Abstract
Evaluation of E-Learning resources plays a significant role in the context of pedagogic systems. Resource evaluation is important in both conventional ‘talk-and-chalk’ teaching and in blended learning. In on-line (e-learning) teaching [an enforced feature of pedagogic systems in tertiary education during the Covid-19 pandemic] the effective evaluation of teaching resources has obtained importance given the lack of ‘face-to-face’ student-teached interaction. Moreover, the enforced use of e-learning has demonstrated the effectiveness of on-line pedagogic systems, which has been argued in blended learning pedagogic systems. Additionally, in e-learning, the lack of ‘face-to-face’ meetings [between teaching staff and students and in staff meetings] makes feedback (positive and negative) important for all actors in the pedagogic system. In this paper we present a novel approach to enable effective evaluation of teaching resources, which provides effective group decision-support designed to evaluate e-learning resources, enhancing students’ satisfaction. The proposed approach employs Picture Fuzzy Sets to quantify survey responses from actors, including: agree, disagree, neutral, and refuse to answer. In our approach, the system can manage the evaluation of e-learning resources based on both explicit and tacit knowledge using a picture fuzzy rule-based approach in which linguistic semantic terms are used to express rules and preferences. The proposed system has been tested using e-learning case studies with the goal of enhancing the learning experience and increasing students' satisfaction. Experimental results demonstrate that our proposed approach achieves a significant improvement in performance in the evaluation of e-learning resources.
Highlights
Overtime, e-learning and m-learning has gained traction as an effective mode of delivery in a broad range of domains and systems as an effective mode of delivery where especially pedagogic systems that can be implemented flexibly anywhere and anytime, especially pedagogic systems (Moore & Pham, 2012). This flexibility: (1) provides a basis for and communication between students and between student and tutors, (2) improves the teaching/learning experience, (3) provides a platform upon which a sense of community can be created, (3) mitigates the isolation often experience by students studying on a distance learning basis, (4) accommodates the three learning styles identified in Stead and Colley (2008) enabling students to learn at their own pace, and (5) provides for improved time management to enable learning and networking at a time and place to suit the students availability to study
(iv) The biggest difference being from “Does mixed training have a positive impact on learning results: knowledge is increased in quantity and better organized, and can lessons be reviewed?” having 28 agree and 0 disagree
In future studies, we propose to focus on the ‘real-time’ learning environment with automated consultant responses to provide advanced technical support using adaptable interfaces designed for web-based, smart phone and other devices which is an essential feature in the developing mobile technology landscape
Summary
E-learning and m-learning (the terms are frequently used interchangeably – hereafter termed e-learning) has gained traction as an effective mode of delivery in a broad range of domains and systems as an effective mode of delivery where especially pedagogic systems that can be implemented flexibly anywhere and anytime, especially pedagogic systems (Moore & Pham, 2012). We present a method designed to provide an effective basis upon which e-learning courses and the related course resources can be assessed and evaluated. A Group Decision-Support in the Evaluation of Pedagogic Systems has quantified sensibilities of learners including positive responses, negative responses, and neutral responses using PFS while studying learning resources online. While the published research has addressed access to education and socio-economic limitations, the open research question is the ability to effectively assess and evaluate the courses and teaching resources. In this paper we present a novel approach to resource evaluation using PFS which is a novel approach introduced in (Cuong, 2014; Garg, 2017), Cuong and Kreinovich (2013) which is a direct extension to the traditional fuzzy set theory (FST) introduced by Lofti Zadeh (Klir & Yuan, 1996; Brown, 1971) and ‘intuitionistic’ fuzzy sets (IFS) which introduced extensions to traditional fuzzy set theory by enabling positive, negative and neutral degrees of membership of a set.
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