Personalised eLearning

LIU Fan*, Esther TAN Meng Yoke, and CHIA Yuen Kwan 

NUS-ISS 

*isslf@nus.edu.sg  

Liu, F., Tan, E. M. Y., & Chia, Y. K. (2024). Harnessing GPT to develop a personalised elearning web application for Java programming [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-lfan-et-al/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Generative AI, Personalised eLearning, ChatGPT, Java Programming 

CATEGORY

Lightning Talk

BACKGROUND

E–learning systems are gaining popularity due to their extensive scalability, flexibility, self-paced capability as well as easy accessibility. Artificial Intelligence (AI) has been used in e-Learning systems (Murtaza et al., 2022; Sayed et. al., 2023) to make personalised learning recommendations to learners. Specifically, Generative Pre-trained Transformers (GPT) such as ChatGPT (OpenAI, 2023) has transformed education by offering intelligent assistance, customised content, personalised learning materials to cater to the individual learner’s needs (Rasul, 2023; Jin & Kim, 2023; Gharbi et al., 2024). AI-based eLearning can significantly enhance learning efficiency through personalised content delivery to learners (Bozkurt, 2021; Chen et al., 2023). However, these AI-based personalised eLearning techniques have not been effectively integrated to provide diverse and dynamic eLearning content for learners based on their personalities and learning preferences. 

PURPOSE

Propose an approach to provide dynamic and personalised content for learners through integrated Generative AI techniques. We developed an eLearning web application that connects to ChatGPT API, which will automatically generate dynamic course contents/exercises/quizzes based on the learners’ personality such as level of language proficiency and personal learning preferences for Java foundations. 

RESEARCH QUESTIONS

1. How to automatically generate dynamic and personalised Java foundational content for each learner on the fly?
2. How to automatically generate dynamic and personalised coding exercise questions for each learner on the fly?
3. How to provide a more efficient learning experience for each learner based on the learner’s personality and learning preferences? 

METHOD

Design and develop an eLearning web application that seamlessly integrates the following technologies:

1. Automatically assesses learner’s learning preferences using the Felder-Silverman learning style model (FSLSM) (Felder & Silverman, 2012). The FSLSM categorises learners into specific dimensions based on their preferred ways of acquiring and processing information. This model is used in our eLearning application to customise the course delivery format to suit the learners’ learning preferences.
2. Harness the GPT model to generate dynamic course contents
3. Provide diverse and personalised eLearning content based on learners’ personality and learning preferences.

 

The general architecture of the eLearning application consists of three modules: web browser client, eLearning server, and Microservices (ChatGPT server). The web browser client enables learners to interact with the eLearning application. The eLearning server delivers the core functionality of the system which includes the generation of dynamic topics, personalised exercises, and quizzes while the Microservice represents the external ChatGPT server.  

KEY FINDINGS 

This study explores how ChatGPT with FSLSM can be harnessed to create a customised Java programming course and provide learners with dynamic and personalised course materials. This method promotes a customised learning journey catering to the different learner’s needs in terms of their levels of Java proficiency and learning preferences.  

FUTURE WORK

The future work of the proposed eLearning application is summarised as follows: (1) using ChatGPT model to generate comprehensive course content including advanced topics of Java, (2) extend this research work to generate dynamic and personalised course content for other object-oriented programming languages as C++, C#, and Python, (3) introduce foundational topics, exercises and quizzes with support of different learning methods, such as hands-on workshops, auto-grading and learning feedback and report., (4) extend the application of the personalised eLearning application to include other academic disciplines. This future work should consider the challenges of the use of ChatGPT in education with regards to the accuracy and reliability of content.

REFERENCES​

Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). Artificial intelligence and reflections from educational landscape: A review of AI Studies in half a century. Sustainability, 13(2), 800. https://doi.org/10.3390/su13020800

Chen, E., Huang, R., Chen, H. S., Tseng, Y. H., & Li, L. Y. (2023, June). GPTutor: a ChatGPT-powered programming tool for code explanation. In International Conference on Artificial Intelligence in Education (pp. 321-327). Springer Nature Switzerland. 

Felder, R., & Silverman, L. (2012). Index of learning styles questionnaire. North Carolina State University. Retrieved from http://www.engr.ncsu.edu/learn ingst yles/ilswe b.html. 

Gharbi, M., Taib Mohtadi, M., & Fal Merkazi, A. (2024). Revolutionizing Moocs with fine-tuned Chatgpt: Personalized Learning At Scale. International Journal of Computing and Digital Systems, 16(1), 1-11. 

Jin, J., & Kim, M. (2023). GPT-Empowered Personalized eLearning System for Programming Languages. Applied Sciences, 13(23), 12773. https://doi.org/10.3390/app132312773

Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-based personalized e-learning systems: Issues, challenges, and solutions. IEEE Access, 10, 81323–81342. https://dx.doi.org/10.1109/ACCESS.2022.3193938

OpenAI (2023). ChatGPT: Optimizing Language Models for Dialogue. https://openai.com/index/chatgpt/

Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W. J., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41-56. http://dx.doi.org/10.37074/jalt.2023.6.1.29

Sayed, W. S., Noeman, A. M., Abdellatif, A., Abdelrazek, M., Badawy, M. G., Hamed, A., & El-Tantawy, S. (2023). AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform. Multimedia Tools and Applications, 82(3), 3303-3333. https://doi.org/10.1007/s11042-022-13076-8