COMPUTATIONAL INTELLIGENCE IN LEARNING ANALYTICS: A MINI REVIEW
DOI:
https://doi.org/10.11113/aej.v14.21375Keywords:
Learning Analytics, Artificial Intelligence, AI Model, Predictive Learning Analytics, Machine Learning, EducationAbstract
he field of Learning Analytics (LA) has witnessed remarkable growth, with a growing emphasis on the utilization of data-driven insights to enhance educational practices. Learning Analytics, encompassing the acquisition, analysis, and interpretation of student data, holds immense promise in transforming education. This review paper synthesizes the key advancements in Learning Analytics, focusing on its definition, benefits, and various levels of learning analytics. A comprehensive literature review has been conducted to delve into existing platforms, LA levels, and technologies. It critically evaluates the significance of predictive Learning Analytics in identifying trends and patterns in educational data. Moreover, the review delves into the integration of Artificial Intelligence (AI) in LA, highlighting its multifaceted utility, from personalized recommendations to intelligent tutoring systems. Several case studies are examined to underscore the real-world applications of AI models in Learning Analytics. This paper offers insights into the advantages of AI-driven LA, such as early intervention and adaptive learning. Challenges and ethical considerations in AI-powered LA are also discussed. Furthermore, it shines a spotlight on the field of machine learning within Learning Analytics, emphasizing its role in automating data analysis and prediction, thus streamlining educational processes. This comprehensive review provides a foundational understanding of the evolving landscape of Learning Analytics, AI, and Machine Learning in education.
References
Lee, L., Cheung, S. K. S., & Kwok, L. 2020. Learning analytics: current trends and innovative practices. Journal of Computers in Education, 7(1): 1–6. https://doi.org/10.1007/s40692-020-00155-8
Jones, K. M. L. 2019. Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. International Journal of Educational Technology in Higher Education, 16(24): 1-22. https://doi.org/10.1186/s41239-019-0155-0
Gouripeddi, P. S., Gouripeddi, R., & Gouripeddi, S. P. 2019. Toward Machine Learning and Big Data Approaches for Learning Analytics. 2019 IEEE Tenth International Conference on Technology for Education (T4E), 256-257. https://doi.org/10.1109/T4E.2019.000-6
Adam, K., Bakar, N. A. A., Fakhreldin, M. A. I., & Majid, M. A. 2019. Big Data and Learning Analytics: A Big Potential to Improve E-learning. Advanced Science Letters, 24(10): 7838-7843. https://doi.org/10.1166/asl.2018.13028
Hantoobi, S., Wahdan, A., Al-Emran, M., & Shaalan, K. 2021. A Review of Learning Analytics Studies. In Recent Advances in Technology Acceptance Models and Theories. Studies in Systems, Decision and Control, 335: 119-134. https://doi.org/10.1007/978-3-030-64987-6_8
Ferguson, R. 2012. Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6): 304-317. https://doi.org/10.1504/IJTEL.2012.051816
Leitner, P., Khalil, M., & Ebner, M. 2017. Learning Analytics in Higher Education – A Literature Review. In Learning Analytics: Fundaments, Applications, and Trends. Studies in Systems, Decision and Control, 94: 1-23. https://doi.org/10.1007/978-3-319-52977-6_1
Shou, Z., Xie, M., Mo, J., & Zhang, H. 2024. Predicting student performance in online learning: a multidimensional time-series data analysis approach. Applied Sciences, 14(6): 1-16. https://doi.org/10.3390/app14062522
Ahmed, E. 2024. Student Performance Prediction using Machine Learning Algorithms. Applied Computational Intelligence and Soft Computing, 2024: 1-15. https://doi.org/10.1155/2024/4067721
Beer, C., Jones, D., & Clark, D. 2012. Analytics and complexity: Learning and leading for the future. In Proceedings of the 29th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE 2012), 1-10.
Yilmaz, F., & Çakır, Hasan. 2021. Learning Analytics and Potential Usage Areas in Education. Journal of Learning and Teaching in Digital Age, 6(2): 81-89.
Gašević, D., Dawson, S., & Siemens, G. 2015. Let’s not forget: Learning analytics are about learning. TECHTRENDS TECH TRENDS, 59(1): 64-71. https://doi.org/10.1007/s11528-014-0822-x
Samuelsen, J., Chen, W., & Wasson, B. 2019. Integrating multiple data sources for learning analytics-review of literature. Research and Practice in Technology Enhanced Learning, 14(1): 1-20. https://doi.org/10.1186/s41039-019-0105-4
Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., & Hlosta, M. 2019. A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educational Technology Research and Development, 67: 1273-1306. https://doi.org/10.1007/s11423-019-09685-0
Arqoub, M. A., El-Khalili, N., Hasan, M. A., & Banna, A. A. 2022. Extending Learning Management System for Learning Analytics. 2022 International Conference on Business Analytics for Technology and Security (ICBATS), 1-6. https://doi.org/10.1109/ICBATS54253.2022.9759070
Bakharia, A., & Dawson, S. 2011. SNAPP: a bird’s-eye view of temporal participant interaction. LAK’11: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, 168-173. https://doi.org/10.1145/2090116.2090144
Atif, A., Richards, D., Bilgin, A., & Marrone, M. 2013. Learning Analytics in Higher Education: A Summary of Tools and Approaches. Proceedings of Electric Dreams. Proceedings ascilite 2013 Sydney, 68-72.
Jackson, G., & Read, M. 2012. Connect for Success: A proactive student identification and support program. 1-5. Retrieved from https://unistars.org/past_papers/papers12/Papers/9B.pdf (Sept 2023)
Sclater, N., Peasgood, A., & Mullan, J. 2016. Learning analytics in higher education: a review of UK and international practice. Retrieved from https://vital.voced.edu.au/vital/access/services/Download/ngv:83377/SOURCE201 (Sept 2023)
Wagner, E., & Ice, P. 2012. Data Changes Everything: Delivering on the Promise of Learning Analytics in Higher Education. EDUCAUSE. Retrieved from https://er.educause.edu/articles/2012/7/data-changes-everything-delivering-on-the-promise-of-learning-analytics-in-higher-education (Sept 2023)
El-Den, F. E. 2022. Student information system. ResearchGate. http://dx.doi.org/10.13140/RG.2.2.24077.61928
Early Warning Systems in Education. (2019, February 15). American Institutes for Research. Retrieved from https://www.air.org/resource/early-warning-systems-education (Sept 2023)
Akçapınar, G., Altun, A., & Aşkar, P. 2019. Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1): 1-20. https://doi.org/10.1186/s41239-019-0172-z
Forster, S. (2022, May 26). Leading teaching and learning support system for K-12 schools SEQTA – the industry leading school learning management system. Retrieved from https://educationhorizons.com/solutions/seqta/ (Jan 2024)
Tool. (2023). OnTask. https://www.ontasklearning.org/tool/ Retrieved from https://www.ontasklearning.org/tool/ (Jan 2024)
Guzmán-Valenzuela, C., González, C. G., Tagle, A. F. R., & Lorca-Vyhmeister, A. 2021. Learning analytics in higher education: a preponderance of analytics but very little learning? International Journal of Educational Technology in Higher Education, 18(1): 1-19. https://doi.org/10.1186/s41239-021-00258-x
Al-Zahrani, A. M., & Alasmari, T. 2023. Learning Analytics for Data-Driven Decision Making: Enhancing Instructional Personalization and Student Engagement in Online Higher Education. International Journal of Online Pedagogy and Course Design, 13(1): 1-18. https://doi.org/10.4018/IJOPCD.331751
Alalawi, K., Athauda, R., Chiong, R., & Renner, I. 2024. Evaluating the student performance prediction and action framework through a learning analytics intervention study. Education and Information Technologies, 2024: 1-30. https://doi.org/10.1007/s10639-024-12923-5
Brooks, C., & Thompson, C. 2017. Predictive modeling in teaching and learning. In Handbook of learning analytics, 61-68.
Yağcı, M. 2022. Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9: 1-19. https://doi.org/10.1186/s40561-022-00192-z
Sghir, N., Adadi, A., & Lahmer, M. 2022. Recent advances in Predictive Learning Analytics: A decade systematic review (2012-2022). Education and Information Technologies, 28: 8299-8333. https://doi.org/10.1007/s10639-022-11536-0
Association for Talent Development. 2018. 4 Uses for Predictive Learning Analytics. Retrieved from https://www.td.org/professional-partner-content/4-uses-for-predictive-learning-analytics (Jan 2024)
Gašević, D., Jovanović, J., Pardo, A., and Dawson, S. 2017. Detecting Learning Strategies with Analytics: Links with Self-Reported Measures and Academic Performance. Journal of Learning Analytics, 4(2), 113-128.
Edly. 2022. Using predictive analytics in elearning. Retrieved from https://edly.io/blog/using-predictive-analytics-in-elearning/ (Jan 2024)
Siemens, G., & Baker, R. S. 2012. Learning analytics and educational data mining: towards communication and collaboration. In LAK’12: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252-254. https://doi.org/10.1145/2330601.2330661
Sandoval, A., Gonzales, C., Alacron, R., Pichara, R., & Montenegro, M. 2018. Centralized student performance prediction in large courses based on low-cost variables in an institutional context. The internet and Higher Education, 37(1): 76-89. https://doi.org/10.1016/j.iheduc.2018.02.002
Bravo-Agapito, J., Romero, S. J., & Pamplona, S. 2021. Early prediction of undergraduate Student’s academic performance in completely online learning: A five-year study. Computers in Human Behavior, 115(1): 106595. https://doi.org/10.1016/j.chb.2020.106595
Baneres, D., Rodríguez-Gonzalez, M. E., & Serra, M. 2019. An Early Feedback Prediction System for Learners At-Risk Within a First-Year Higher Education Course. IEEE Transactions on Learning Technologies, 12(2): 249-263. https://doi.org/10.1109/TLT.2019.2912167
Xing, W., & Du, D. 2019. Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention. Journal of Educational Computing Research, 57(3): 547-570. https://doi.org/10.1177/0735633118757015
Kaplan, A. 2021. Higher Education at the Crossroads of Disruption. Leeds: Emerald Publishing Limited.
Baraishuk, D. 2023. Adaptive Learning AI technology in education. Belitsoft, retrieved from https://belitsoft.com/custom-elearning-development/ai-in-education/adaptive-learning-ai (Jan 2024)
Barari, A. 2023. How is Adaptive Learning Improving Education in the Digital World? Emeritus, retrieved from https://emeritus.org/blog/online-learning-adaptive-learning/ (Jan 2024)
National Mentoring Day. 2021. Facts and Statistics. Retrieved from https://nationalmentoringday.org/facts-and-statistics/Statistics (Jan 2024)
Wikipedia. 2023. Prodigy education. Retrieved from https://en.wikipedia.org/wiki/Prodigy_Education#Gameplay (Jan 2024)
Presthitha, H., & Goswami, C. 2022. Embibe - How this AI-Powered Edtech is Making Learning Easy. Startup Talky, retrieved from https://startuptalky.com/embibe-success-story/ (Jan 2024)
Morel, G. M., & Spector, J. M. 2023. Foundations of Educational Technology: Integrative Approaches and Interdisciplinary Perspectives. London: Routledge.
Chatti, M. A. 2010. Personalization in Technology Enhanced Learning: A Social Software Perspective. Aachen : Skaker.
Miliband, D. 2006. Choice and voice in personalized learning. In Personalizing Education, 21-30. https://doi.org/10.1787/9789264036604-2-en
Raudys, J. 2021. 7 Personalized Learning Strategies to Implement in Class & Examples. Prodigy, retrieved from https://www.prodigygame.com/main-en/blog/personalized-learning/#:~:text=Personalizing%20content%20delivery%20and%20interaction,an%20audio%20version%20while%20reading. (Jan 2024)
Andreev, I. 2023. Personalized Learning. Valamis, retrieved from https://www.valamis.com/hub/personalized-learning#how-does-personalized-learning-work (Jan 2024)
Flanigan, R. L. 2017. Personalized-Learning Case Studies: Lessons From 3 Schools. EducationWeek, retrieved from https://www.edweek.org/teaching-learning/personalized-learning-case-studies-lessons-from-3-schools/2017/11 (Jan 2024)
Bhattacharyya, S. 2023. 7 Applications of NLP in the education sector. Analytics Steps, retrieved from https://www.analyticssteps.com/blogs/7-applications-nlp-education-sector (Jan 2024)
Floridi, L., & Chiriatti, M. 2020. GPT-3: Its Nature, Scope, Limits, and Consequences. Minds and Machines, 30: 681-694. https://doi.org/10.1007/s11023-020-09548-1
Ausin, M. S., Azizsoltani, H., Barnes, T., & Chi, M. 2019. Leveraging deep reinforcement learning for pedagogical policy induction in an intelligent tutoring system. In Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019), 168-177. https://par.nsf.gov/servlets/purl/10136494
TORRES.AI, J. 2020. A gentle introduction to Deep Reinforcement Learning. Medium, retrieved from https://towardsdatascience.com/drl-01-a-gentle-introduction-to-deep-reinforcement-learning-405b79866bf4 (Jan 2024)
Domoscio. (2022). Domoscio. https://domoscio.com/en/domoscio-spark-2/ (Jan 2024)
Songer, N. B., Newstadt, M. R., Lucchesi, K., & Ram, P. 2020. Navigated learning: An approach for differentiated classroom instruction built on learning science and data science foundations. Hum Behav & Emerg Tech, 2(1): 93-105. https://doi.org/10.1002/hbe2.169
Bull, S., & Kay, J. 2010. Open learner models. In Advances in Intelligent Tutoring Systems. Studies in Computational Intelligence, 308: 301-322. https://doi.org/10.1007/978-3-642-14363-2_15
SayHi. (2022). SayHi translate. https://www.sayhi.com/en/translate/ . Amazon Inc. retrieved (Sept 2023)
Photomath. (2022). Photomath. https://photomath.com/en (Jan 2024)
Watters, A. 2015. A brief history of calculators in the classroom. Retrieved from https://hackeducation.com/2015/03/12/calculators (Jan 2024)
Dan, Z. 2021. Ministry removes homework help apps, citing impact on student learning. Global Times, retrieved from https://www.globaltimes.cn/page/202112/1241383.shtml (Jan 2024)
McGuire, L. S., & Alaraj, A. 2018. Competency Assessment in Virtual Reality-Based Simulation in Neurosurgical Training. In Comprehensive Healthcare Simulation: Neurosurgery, 153-157. https://doi.org/10.1007/978-3-319-75583-0_12
Behmke, D., Kerven, D., Lutz, R., Paredes, J., Pennington, R., Brannock, E., Deiters, M., Rose, J., & Stevens, K. 2018. Augmented reality chemistry: Transforming 2-D molecular representations into interactive 3-D structures. Proceedings of the Interdisciplinary STEM Teaching and Learning Conference (2017-2019), 2: 5-11. https://doi.org/10.20429/STEM.2018.020103
LaPierre, J. 2021. Educational games and ai. Retrieved from https://www.filamentgames.com/blog/educational-games-and-ai/ (Jan 2024)
Barua., P. D., Vicnesh, J., Gururajan, R., Oh, S. L., Palmer, E., Azizan, M. M., Kadri, N. A., & Acharya, U. R. 2022. Artificial Intelligence Enabled Personalized Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders-A Review. Int J Environ Res Public Health, 19(3): 1192. https://doi.org/10.3390/ijerph19031192
Anuradha, J., Tisha, Ramachandran, V., Arulalan, K. V., & Tripathy, B. K. 2010. Diagnosis of ADHD using SVM algorithm. In COMPUTE’10: Proceedings of the Third Annual ACM Bangalore Conference, 1-4. https://doi.org/10.1145/1754288.1754317
Benfatto, M. N., Seimyr, G. Ö., Ygge, J., Pansell, T., Rydberg, A., & Jacobson, C. 2016. Screening for Dyslexia Using Eye Tracking during Reading. PLOS ONE, 11(12): e0165508. https://doi.org/10.1371/journal.pone.0165508
Asselborn, T., Chapatte, Mateo., & Dillenbourg, 2020. Extending the Spectrum of Dysgraphia: A Data Driven Strategy to Estimate Handwriting Quality. Scientific Reports, 10(1): 3140. https://doi.org/10.1038/s41598-020-60011-8
Alabdulkareem, A., Alhakbani, N., & Al-Nafjan, A. 2022. A Systematic Review of Research on Robot-Assisted Therapy for Children with Autism. Sensors, 22(3): 944. https://doi.org/10.3390/s22030944
Huawei. 2021. Huawei StorySign. Retrieved from https://developer.huawei.com/consumer/en/doc/hiai-Guides/storysign-0000001055597759 (Jan 2024)
Marcus, G., & Davis, E. 2020. Insights for AI from the human mind. Communication of the ACM, 64(1): 38-41. https://doi.org/10.1145/3392663
Hwang, G. J., & Chang C. Y. 2021. A review of opportunities and challenges of chatbots in education. Interactive Learning Environments, 31(7): 4099-4112. https://doi.org/10.1080/10494820.2021.1952615
Pérez, J. Q., Daradoumis, T., & Puig, J. M. M. 2020. Rediscovering the use of chatbots in education: A systematic literature review. Computer Applications in Engineering Education, 28(6): 1549-1565. https://doi.org/10.1002/cae.22326
Hussain, A. 2017. Ada - Bolton College's Latest Digital Assistant. Retrieved from https://www.aftabhussain.com/ada.html (Jan 2024)
Guilherme, A. 2019. AI and education: the importance of teacher and student relations. AI & SOCIETY, 34(1): 47-54. https://doi.org/10.1007/s00146-017-0693-8
Selwyn, N. 2019. Should robots replace teachers? AI and the Future of Education. UK: Polity Press.
Graide. 2022. Retrieved from https://www.graide.co.uk/ (Jan 2024)
Takyar, A. 2023. Benefits and use cases of AI in education. Retrieved from https://www.leewayhertz.com/ai-use-cases-in-education/ (Sept 2023)
Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. 2024. The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technological Forecasting and Social Change, 199: 1-18. https://doi.org/10.1016/j.techfore.2023.123076
McNeely, C. L., & Hahm, J. 2014. The Big (Data) Bang: Policy, Prospect, and Challenges. Review of Policy Research, 31: 304-310. https://doi.org/10.1111/ropr.12082
Prinsloo, P., Khalil, Mohammad., & Slade, Sharon. 2024. Learning analytics as data ecology: a tentative proposal. Journal of Computing in Higher Education, 36: 154-182. https://doi.org/10.1007/s12528-023-09355-4
Riazy, S., Simbeck, K., & Schreck, V. 2021. Systematic Literature Review of Fairness in Learning Analytics and Application of Insights in a Case Study. In Computer Supported Education. CSEDU 2020, 1473: 430-449. https://doi.org/10.1007/978-3-030-86439-2_22
Francis, M., Avoseh, M., Card, K., Newland, L., & Streff, K. 2023. Student Privacy and Learning Analytics: Investigating the Application of Privacy Within a Student Success Information System in Higher Education. Journal of Learning Analytics, 10(3): 102-114. https://doi.org/10.18608/jla.2023.7975
Ayling, J., & Chapman, A. 2022. Putting AI ethics to work: are the tools fit for purpose? AI and Ethics, 2(1): 405-429. https://doi.org/10.1007/s43681-021-00084-x
Jobin, A., Ienca, M., & Vayena, E. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(1): 389-399. https://doi.org/10.1038/s42256-019-0088-2
Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. 2022. The ethics of algorithms: key problems and solutions. AI & SOCIETY, 37(1): 215-230. https://doi.org/10.1007/s00146-021-01154-8
Adams, C., Pente, P., Lemermeyer, G., & Rockwell, Geoffrey. 2021. Artificial Intelligence Ethics Guidelines for K-12 Education: A Review of the Global Landscape. In Artificial Intelligence in Education. AIED 2021, 12749: 24-28. https://doi.org/10.1007/978-3-030-78270-2_4
Aiken, R. M., & Epstein, R. G. 2000. Ethical guidelines for AI in education: Starting a conversation. International Journal of Artificial Intelligence in Education, 11(2): 163-176.
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. 2022. Ethics of AI in Education: Towards Community-Wide Framework. International Journal of Artificial Intelligence in Education, 32(1): 504-526. https://doi.org/10.1007/s40593-021-00239-1
Holmes, W., & Porayska-Pomsta, K. 2023. The Ethics of Artificial Intelligence in Education. Practices, Challenges, and Debates. London: Routledge.
Holstein, K., & Doroudi, S. 2021. Equity and artificial intelligence in education: will “aied” amplify or alleviate inequilities in education? arXiv preprint, 1-24. https://doi.org/10.48550/arXiv.2104.12920
Ferrara, E. 2024. Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. Sci, 6(1): 1-15. https://doi.org/10.3390/sci6010003
