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Related Experiment Video

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Smart education system to improve the learning system with CBR based recommendation system using IoT.

Veeramanickam M R M^1,2, Manisha Sachin Dabade³, Sita Rama Murty P⁴

¹Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.

|August 4, 2023

Summary

This study enhances smart tutoring systems (STS) using IoT and Case-Based Reasoning (CBR) for personalized e-learning. The CBR model significantly improved learner performance, especially for slow learners, by recommending tailored online resources.

Keywords:

Artificial intelligence Internet of things Recommendation system Smart learning techniques

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Area of Science:

Intelligent learning systems
E-learning technologies
Educational data mining

Background:

Smart Tutoring Systems (STS) are increasingly used in e-learning to improve learning processes.
Personalized learning models are crucial, driven by learner engagement and specific requirements.
Existing systems need optimization for diverse learner needs and engagement levels.

Purpose of the Study:

To design and evaluate an IoT-based personalized learning system utilizing Case-Based Reasoning (CBR).
To improve learner engagement and performance by customizing learning paths based on individual needs.
To focus on developing an STS recommendation model specifically for slow learners.

Main Methods:

Developed an IoT-based personalized learning system incorporating learner requirements, search history, experience, and proficiency.
Implemented a Case-Based Reasoning (CBR) classifier-based search model for outcome analysis.
Analyzed learner performance using quiz assessments before and after personalized learning interventions.
Evaluated the recommendation model's performance using Root Mean Square Error (RMSE) across different group sizes.

Main Results:

Learner performance significantly increased after using the CBR-based personalized learning model, with response rates rising from 42.57% to 74.82%.
The CBR model demonstrated effectiveness in recommending suitable online learning resources tailored to individual learner needs.
The recommendation model showed lower RMSE (10-20%) for a group size of 550 students, indicating better performance compared to a group size of 1600 (24% RMSE).

Conclusions:

The proposed IoT-based STS with a CBR recommendation model effectively enhances personalized e-learning experiences.
The system shows particular promise in supporting slow learners by identifying and recommending appropriate online learning resources.
Further research can optimize the recommendation model for larger student groups to improve performance consistency.