TY - JOUR
T1 - An extractive summarization for utilizing learning content using deep learning algorithm
T2 - Proposed framework and implementation
AU - AlRoshdi, Yusra
AU - AlBadawi, Mohammed
AU - Alhamadani, Abdullah
AU - Sarrab, Mohamed
N1 - Publisher Copyright:
© 2023 University of Bahrain. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Education has always been a critical factor in the long-Term economic development of any society. Most educational institutions use Learning Management Systems (LMSs) to manage and organize students' learning content. These systems contain many learning materials related to a specific topic or course in different formats, such as documents, HTML pages, videos, figures, etc. However, the enormous amount of information in these materials makes it difficult for students to get what they need according to the course objectives. Therefore, summarization techniques could be one way to facilitate the learning process and provide essential content. Therefore, there is a need to summarize the learning content of the course in the guidance of the course outline. Consequently, it is important to investigate how to summarize learning content to enhance and increase students' achievement. This paper proposes a framework for a Guided Extractive Summarization of the Learning Content (GESLC). The main contribution is proposing and developing a novel framework combining several deep learning algorithms to provide efficient summarization techniques to summarize the learning content according to the course outline. Several methods are utilized in this study to evaluate the proposed Framework. As we contribute, the evaluation process shows better results in guiding instructors or students to summarize learning content according to the course objectives to finally have a perfect summary matching the learning process's objectives and enhancing the students' achievement.
AB - Education has always been a critical factor in the long-Term economic development of any society. Most educational institutions use Learning Management Systems (LMSs) to manage and organize students' learning content. These systems contain many learning materials related to a specific topic or course in different formats, such as documents, HTML pages, videos, figures, etc. However, the enormous amount of information in these materials makes it difficult for students to get what they need according to the course objectives. Therefore, summarization techniques could be one way to facilitate the learning process and provide essential content. Therefore, there is a need to summarize the learning content of the course in the guidance of the course outline. Consequently, it is important to investigate how to summarize learning content to enhance and increase students' achievement. This paper proposes a framework for a Guided Extractive Summarization of the Learning Content (GESLC). The main contribution is proposing and developing a novel framework combining several deep learning algorithms to provide efficient summarization techniques to summarize the learning content according to the course outline. Several methods are utilized in this study to evaluate the proposed Framework. As we contribute, the evaluation process shows better results in guiding instructors or students to summarize learning content according to the course objectives to finally have a perfect summary matching the learning process's objectives and enhancing the students' achievement.
KW - Course outline
KW - Extractive Summarization
KW - Knowledge Dissemination
KW - Learning Content
KW - Restricted Boltzmann Machine
KW - Summarization
UR - http://www.scopus.com/inward/record.url?scp=85152910478&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85152910478&partnerID=8YFLogxK
U2 - 10.12785/ijcds/130138
DO - 10.12785/ijcds/130138
M3 - Article
AN - SCOPUS:85152910478
SN - 2210-142X
VL - 13
SP - 461
EP - 474
JO - International Journal of Computing and Digital Systems
JF - International Journal of Computing and Digital Systems
IS - 1
ER -