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All Journal Daengku: Journal of Humanities and Social Sciences Innovation Jurnal Pendidikan Terapan
Addy Septyawan
Universitas Sebelas Maret
Humanities Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Predicting Student Academic Success Using Machine Learning Models: A Learning Analytics Approach in Higher Education Arief Hidayat; Swasti Maharani; Dendi Pratama; Ramadiani Ramadiani; S Sujito; Addy Septyawan; Dian Wardiana Sjuchro
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4881

Abstract

Rapid deployment of digital learning technologies in the higher education sector has created immense amounts of educational data that could be leveraged to enhance student success and institutional effectiveness. Nevertheless, student dropout, poor academic performance, and lack of retention continue to plague universities across the world. In most cases, identification of academically struggling students is often late since existing models are largely reactive. Therefore, there is need for development of advanced learning analytics models that are able to forecast student performance in higher education institutions. The current study seeks to create an artificial neural network (ANN)-based learning analytics framework to predict student success in higher education institutions. A predictive analytical approach based on quantitatively evaluating a sample of 1,000 undergraduate students was used in the current study. Various attributes used to evaluate the students included demographic information, academic performance, LMS activity, and learning behaviors. Learning analytics indicators used in the model included previous GPA, attendance rate, assignment completion rate, quiz scores, logins per week, learning hours per week, discussion engagement, engagement index, interaction scores, and learning consistency. In the analysis, the model was validated and tested against accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix, and cross validation tests. Results showed that accuracy, precision, recall, F1-Score, and ROC-AUC of the ANN model were 92.8%, 91.4%, 93.7%, 92.5%, and 0.96, respectively. Based on these outcomes, previous GPA, attendance rate, assignment completion rate, and various engagement indicators were found to be the strongest predictors of student success in college. On the theoretical front, contributions of this study include AI-assisted student performance and behavior prediction. Practically, a sophisticated warning system was developed in this study to assist in effective academic advisement and planning for student retention and academic improvement strategies.
A Computational Thinking Learning Trajectory for Primary Mathematics through Renewable Energy Optimization Projects Swasti Maharani; Irna Tri Yuniahastuti; Vera Dewi Susanti; Ihtiari Prastyaningrum; Addy Septyawan
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 3 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku5023

Abstract

Despite the increasing emphasis on Computational Thinking (CT) in primary education, limited research has examined how CT can be systematically developed through mathematics learning trajectories situated in authentic sustainability contexts. This study aimed to design and validated a Computational Thinking Learning Trajectory (CTLT) that integrates four CT practices—Data Practices, Modeling and Simulation, Computational Problem-Solving, and Systems Thinking—through renewable energy optimization projects in primary mathematics classrooms. Using a design research approach, the study was conducted in three phases: preliminary design, teaching experiment, and retrospective analysis. Participants were 30 fourth-grade students (aged 9–10 years) from an primary school in Madiun, Indonesia. Quantitative and qualitative data were collected through observations, interviews, student artifacts, and assessment rubrics to investigate the emergence of CT practices during learning activities. The results, derived from rubric attainment scores and supported by qualitative evidence, showed that all CT practices emerged during learning activities, with Modeling and Simulation (93.3%), Computational Problem-Solving (83.3%), Data Practices (80%), and Systems Thinking (70%) demonstrating substantial levels of attainment. Students demonstrated a progression from arithmetic reasoning toward algorithmic and systems-oriented thinking as they analyzed data, constructed models, evaluated alternative renewable energy solutions, and optimized decision-making processes. The validated learning trajectory provides empirical evidence of how sustainability-based mathematical projects can support the development of CT in primary education. These findings contribute to the growing body of research on CT integration in mathematics curricula and offer practical guidance for implementing project-based learning within the Indonesian Merdeka Curriculum.
Integration of the STEM Approach in Indonesian Language Learning to Improve Elementary School Students’ Critical Thinking Skills: A Quasi-Experimental Study Dwi Rohman Soleh; Addy Septyawan; Estuning Dewi Hapsari
Jurnal Pendidikan Terapan Vol 4, No 3 July (2026)
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/jupiter.v4i3.1271

Abstract

Purpose: The purpose of this study is to determine whether incorporating the Science, Technology, Engineering, and Mathematics (STEM) method into Indonesian language instruction may enhance elementary school students' critical thinking abilities. The study was carried out in response to students' poor critical thinking abilities and the underutilisation of interdisciplinary teaching methods in Indonesian language instruction. Methods: This study used a nonequivalent control group design and a quasi-experimental method in a quantitative manner. 55 primary school kids made up the participants; they were split into two groups: an experimental class (n = 28) and a control class (n = 27). Pretests and posttests of critical thinking skills, classroom observations, and documentation were used to gather data. Descriptive statistics, homogeneity and normality tests, paired-sample t-tests, and independent-sample t-tests were all used in the data analysis. Findings: The findings showed that students' critical thinking abilities were much enhanced by the use of the STEM methodology. While the independent-sample t-test revealed significantly higher posttest scores in the experimental group relative to the control group (p < 0.05), the paired-sample t-test revealed a significant difference between pretest and posttest scores in the experimental class (p < 0.05). The STEM intervention had a significant educational influence on students' critical thinking abilities, according to the computed effect size. Research implications: Because of the study's single-school context, limited sample size, and lack of random assignment, the results should be interpreted cautiously even though they show the efficacy of STEM-based Indonesian language acquisition. To improve the findings' generalisability, larger and more varied sample sizes from various educational environments should be used in future research. Originality: By incorporating the STEM approach into Indonesian language learning an area that has gotten little attention in prior research this study makes a unique addition. This study shows how STEM integration in language instruction can improve students' critical thinking abilities, in contrast to the majority of STEM studies that concentrate on science and maths.
Co-Authors Arief Hidayat Dendi Pratama Dian Wardiana Sjuchro Dwi Rohman Soleh Hapsari, Estuning Dewi Ihtiari Prastyaningrum Ramadiani - S Sujito Swasti Maharani Vera Dewi Susanti yuniahastuti, irna tri