An intelligent content provider based on students learning style to increase their engagement level and performance

ABSTRACT

Learning trend has been shifted from a conventional way to a digital way in the form of E-learning, but it faces a high dropout ratio. Lack of engagement is one of the primary factors reported for this issue as the same type of course content is presented to learners despite their different background, knowledge and learning styles. Different researchers used adaptive learning techniques to increase students’ performance and engagement, but it has not affected too much for reducing the dropout ratio. One of the major problems with these adaptive techniques is to focus on one thing. To target this issue we have proposed the framework that provides adaptive content to each student based on his learning dimensions and knowledge background as per Bloom’s Taxonomy. The results of the experiment show a significant increase in students’ engagement and performance.

KEYWORDS:

• Adaptive content
• E-learning
• engagement
• learning dimensions
• Bloom’s Taxonomy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Yousra Yousaf

Miss Yousra Yousaf is a Postgraduate student of computer science at Computer Science and Engineering Department at the University of Engineering and Technology, Lahore. Her research interests include Adaptive eLearning Systems.

Muhammad Shoaib

Dr Muhammad Shoaib is a professor at the Computer Science and Engineering Department at the University of Engineering and Technology Lahore, Pakistan. He received his MSc in computer science from Islamia University, Pakistan. He has completed his PhD at the University of Engineering and Technology, Pakistan in 2006. His Post Doc. is from Florida Atlantic University, USA, in 2009. His current research interests include information retrieval systems, information systems, software engineering and the semantic web.

Muhammad Awais Hassan

Dr Muhammad Awais is a Gold Medalist of Punjab University in MCS computer science. He has completed his PhD Computer Science from the University of Engineering and Technology, Lahore, Pakistan. He is currently working as an assistant professor at Computer Science and Engineering Department at the University of Engineering and Technology. His research interest includes Artificial Intelligence, Reinforcement Learning, Adaptive eLearning Systems, and Affective Computing.

Ume Habiba

Miss Ume Habiba is a Postgraduate student of computer science at the Computer Science and Engineering Department at the University of Engineering and Technology, Lahore, Pakistan. She is also working as a research assistant in UET Lahore. Her research interests include Gamification and adaptive learning.