Abstract
Smart education requires design, development, implementation and active use of innovative systems, technologies, teaching and learning strategies and approaches that are based on various data sources in academia, modern mathematical methods in data statistics and data analytics, and state-of-the-art data-driven approaches and technologies. The availability of tools that measure, collect, clean, organize, analyze, process, store, visualize and report data about student academic performance in an academic course and/or student overall academic progress in the selected program of study has given rise to the field of learning analytics for student academic success. Student data representation, processing and prediction, as a central part of learning analytics system, are crucial topics for researchers and practitioners in academia. Our vision for the engineering of smart learning analytics—the next generation of learning analytics—is based on the concept that this technology should strongly support (a) various “smartness” levels of smart education such as adaptivity, sensing, inferring, anticipation, self-learning and self-organization, and (b) main types of data analytics of smart education such as descriptive, diagnostic, predictive and prescriptive analytics. This paper presents the up-to-date findings and outcomes of the research, design and development project at the InterLabs Research Institute at Bradley University (USA) aimed at application of a quantitative approach to student academic performance data representation, hierarchical levels of data processing, multiple quality evaluation criteria to be selected and used, and high-quality student academic performance data prediction in smart learning analytics systems.
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References
Grajek, S.: Top 10 IT issues, 2020: the drive to digital transformation begins, EDUCAUSE Review (2019). https://er.educause.edu/articles/2020/1/top-10-it-issues-2020-the-drive-to-digital-transformation-begins
EDUCAUSE Horizon Report: Teaching and Learning Edition, https://library.educause.edu/resources/2020/3/2020-educause-horizon-report-teaching-and-learning-edition
Aldowah, H., et al.: Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telematics Inform. 37, 13–49 (2019)
Ogor, E.: Student academic performance monitoring and evaluation using data mining techniques, electronics, robotics and automotive mechanics conference (CERMA 2007). Morelos 2007, 354–359 (2007)
Asif, R., et al.: Predicting student academic performance at degree level: a case study, I.J. Intell. Syst. Appl. 01, 49–61 (2015)
Hamza, H., et al.: Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Procedia Technol. 25(2016), 326–332 (2016)
Guo, B., et al.: Predicting students performance in educational data mining, 2015 Int. Symp. Educ. Technol. (ISET), Wuhan, 125–128 (2015)
Halde, R.R., et al.: Psychology assisted prediction of academic performance using machine learning. In: 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) pp. 431–435. Bangalore (2016)
Pereira, F.D. et al.: Early performance prediction for CS1 course students using a combination of machine learning and an evolutionary algorithm. In: Proceedings of 2019 IEEE International Conference on Advanced Learning Technologies (ICALT), pp. 183–184. Maceió, Brazil (2019)
Uskov, V.L. et al.: Smart learning analytics: conceptual modelling and agile engineering. In: Uskov, V.L., Howlett, R.J., Jain, L.C., (eds.) Smart Education and e-Learning 2018, pp. 3–16, Springer. ISBN: 978-3-319-92362-8 (2018)
Uskov, V.L., Bakken, J., Shah, A., Hancher, N., McPartlin, C., Gayke, K.: Innovative InterLabs system for smart learning analytics in engineering education. In: Proceedings of the 2019 IEEE Global Engineering Education Conference (EDUCON), pp. 1363–1369. IEEE, Dubai, UAE (2019). https://ieeexplore.ieee.org/Xplore/home.jsp, https://doi.org/10.1109/educon.2019.8725145
Uskov, V.L., Bakken, J., Shah, A., Byerly, A.: Machine learning-based predictive analytics of student academic performance in STEM education. In: Proceedings of the 2019 IEEE Global Engineering Education Conference (EDUCON), pp. 1370–1376. IEEE, Dubai, UAE (2019). https://ieeexplore.ieee.org/Xplore/home.jsp, https://doi.org/10.1109/educon.2019.8725237
Witten, I. et al.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn, Morgan Kaufmann Publishers (2011). ISBN 978-0-12-374856-0
WEKA: The workbench for machine learning. https://www.cs.waikato.ac.nz/ml/weka/
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Uskov, V.L. et al. (2020). Smart Learning Analytics: Student Academic Performance Data Representation, Processing and Prediction. In: Uskov, V., Howlett, R., Jain, L. (eds) Smart Education and e-Learning 2020. Smart Innovation, Systems and Technologies, vol 188. Springer, Singapore. https://doi.org/10.1007/978-981-15-5584-8_1
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DOI: https://doi.org/10.1007/978-981-15-5584-8_1
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