NAVIGATING EDUCATIONAL FRONTIERS: A SYSTEMATIC REVIEW ON PREDICTING STUDENT PERFORMANCE THROUGH DATA MINING AND LEARNING ANALYTICS TECHNIQUES
Keywords:
Learning Analytics, Data Mining, Student Performance PredictionAbstract
A Systematic Review on Predicting Student Performance through Data Mining and Learning Analytics Techniques" critically examines the expansive landscape of scholarly research dedicated to forecasting student outcomes using advanced data mining and learning analytics methodologies. This systematic literature review investigates the evolving methodologies, key findings, and emerging trends in the application of data-driven techniques to predict student success. By synthesizing insights from diverse studies, this review aims to provide educators, researchers, and policymakers with a comprehensive understanding of the current state and future directions in predicting academic performance.
Downloads
References
Daniel, B. Big data and analytics in higher education: Opportunities and challenges. Br. J. Educ. Technol. 2015, 46, 904–920.
Zohair, L.M.A. Prediction of student’s performance by modelling small dataset size. Int. J. Educ. Technol. High. Educ. 2019, 16, 27.
Hellas, A.; Ihantola, P.; Petersen, A.; Ajanovski, V.V.; Gutica, M.; Hynninen, T.; Knutas, A.; Leinonen, J.; Messom, C.; Liao, S.N. Predicting academic performance: A systematic literature review. In Proceedings of the Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, Larnaca, Cyprus, 2–4 July 2018; pp. 175–199.
Baradwaj, B.K.; Pal, S. Mining educational data to analyze students’ performance. Int. J. Adv. Comput. Sci. Appl. 2012, 2, 63–69.
Zhang, L.; Li, K.F. Education analytics: Challenges and approaches. In Proceedings of the 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), Krakow, Poland, 16–18 May 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 193–198.
Daud, A.; Aljohani, N.R.; Abbasi, R.A.; Lytras, M.D.; Abbas, F.; Alowibdi, J.S. Predicting student performance using advanced learning analytics. In Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, 3–7 April 2017; pp. 415–421.
Macayan, J.V. Implementing outcome-based education (OBE) framework: Implications for assessment of students’ performance. Educ. Meas. Eval. Rev. 2017, 8, 1–10.
Yassine, S.; Kadry, S.; Sicilia, M.A. A framework for learning analytics in moodle for assessing course outcomes. In Proceedings of the 2016 IEEE Global Engineering Education Conference (EDUCON), Abu Dhabi, UAE, 10–13 April 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 261–266.
Rajak, A.; Shrivastava, A.K.; Shrivastava, D.P. Automating outcome based education for the attainment of course and program outcomes. In Proceedings of the 2018 Fifth HCT Information Technology Trends (ITT), Dubai, UAE, 28–29 November 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 373–376.
Kitchenham, B.; Charters, S. Guidelines for Performing Systematic Literature Reviews in Software Engineering; EBSE: Keele, UK, 2007; pp. 1–65.
Okoli, C.; Schabram, K. A guide to conducting a systematic literature review of information systems research. Ssrn Eletronic J. 2010, 10.
Kaliannan, M.; Chandran, S.D. Empowering students through outcome-based education (OBE). Res. Educ. 2012, 87, 50–63.
Premalatha, K. Course and program outcomes assessment methods in outcome-based education: A review. J. Educ. 2019, 199, 111–127.
Kanmani, B.; Babu, K.M. Leveraging technology in outcome-based education. In Proceedings of the International Conference on Transformations in Engineering Education, New Delhi, India, 5–8 January 2015; Natarajan, R., Ed.; Springer: New Delhi, India, 2015; pp. 415–421.
Namoun, A.; Taleb, A.; Benaida, M. An expert comparison of accreditation support tools for the undergraduate computing programs. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 2018, 9, 371–384.
Mahajan, M.; Singh, M.K.S. Importance and benefits of learning outcomes. IOSR J. Humanit. Soc. Sci. 2017, 22, 65–67.
Namoun, A.; Taleb, A.; Al-Shargabi, M.; Benaida, M. A learning outcome inspired survey instrument for assessing the quality of continuous improvement cycle. Int. J. Inf. Commun. Technol. Educ. (IJICTE) 2019, 15, 108–129.
Taleb, A.; Namoun, A.; Benaida, M. A holistic quality assurance framework to acquire national and international. J. Eng. Appl. Sci. 2019, 14, 6685–6698.
Downloads
Published
How to Cite
Issue
Section
License
All content published in the Journal of Applied Science and Social Science (JASSS) is protected by copyright. Authors retain the copyright to their work, and grant JASSS the right to publish the work under a Creative Commons Attribution License (CC BY). This license allows others to distribute, remix, adapt, and build upon the work, even commercially, as long as they credit the author(s) for the original creation.
