IMPROVING ONLINE LEARNING USING DEEP LEARNING AND STUDENT'S INTELLIGENCES.

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Bibliographic Details
Title:	IMPROVING ONLINE LEARNING USING DEEP LEARNING AND STUDENT'S INTELLIGENCES.
Authors:	RAFIQ, Jamal Eddine¹ amal.rafiq-etu@etu.univh2c.ma, ZAKRANI, Abdelali¹ abdelali.zakrani@univh2c.ma, AMRAOUY, Mohammed² amraouy.mohamed1@gmail.com, NOUH, Said³ said.nouh@univh2m.ma, BENNANE, Abdellah² abdellah.bennane@gmail.com
Source:	Turkish Online Journal of Distance Education (TOJDE). Apr2025, Vol. 26 Issue 2, p39-52. 14p.
Subject Terms:	Individualized instruction, Effective teaching, Classroom environment, Academic achievement, *Online education, Deep learning
Abstract:	The emergence of online learning has sparked increased interest in predicting learners' academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aimes to predict learners' performance in online learning settings. This systemic model integrates cognitive, social, emotional, contextual, and normative aspects to predict the learners' performance in online learning environment. This model, based on Competency-Based Learning Traces, takes a holistic approach by integrating various data reflecting knowledge acquisition and skills development. By Taking into account the exchanges among the learners, as well as the interactions with their teachers and the complexity of their online learning environment, the model aims to provide accurate and informed predictions of academic performance. This study provides a detailed overview of the APPMLT-CBT model, its data collection methodology, and discusses its potential implications for online teaching. Results suggest that the model can serve as a robust framework for improving online teaching and learning while offering a deep understanding of the underlying mechanisms of online learning. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	The emergence of online learning has sparked increased interest in predicting learners' academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aimes to predict learners' performance in online learning settings. This systemic model integrates cognitive, social, emotional, contextual, and normative aspects to predict the learners' performance in online learning environment. This model, based on Competency-Based Learning Traces, takes a holistic approach by integrating various data reflecting knowledge acquisition and skills development. By Taking into account the exchanges among the learners, as well as the interactions with their teachers and the complexity of their online learning environment, the model aims to provide accurate and informed predictions of academic performance. This study provides a detailed overview of the APPMLT-CBT model, its data collection methodology, and discusses its potential implications for online teaching. Results suggest that the model can serve as a robust framework for improving online teaching and learning while offering a deep understanding of the underlying mechanisms of online learning. [ABSTRACT FROM AUTHOR]
ISSN:	13026488
DOI:	10.17718/tojde.1477677