Exploring the Potential of Deep Learning for Personalized Learning Environments

Fadhla Binti Junus; Sean P. Brophy

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: Computing and Information Technology Division (CIT) Technical Session 4
Tagged Division: Computing and Information Technology Division (CIT)
Page Count: 23
DOI: 10.18260/1-2--43641
Permanent URL: https://strategy.asee.org/43641
Download Count: 157

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Paper Authors

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Fadhla Binti Junus Purdue University at West Lafayette (PPI) orcid.org/0000-0002-0181-997X

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She was a tenured Lecturer at Information Technology program at the Department of Science and Technology, State Islamic University (UIN) Ar-Raniry, Banda Aceh-Indonesia. Currently, she is a second-year Ph.D. student in the School of Engineering Education at Purdue University, Indiana, USA.

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Sean P. Brophy Purdue University at West Lafayette (COE)

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Dr. Sean Brophy is a learning scientist and engineer interested in designing effective learning environments to engage students' application of knowledge to engineering problem solving. His research in engineering education centers on the potential of technology to support students' conceptual understanding of difficult concepts and their computational abilities.

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Abstract

This study investigates the current potential for artificial intelligence (AI) to support personalized learning (PL). Personalized learning can provide a customized learning environment to support student learning processes based on individual needs, competencies, and interests. One way to conduct personalized learning is by using a recommender system that employs deep learning, an AI technique. To date, a limited number of researchers have discussed the application of deep learning methods to develop advanced recommenders in personalized learning environments. This study examines the literature that describe deep learning as a recommender system to support personalized learning environments. This initial phase of the project seeks to synthesize the issues and opportunities associated with personalized learning experiences and the potential of using deep learning to support the process. Because the topic intersects the education and information technology (IT) fields, we selected three databases for this literature review project: Scopus, ERIC, and Engineering Village. We used the phrase “deep learning recommender system for personalized learning environments” as our search string. We focused only on papers that experts had evaluated in the field to ensure accuracy. Therefore, terms such as “peer review,” “literature review,” and “systematic review” were added to the original search string. The initial search results included 409 documents. After applying inclusion/exclusion criteria, 20 papers emerged as the focus of this study. Thematic analysis was used to look for various themes to identify how deep learning methods are used in education and their potential to inform personalized learning environments. The analysis process utilized Mendeley and NVivo to quickly capture themes by focusing on six features to peruse within the articles. The features involved research questions, goals of studies, research methodology, research design, primary outcomes, and limitations. We then generated three themes from the six features in the analysis phase of the 20 papers, first, regarding the type of study. We grouped the articles into primary and secondary studies. By these categorizations, we could identify the types of studies that employed deep learning methods in the development of recommender systems and their integration with personalized learning. Second, recommender system (RS) techniques mainly emerged in the articles. This theme highlighted the AI methodologies that were most frequently utilized in previous research. And the third theme was a list of e-learning platforms that applied RS for personalized learning. The main findings revealed that the deep learning method was effective in big data analysis due to its ability to forecast students’ achievements, behaviors, and future paths. Therefore, we considered that deep learning could be widely applied as a technique to develop recommender systems to support personalized learning environments. Furthermore, because we found that only a few studies have investigated the implementation of this AI technology, researchers will have a great opportunity to explore deep learning to develop more innovative solutions in educational fields.

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Junus, F. B., & Brophy, S. P. (2023, June), Exploring the Potential of Deep Learning for Personalized Learning Environments Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43641

TY - CPAPER
AB - This study investigates the current potential for artificial intelligence (AI) to support personalized learning (PL). Personalized learning can provide a customized learning environment to support student learning processes based on individual needs, competencies, and interests. One way to conduct personalized learning is by using a recommender system that employs deep learning, an AI technique. To date, a limited number of researchers have discussed the application of deep learning methods to develop advanced recommenders in personalized learning environments. This study examines the literature that describe deep learning as a recommender system to support personalized learning environments. This initial phase of the project seeks to synthesize the issues and opportunities associated with personalized learning experiences and the potential of using deep learning to support the process. Because the topic intersects the education and information technology (IT) fields, we selected three databases for this literature review project: Scopus, ERIC, and Engineering Village. We used the phrase “deep learning recommender system for personalized learning environments” as our search string. We focused only on papers that experts had evaluated in the field to ensure accuracy. Therefore, terms such as “peer review,” “literature review,” and “systematic review” were added to the original search string. The initial search results included 409 documents. After applying inclusion/exclusion criteria, 20 papers emerged as the focus of this study. Thematic analysis was used to look for various themes to identify how deep learning methods are used in education and their potential to inform personalized learning environments. The analysis process utilized Mendeley and NVivo to quickly capture themes by focusing on six features to peruse within the articles. The features involved research questions, goals of studies, research methodology, research design, primary outcomes, and limitations. We then generated three themes from the six features in the analysis phase of the 20 papers, first, regarding the type of study. We grouped the articles into primary and secondary studies. By these categorizations, we could identify the types of studies that employed deep learning methods in the development of recommender systems and their integration with personalized learning. Second, recommender system (RS) techniques mainly emerged in the articles. This theme highlighted the AI methodologies that were most frequently utilized in previous research. And the third theme was a list of e-learning platforms that applied RS for personalized learning. The main findings revealed that the deep learning method was effective in big data analysis due to its ability to forecast students’ achievements, behaviors, and future paths. Therefore, we considered that deep learning could be widely applied as a technique to develop recommender systems to support personalized learning environments. Furthermore, because we found that only a few studies have investigated the implementation of this AI technology, researchers will have a great opportunity to explore deep learning to develop more innovative solutions in educational fields.
AU - Fadhla Binti Junus
AU - Sean P. Brophy
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - Exploring the Potential of Deep Learning for Personalized Learning Environments
UR - https://strategy.asee.org/43641
DO - 10.18260/1-2--43641
ER -