A Collaborative Framework for Customized E-Learning Services by Analytic Hierarchy Processing

Abstract

Thanks to the drastic proliferation of the Internet, e-learning has been recognized as an effective medium for various kinds of aggressive learners. However, due to the deficiencies of tutoring and guiding functionalities in current learning platforms, casual learners may deviate from the original course direction with frustration, when confronting inflexible course materials and fixed learning models. In the post-COVID-19 era, we believe that the most important functionality for a personal learning environment (PLE) to offer is a course recommendation process which adaptively provides a versatile course combination scheme for different learners from different perspectives. In this paper, we propose a flexible framework for users to customize their e-learning environment based on a two-stage Analytical Hierarchical Processing (AHP) structure for building adaptive course portfolios, which adaptively provides a versatile course scheme for different learners. The main objective of our framework is to transform a learner from a role of passively accepting the course content organized by instructors, into another role of proactively selecting the courses and contributing their knowledge to continuously improve the learning platform. We believe the approach proposed is a versatile way for supporting various challenges for the next generation of personal e-learning environment.

Generation of a Learning Path in E-Learning Environments: Literature Review

“…According to the authors Tseng et al (2022), with proper storage of learning objects, users can directly track topics based on some criteria in the time dimension, which supports a flexible, personalised e-learning environment.…”

“…The need to adapt training materials is becoming increasingly evident. Including professional teaching/education principles and an appropriate curriculum, such an e-learning environment can support extremely high-quality, student-centred educational programs for remote learners using the synchronous and asynchronous tools available to Internet technologies communications (Tseng et al, 2022). A fruitful e-learning infrastructure should be equipped with the ability to absorb the learning experience of users (Safitri et al, 2022) and use this information to recommend random users based on their experience and requirements and use AI to assess knowledge for educational researchers and practitioners and develop student models on various types of data with their challenges and potential solutions for a future ecosystem of adaptive e-learning environments (Minn, 2022).…”

“…The implementation of cluster analysis using the K-means clustering algorithm can show the learning patterns of student groups formed based on access. Tseng et al (2022) propose integrating computerised learning objects and creating specifications that allow multiple instructors to collaborate to develop valuable and reconfigurable learning content. The importance of indexing learning objects into document warehouses to support text-centric business intelligence and propose the architecture for the next-generation e-learning environment.…”

Towards the Achievement of the Sustainable Development Goals through Engineering Training for Labor Market Reintegration of Older Workers

“…The traditional teaching model is replaced by one with a more professional approach. Students acquire a relevant role and are encouraged to actively participate [20], promoting a shift in teaching methodologies towards student-centered learning according to the Leuven and Louvain-la-Neuve Ministerial Communiqué, the Bucharest Ministerial Communiqué, and the European Commission's Communication on Rethinking Education [21] in a context that extends throughout life to achieve increased competitiveness [22]. This implies that students acquire skills and competencies based on real-world problems [23], which implies that the teacher must have a strong professional orientation in their way of teaching [24].…”