Abstract
There exist numerous resources online to gain the desired level of knowledge on any topic. However, this complicates the process of selecting the most appropriate resources. Every learner differs in terms of their learning speed, proficiency, and preferred mode of learning. This paper develops an adaptive learning management system to tackle this challenge. It creates a customized course for every student based on their level of knowledge, preferred mode of learning and continuously updates the course based on their learning speed. The material is filtered from a knowledge base that is dynamically updated using web scraping and ranked using feedback from students on the relevance and quality of each material. The model is tested in two phases: the content generation algorithm and the learnability of the system as a whole. The evaluation is done both quantitatively and qualitatively and validated with statistical analysis. Real-time testing of the system shows state-of-the-art performance.
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The implementation of the system can be found here: https://github.com/Akshayaks/Final_Year_ProjectCode Link.
References
Abu-Alsaad, H.A. (2019). Agent applications in e-learning systems and current development and challenges of adaptive E-learning systems. In 2019 11th international conference on electronics, computers and artificial intelligence (ECAI). https://doi.org/10.1109/ECAI46879.2019.9042015 (pp. 1–6).
Al-Fraihat, D., & et al. (2020). Evaluating E-learning systems success: An empirical study. In Computers in human behavior. issn: 0747-5632, (Vol. 102 pp. 67–86). https://doi.org/10.1016/j.chb.2019.08.004. http://www.sciencedirect.com/science/article/pii/S0747563219302912.
Alsadoon, E. (2020). The impact of an adaptive e-course on students’ achievements based on the students’ prior knowledge. In Education and information technologies. https://doi.org/10.1007/s10639-020-10125-3.
Amit, K.N.J.A., & Singh, N. (2018). Learner characteristics based learning style models classification and it’s implications on teaching. In International journal of pure and applied mathematics, (Vol. 118 pp. 175–184).
Azzi, I., & et, al. (2019). A robust classification to predict learning styles in adaptive E-learning systems. In Education and information technologies. https://doi.org/10.1007/s10639-019-09956-6 (p. 25).
Botchkarev, A. (2018). Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology. In arXiv:1809.03006.
Chang, Y.-H., & et al. (2016). Yet another adaptive learning management system based on Felder and Silverman’S Learning Styles and Mashup. In EURASIA journal of mathematics, science technology education, (Vol. 12 pp. 1273–1285). https://doi.org/10.12973/eurasia.2016.1512a.
Chen, H., & et al. (2020). Enhanced learning resource recommendation based on online learning style model. In Tsinghua science and technology, (Vol. 25.3 pp. 348–356).
Dziuban, C., & et, al. (2016). Adaptive learning in psychology: Wayfinding in the digital age. In Online learning. https://doi.org/10.24059/olj.v20i3.972 (p. 20).
Fatahi, S. (2019). An experimental study on an adaptive e-learning environment based on learner’s personality and emotion. In Education and information technologies (p. 24). https://doi.org/10.1007/s10639-019-09868-5.
Gerald, B. (2018). A brief review of independent, dependent and one sample t-test. In International journal of applied mathematics and theoretical physics, (Vol. 4.2 pp. 50–54).
Gunawan, D., Sembiring, C., & Budiman, M. (2018). The implementation of cosine similarity to calculate text relevance between two documents. In Journal of physics: conference series, (Vol. 978 p. 012120). https://doi.org/10.1088/1742-6596/978/1/012120.
Hamat, A., & Amin, M. (2010). Constructivism in the design of online learning tools. In European journal of educational studies (p. 2).
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. In Neural computation, (Vol. 9 pp. 1735–80). https://doi.org/10.1162/neco.1997.9.8.1735.
Hubalovsky, S., Hubalovska, M., & Musilek, M. (2019). Assessment of the in uence of adaptive E-learning on learning effectiveness of primary school pupils. In Computers in human behavior. issn: 0747-5632, (Vol. 92 pp. 691–705). https://doi.org/10.1016/j.chb.2018.05.033. http://www.sciencedirect.com/science/article/pii/S0747563218302590.
Jagadeesan, S., & Subbiah, J. (2020). Real-time personalization and recommendation in adaptive learning management system. In Journal of ambient intelligence and humanized computing. https://doi.org/10.1007/s12652-020-01729-1(pp. 1–11).
Johari, J., & et al. (2010). Difficulty index of examinations and their relation to the achievement of programme outcomes. In Procedia - Social and Behavioral Sciences 18. Kongres Pengajaran dan Pembelajaran UKM. issn: 1877-0428 (pp. 71–80). https://doi.org/10.1016/j.sbspro.2011.05.011. http://www.sciencedirect.com/science/article/pii/S1877042811011244.
Kausar, S., & et al. (2018). Integration of data mining clustering approach in the personalized E-Learning system. In IEEE Access, (Vol. 6 pp. 72724–72734).
Kirschner, P. (2017). Stop propagating the learning styles myth. In Comput Educ, (Vol. 106 pp. 166–171).
Klašnja-Milićević, A., & et al. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. In Computers education. issn: 0360-1315, (Vol. 56.3 pp. 885–899). https://doi.org/10.1016/j.compedu.2010.11.001. http://www.sciencedirect.com/science/article/pii/S0360131510003222.
Kolekar, S., Pai, R., & Manohara, M.M. (2018). Rule based adaptive user interface for adaptive E-learning system. In Education and information technologies (p. 24). https://doi.org/10.1007/s10639-018-9788-1.
Kumar, B., & Sharma, B. (2020). Context aware mobile learning application development: A systematic literature review. In Education and information technologies. https://doi.org/10.1007/s10639-019-10045-x (p. 25).
Moubayed, A., & et al. (2018). E-Learning: Challenges And research opportunities using machine learning data analytics. In IEEE Access, (Vol. 6 pp. 39117–39138).
Qaiser, S., & Ali, R. (2018). Text Mining: Use of TF-IDF to examine the relevance of words to documents. In International journal of computer applications. https://doi.org/10.5120/ijca2018917395 (p. 181).
Rodrigues, H., & et al. (2019). Tracking e-learning through published papers: A systematic review. In Computers education. issn: 0360-1315, (Vol. 136 pp. 87–98). https://doi.org/10.1016/j.compedu.2019.03.007. http://www.sciencedirect.com/science/article/pii/S0360131519300715.
Seel, N.M. (2012). Carroll’s model of school learning. In Seel, N.M. (Ed.) Encyclopedia of the sciences of learning. isbn: 978-1-4419-1428-6. https://doi.org/10.1007/978-1-4419-1428-6980 (pp. 501–503). Boston: Springer.
Stevens, K., & et al. (2012). Exploring topic coherence over many models and many topics.
Šumak, B., & et al. (2019). Development of an autonomous, intelligent and adaptive e-learning system. In 2019 42nd international convention on information and communication technology, electronics and Microelectronics (MIPRO). https://doi.org/10.23919/MIPRO.2019.8756889 (pp. 1492–1497).
Surjono, H.D. (2013). The development of an adaptive E-Learning system by customizing an LMS Moodle.
Tang, T., & McCalla, G. (2005). Smart recommendation for an evolving e-learning system: architecture and experiment. In International journal on E-Learning (p. 4).
Terzis, V., Moridis, C., & Economides, A. (2012). The effect of emotional feedback on behavioral intention to use computer based assessment. In Computers education, (Vol. 59 pp. 710–721). https://doi.org/10.1016/j.compedu.2012.03.003.
Valverde-Berrocoso, J., & et al. (2020). Trends in educational research about e-learning: A systematic literature review (2009–2018). In Sustainability. issn: 2071-1050, (Vol. 12.12 p. 5153). https://doi.org/10.3390/su12125153.
Weaver, B. (2015). Minimum sample size for t-test.
Wu, C.-H., Chen, Y.-S., & Chen, T.-C. (2017). An adaptive e-learning system for enhancing learning performance: Based on dynamic scaffolding theory. In Eurasia journal of mathematics, science and technology education, (Vol. 14 pp. 903–913).
Wu, D., Lu, J., & Zhang, G. (2015). A fuzzy tree matching-based personalized e-learning recommender system. In IEEE Transactions on fuzzy systems, (Vol. 23.6 pp. 2412–2426).
Xie, H., & et al. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. In Computers education. issn: 0360- 1315., (Vol. 140 p. 103599) https://doi.org/10.1016/j.compedu.2019.103599. http://www.sciencedirect.com/science/article/pii/S0360131519301526.
Yilmaz, K. (2011). The cognitive perspective on learning: its theoretical underpinnings and implications for classroom practices. In The clearing house, (Vol. 84 pp. 204–212). https://doi.org/10.1080/00098655.2011.568989.
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Sridharan, S., Saravanan, D., Srinivasan, A.K. et al. Adaptive learning management expert system with evolving knowledge base and enhanced learnability. Educ Inf Technol 26, 5895–5916 (2021). https://doi.org/10.1007/s10639-021-10560-w
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DOI: https://doi.org/10.1007/s10639-021-10560-w