A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Abstract

Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.

Relationship Between Academic Self-Efficacy and Cognitive Load for Students in Distance Learning

“…This study confirms that subjective cognitive load is mainly associated with the achievement of educational goals in the contexts of distance learning (Zhampeissova et al, 2020). The cognitive load has been recognised as a significant factor that influences achievement results in the digital environment learning processes (Al- Hunaiyyan et al, 2017). This study's observed results could be attributed to the idea that educational programs in a remote learning environment are more effective if the cognitive load is optimal (Curum & Khedo, 2020).…”

The Effects of Adaptive Feedback on Student’s Learning Gains

“…The purpose of the adaptive feedback algorithm is to determine the appropriate feedback for different students based on adaptive characteristics [23]. The input to the adaptive feedback algorithm is the partial problem solving state P S s .…”

Academic Performance and Cognitive Load in Mobile Learning

“…The cognitive approach places a student and internal mental processes at the center of learning. Due to the fact that cognitivism seeks to identify various processes associated with the acquisition of knowledge and thus to provide strategies that support student learning, educators can use cognitive approaches to help students effectively achieve their educational goals [20].…”

“…Distance learning complicates the assessment process and forces educational institutions to consider the technical aspects of mobile learning, social norms, and pedagogical theories, including learning strategies, learning outcomes, engagement, and collaboration. Therefore, the development and implementation of mobile learning technologies should be integrated with traditional education aspects to ensure the sustainability of the learning system on mobile basis [20].…”