, pattern recognition, abstraction, and algorithmic thinking—were used as classification features. KNN, RandomOverSampler–KNN, and Synthetic Minority Over-sampling Technique (SMOTE)–KNN pipelines were evaluated using group-aware cross-validation. The selected SMOTE–KNN model used distance weighting with k = 9 and achieved 92.19% accuracy, 88.59% balanced accuracy, and 89.75% macro F1-score. The classification result adjusted adaptive hints, while explicit rules controlled indicator priority, difficulty progression, mastery points, and treatment transitions. All 24 black-box scenarios met the expected requirements. The novelty and main contribution of this study lie in integrating indicator-level CT diagnosis, data-driven classification, and transparent rule-based adaptation within a single web-based workflow for vocational database learning.
Co-Authors Alya Nurfaridah Dedi Rohendi Dela Adelia Eki Nugraha Enjun Junaeti Erna Piantari Herbert Siregar M. Miftach Fakhri Miftahul Fadli Muttaqin Muhamad Dzikri Gumilang Muhammad Izzuddin Al Fatih Muhammad Riza Abqari Muhammad Zahid Tsaqif Mukhlis, Rizki Raka Ibnu Firdaus Rijayanti, Rita Sarifah Putri Raflesia, Sarifah Putri Sidiq Nugraha Somantri, Asep Wahyudin Sanusi Wahyudin Wahyudin Wawan Setiawan Wihardi, Yaya Winda Mauli Kristy Yuli Sopianti