StudyPalz: A Personalized Academic Learning Path Recommendation System

DOI

10.2991/978-94-6463-866-0_53How to use a DOI?

Keywords

Adaptive learning; Django framework; LMS; performance analytics; study path recommendation

Abstract

The increasing demand for adaptive learning systems has highlighted the limitations of traditional, one-size-fits-all educational approaches. StudyPalz, a personalized learning platform has been crafted to help students study more effectively by pinpointing their weak spots and providing tailored resources like mind maps, short videos, and mnemonic aids. It kicks off with quick diagnostic quizzes to assess a student’s understanding of various concepts. Depending on how they perform, StudyPalz suggests customized study materials and creates focused re-attempt quizzes to reinforce what they’ve learned. To help maintain consistency and prevent burnout, it also includes time management tools like a Pomodoro timer and a task planner-creating a unified ecosystem for both academic support and habit-building.

In a pilot study involving over 150 students who completed more than 200 quizzes, recurring misconceptions were identified, highlighting the need for better teaching strategies and improved learning resources. The platform also shows that students prefer concise visual content over traditional, text-heavy materials. In a smaller study with 10 students, average quiz scores jumped from 51 to 85 over five attempts—a remarkable 66.67% increase—showing the platform’s potential to boost academic performance.

StudyPalz seamlessly combines adaptive feedback, progress tracking, and smart content recommendations to create meaningful learning experiences. Its modular design allows for future upgrades, such as AI-based tutoring and multilingual features, making it a promising option for personalized, data-driven education.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Avadhoot Rajurkar
AU  - Aakash Darda
AU  - Aayush Barsainya
AU  - Abhaykumar Roy
AU  - Aaryaman Mishra
AU  - Aariz Kadri
AU  - Abbas Murtaza
PY  - 2025
DA  - 2025/10/31
TI  - StudyPalz: A Personalized Academic Learning Path Recommendation System
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 642
EP  - 654
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_53
DO  - 10.2991/978-94-6463-866-0_53
ID  - Rajurkar2025
ER  -