StudyPalz: A Personalized Academic Learning Path Recommendation System

doi:10.23940/ijpe.25.09.p3.496505

Abstract

Abstract: The growing demand for adaptive learning software has highlighted the drawback of static content delivery. Focusing on content delivery, StudyPalz is an adaptive learning system designed to boost student productivity. Rather than offering generic content, it finds particular learning needs and provides modified tools including mind maps, short films, and mnemonic devices. Built on Django, StudyPalz starts with diagnostic quizzes to gauge conceptual knowledge. Depending on quiz results, it identifies weak areas and recommends focused study material as well as tailored re-attempt quizzes to strengthen learning.
In the interest of consistency and well-being, StudyPalz also includes a Pomodoro timer and task planner, encouraging focused, brief study intervals and good use of time. A field study of 150+ students and 200+ completed quizzes revealed misconceptions and the need for curriculum improvement, informing the potential for the platform to offer real-time feedback loops for instructors. Student feedback also indicated a preference for concise visual aids over static content. A case study of 10 students indicated a score improvement from 51 to 85 over five quizzes, representing a 66.67% improvement. StudyPalz overall integrates adaptive diagnostics, personalized content, and progress tracking to enable academic improvement. Its modular nature also provides opportunities for future AI-based tutoring and multilingual support.

Key words: adaptive learning, Django framework, LMS, performance analytics, study path recommendation

Avadhoot Rajurkar, Aakash Darda, Aaryaman Mishra, Aayush Barsaniya, and Abhaykumar Roy. StudyPalz: A Personalized Academic Learning Path Recommendation System [J]. Int J Performability Eng, 2025, 21(9): 496-505.

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References

[1] Ma Y., Wang L., Zhang J., Liu F., and Jiang Q., 2023. A personalized learning path recommendation method incorporating multi-algorithm.Applied Sciences, 13(10), 5946.
[2] Li T., Wang X., Zhang S., Yang F., and Lu W., 2023. A personalized learning path recommendation method for learning objects with diverse coverage levels. InInternational Conference on Artificial Intelligence in Education, pp. 714-719.
[3] Mansouri N., Soui M., and Abed M., 2023. Full personalized learning path recommendation: A literature review. InInternational Conference on Advanced Intelligent Systems and Informatics, pp. 185-195.
[4] Nabizadeh A.H., Goncalves D., Gama S., Jorge J., and Rafsanjani H.N., 2020. Adaptive learning path recommender approach using auxiliary learning objects.Computers & Education, 147, 103777.
[5] Rahayu N.W., Ferdiana R., and Kusumawardani S.S., 2023. A systematic review of learning path recommender systems. Education and Information Technologies,28(6), pp. 7437-7460.
[6] Cai D., Zhang Y., and Dai B., 2019. Learning path recommendation based on knowledge tracing model and reinforcement learning. In2019 IEEE 5th International Conference on Computer and Communications (ICCC), pp. 1881-1885.
[7] Zhang X., Liu S., and Wang H., 2023. Personalized learning path recommendation for e-learning based on knowledge graph and graph convolutional network. International Journal of Software Engineering and Knowledge Engineering,33(01), pp. 109-131.
[8] Zhou Y., Huang C., Hu Q., Zhu J., and Tang Y., 2018. Personalized learning full-path recommendation model based on LSTM neural networks.Information Sciences, 444, pp. 135-152.
[9] Li H., Li H., Zhang S., Zhong Z., and Cheng J., 2019. Intelligent learning system based on personalized recommendation technology. Neural Computing and Applications,31(9), pp. 4455-4462.
[10] Wang S.L., and Wu C.Y., 2011. Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with Applications,38(9), pp. 10831-10838.
[11] Klašnja-Milićević A., Vesin B., Ivanović M., and Budimac Z., 2011. E-learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education,56(3), pp. 885-899.
[12] Li J.W., Chang Y.C., Chu C.P., and Tsai C.C., 2012. A self-adjusting e-course generation process for personalized learning. Expert Systems with Applications,39(3), pp. 3223-3232.
[13] Klašnja-Milićević A., Ivanović M., Vesin B., and Budimac Z., 2018. Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques. Applied Intelligence,48(6), pp. 1519-1535.
[14] Tang X., Chen Y., Li X., Liu J., and Ying Z., 2019. A reinforcement learning approach to personalized learning recommendation systems. British Journal of Mathematical and Statistical Psychology,72(1), pp. 108-135.
[15] Muhammad A., Zhou Q., Beydoun G., Xu D., and Shen J., 2016. Learning path adaptation in online learning systems. In2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 421-426.
[16] He H., Zhu Z., Guo Q., and Huang X., 2019. A personalized e-learning services recommendation algorithm based on user learning ability. In2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 2161, pp. 318-320.
[17] Bremgartner V., de Magalhães Netto J.F., and de Menezes C.S., 2015. Adaptation resources in virtual learning environments under constructivist approach: A systematic review. In2015 IEEE Frontiers in Education Conference (FIE), pp. 1-8.
[18] Bremgartner V., and de Magalhães Netto J.F., 2012. Improving collaborative learning by personalization in virtual learning environments using agents and competency-based ontology. In2012 Frontiers in Education Conference Proceedings, pp. 1-6.