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All Journal Knowledge Engineering and Data Science Journal of Education Technology Jurnal Paedagogy
Nazir, Shah
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Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Design and Development of Virtual Reality Media on Computer System Learning to Enhance Students' Cognitive Abilities Wahyudin; Akbar, Anthonio; Nugraha, Eki; Riza, Lala Septem; Nazir, Shah
Journal of Education Technology Vol. 9 No. 2 (2025): May
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jet.v9i2.94553

Abstract

The quality of education in Indonesia remains a significant concern, as reflected in the PISA survey, which ranks Indonesia 72nd out of 77 participating countries. One contributing factor is the limited development of Higher Order Thinking Skills (HOTS) among students, particularly in cognitive, psychomotor, and affective domains. This study aims to design and develop Virtual Reality (VR) media integrated with a Self-Directed Learning (SDL) model to enhance students' cognitive abilities in computer system learning. Employing a Research and Development (R&D) approach with the ADDIE model, this experimental research involved 33 students and applied a One Group Pre-test Post-test design. Data were collected through cognitive tests and student response questionnaires, and analyzed using paired sample t-tests and N-Gain calculations. The results indicated a significant improvement in students' cognitive abilities, with overall conceptual gains categorized as moderate and positive student responses toward the VR media. These findings suggest that SDL-based VR media can effectively foster students’ cognitive development, encourage active, independent learning, and serve as an innovative instructional solution to address educational quality challenges. The study implies that immersive technology integration, when paired with appropriate learning models, holds substantial potential in enhancing students' higher-order thinking skills in the digital era.
Development of a Drill-and-Practice Chatbot for Enhancing English Pronunciation through Interactive Dialogue Exercises Iskandar, Aysha Alia; Megasari, Rani; Nazir, Shah; Riza, Lala Septem
Jurnal Paedagogy Vol. 12 No. 4 (2025): October
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jp.v12i4.17760

Abstract

This study aims to implement a drill and practice-based chatbot to improve English speaking skills, particularly in the aspect of pronunciation. The research employed a mixed-methods approach by combining the Research and Development (ADDIE) model with a quasi-experimental design using a pretest-posttest control group pattern. The participants consisted of 76 eighth-grade students from SMPN 5 Cirebon, divided into experimental and control groups. The instruments used included a pronunciation assessment rubric based on the Cambridge English Linguaskill Speaking Global Assessment Criteria, observation sheets, and student perception questionnaires. Data analysis was conducted through normality tests, the Wilcoxon Signed Rank Test, the Mann-Whitney U Test, and N-Gain calculation, complemented by qualitative analysis from observations and questionnaires. The findings revealed that the use of a drill and practice-based chatbot had a positive impact on improving students' pronunciation skills, although the improvement achieved remained merely in the low category, with an N-Gain score of 0.25. The chatbot was proven to provide broader, more flexible, and personalized practice opportunities for students, as well as facilitate instant feedback that is difficult to obtain in conventional learning. These results indicate that chatbots can serve as an effective supplementary medium in English language learning, particularly for practicing pronunciation both independently and in integration with classroom learning, suggesting the potential for further development and integration of chatbot technology in language education.
Optimizing Random Forest Algorithm to Classify Player's Memorisation via In-game Data Alzuhdi, Akmal Vrisna; Ar Rosyid, Harits; Chuttur, Mohammad Yasser; Nazir, Shah
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Assessment of a player's knowledge in game education has been around for some time. Traditional evaluation in and around a gaming session may disrupt the players' immersion. This research uses an optimized Random Forest to construct a non-invasive prediction of a game education player's Memorization via in-game data. Firstly, we obtained the dataset from a 3-month survey to record in-game data of 50 players who play 4-15 game stages of the Chem Fight (a test case game). Next, we generated three variants of datasets via the preprocessing stages: resampling method (SMOTE), normalization (min-max), and a combination of resampling and normalization. Then, we trained and optimized three Random Forest (RF) classifiers to predict the player's Memorization. We chose RF because it can generalize well given the high-dimensional dataset. We used RF as the classifier, subject to optimization using its hyperparameter: n_estimators. We implemented a Grid Search Cross Validation (GSCV) method to identify the best value of n_estimators. We utilized the statistics of GSCV results to reduce the weight of n_estimators by observing the region of interest shown by the graphs of performances of the classifiers. Overall, the classifiers fitted using the BEST n_estimators (i.e., 89, 31, 89, and 196 trees) from GSCV performed well with around 80% accuracy. Moreover, we successfully identified the smaller number of n_estimators (OPTIMAL), at least halved the BEST n_estimators. All classifiers were retrained using the OPTIMAL n_estimators (37, 12, 37, and 41 trees). We found out that the performances of the classifiers were relatively steady at ~80%. This means that we successfully optimized the Random Forest in predicting a player's Memorization when playing the Chem Fight game. An automated technique presented in this paper can monitor student interactions and evaluate their abilities based on in-game data. As such, it can offer objective data about the skills used.
Co-Authors Akbar, Anthonio Alzuhdi, Akmal Vrisna Chuttur, Mohammad Yasser Eki Nugraha Harits Ar Rosyid ISKANDAR, AYSHA ALIA Lala Septem Riza Rani Megasari Wahyudin