The Utilization of AI in Student Assessment and Analysis at SMA Negeri 18 Pekanbaru
DOI:
https://doi.org/10.31849/kr4bsa52Keywords:
Artificial Intelligence, Machine Learning, Orange Data, Learning Analytics, TeachersAbstract
This community service activity aims to improve the technological literacy of teachers at State Senior High School 18 Pekanbaru. This is done through training on the use of artificial intelligence (AI), specifically machine learning (ML), in assessing and analyzing student performance. This training uses Orange Data Mining as a practical tool to help teachers understand AI/ML concepts, process student grade data, and build applicable graduation prediction models. This program is designed to address the low utilization of technology in learning evaluation and encourage the adoption of data-driven approaches that align with modern educational trends. Supported by the latest literature (2021–2023), this activity is expected to have a tangible impact on improving the quality of teacher decision-making based on data analysis.
References
Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2021). Digital readiness and teaching innovation: A model for adoption of learning analytics in secondary education. Computers in Human Behavior Reports, 4, 100118.
Aljohani, N. R., & Davis, H. C. (2022). Teachers’ concerns about learning analytics: Challenges and ethical considerations. Journal of Learning Analytics, 9(2), 33-47.
Baker, R. S., Bowers, A. J., & Slater, S. (2021). Predictive modeling in education: Ethical and interpretability challenges. Educational Data Mining Journal, 13(1), 3-17.
Dutt, A., Ismail, M. A., & Herawan, T. (2021). A systematic review on educational data mining: Past, present, and future. IEEE Access, 9, 54195-54210.
Ifenthaler, D., & Yau, J. Y.-K. (2021). Utilising learning analytics to support study success: Reflections from educators. Educational Technology Research and Development, 69(1), 327–331.
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2021). The current landscape of learning analytics in secondary education. Computers in Human Behavior, 116, 106648.
Romero, C., & Ventura, S. (2022). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(2), e1452.
Peña-Ayala, A. (2021). Learning analytics and AI in education: A review of recent work. AI & Education Review, 4, 34–56.
Bowers, A. J., & Zhou, X. (2023). ROC analysis in education: Practical guide for decision makers. Journal of Educational Measurement, 60(1), 22-37.
Siemens, G., & Long, P. (2021). Data-driven decision making in education: The power and pitfalls. EDUCAUSE Review, 56(3), 34–45.
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2021). Comparing learner behavior from LMS log data with academic performance. British Journal of Educational Technology, 52(4), 1241–1256.
Viberg, O., & Grönlund, Å. (2022). Supporting teachers’ digital competence in AI age. Computers & Education: Artificial Intelligence, 3, 100061.
Yilmaz, R. M., & Yilmaz, F. G. K. (2022). Adaptive learning systems and AI in education: Future trends. Educational Technology Research and Development, 70(3), 861–884.
Gao, Q., & Li, M. (2023). Building explainable models for learning analytics in secondary schools. Journal of Learning Analytics, 10(1), 44–60.
Kumar, S., & Singh, R. (2023). Analyzing the impact of AI-based feedback tools in classrooms. Education and Information Technologies, 28, 10109–10128.
