Analysis of Student Study Pattern for Personalized Learning using an Innovative Approach

Authors

DOI:

https://doi.org/10.4108/eetiot.6988

Keywords:

Personalized learning, Machine learning, Education, Artificial Intelligence, study improvement application, student study analyser

Abstract

In an era of rapid technological advancements, the area of Artificial Intelligence and Machine Learning (AIML) is revolutionizing the way we learn and interact with technology. However, this influx of information can be over- whelming for students, making it challenging to absorb and retain knowledge within a short timeframe. Learning preferences vary greatly from individual to individual, with some students preferring video tutorials, others favouring hands- on practical experiences, and still others relying on traditional textbooks. To ad- dress this diverse range of learning styles, i.e., a need for an interactive application that provides regular assessments following each lesson, regardless of the chosen learning method. This application would analyse each student's performance to identify their most effective learning approach. This personalized approach is particularly valuable in large coaching institutes, where a limited number of instructors cannot effectively monitor the progress of thousands of students simultaneously. By incorporating additional learning materials and implementing specific adjustments, this application can significantly enhance the learning experiences to students and adult learners alike, empowering them to navigate the complexities of technology with greater confidence and ease.

Downloads

Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">

References

[1] Q. Yang and W. Zhou, "English Online Learning Platform Based on Mobile Android App," 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), Bangalore, India, 2022, pp. 1-5, doi: 10.1109/ICATIECE56365.2022.10 047519. DOI: https://doi.org/10.1109/ICATIECE56365.2022.10047519

[2] Y. Getman et al., "Developing an AI-Assisted Low-Resource Spoken Language Learning App for Children," in IEEE Access, vol. 11, pp. 86025-86037, 2023, doi: 10.1109/ACCESS.2023.3304274 DOI: https://doi.org/10.1109/ACCESS.2023.3304274

[3]. -F. Hsu, C. -M. Chen and D. Cao, "Effects of Design Factors of Game-Based English Vocabulary Learning APP on Learning Performance, Sustained Attention, Emotional State, and Memory Retention," 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), Hamamatsu, Japan, 2017, pp. 661-666, doi: 10.1109/IIAI-AAI.2017.53. DOI: https://doi.org/10.1109/IIAI-AAI.2017.53

[4] S. F. Isamiddinovna, "Mobile Applications as a Modern Means of Learning English," 2019 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2019, pp. 1-5, doi: 10.1109/ICISCT47635.2019.9011897. DOI: https://doi.org/10.1109/ICISCT47635.2019.9011897

[5] S. Ying, "Learn About Vocabulary Learning in Mobile Apps through Web Surveys," 2022 Second International Conference on Advanced Technologies in Intelligent Con- trol, Environment, Computing & Communication Engineering (ICATIECE), Banga- lore, India, 2022, pp. 1-5, doi: 10.1109/ICATIECE56365.2022.10047347. DOI: https://doi.org/10.1109/ICATIECE56365.2022.10047347

[6] X. L. Pham, T. H. Nguyen and G. -D. Chen, "Factors that Impact Quiz Score: A Study with Participants in a Mobile Learning App," 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), Timisoara, Romania, 2017, pp. 103- 105, doi: 10.1109/ICALT.2017.81. DOI: https://doi.org/10.1109/ICALT.2017.81

[7] Basu, Debarati & Lohani, Vinod & Xia, Kang. (2019). Analysis of Students’ Person- alized Learning and Engagement within a Cyberlearning System. 10.18260/1-2-- 32088.

[8] V. N. Rathod, R.H.Goudar, A. Kulkarni, G. M. Dhananjaya and G. S. Hukkeri, "A Survey on E-learning Recommendation Systems for Autistic People.," in IEEE Access, doi: 10.1109/ACCESS.2024.3355589. DOI: https://doi.org/10.1109/ACCESS.2024.3355589

[9] G. S. Hukkeri and R. H. Goudar, "Machine Learning-Based Personalized Recommendation System for E-Learners," 2022 Third International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bengaluru, India, 2022,pp. 1-6, doi: 10.1109/ICSTCEE56972.2022.10100069. DOI: https://doi.org/10.1109/ICSTCEE56972.2022.10100069

Downloads

Published

21-08-2024

How to Cite

[1]
A. Rao, R. Goudar, D. G M, V. Rathod, and A. Kulkarni, “Analysis of Student Study Pattern for Personalized Learning using an Innovative Approach”, EAI Endorsed Trans IoT, vol. 10, Aug. 2024.

Most read articles by the same author(s)