ABSTRACT
This paper presents a novel and comprehensive classification mechanism that groups numerous dimensions associated with group decision-making approaches. We identify three broad categorizations of group decision-making literature. The classified dimensions are clustered by whether they are intrinsic to the group of participants, the nature of the addressed problems (topics), or a choice in the decision process design. We highlight the unique challenges facing manual group decision-making and those linked with intelligent support for group decision-making. Manual rules governing traditional face-to-face meetings must be adapted to suit virtual meetings where co-decisions must be made electronically. Virtual group decision-making for large groups facilitated by the extensive use of social media tools is an emergent area that has proven to be a necessity that has had a tremendous influence on society for the whole world during the recent Covid-19 pandemic. We first present the associated challenges, followed by the potential solution of computer-based group decision-making systems. We further discuss binary and multi-features of group decision approaches with justifications and examples. Communication has been identified as the main bottleneck, and various attacks have been revealed. This research opens up several aspects of group decision-making that could be further studied. For example, a cluster of dimensions concerns the truthfulness of the information exchanged and how untruthfulness is handled, starting with detection and how to react to detected lies. The scale of technology-supported group decision-making has grown to the point where its influence has been accused or lauded in the last few US elections. There is a persistent call for properties such as transparency and fairness in group decision-making systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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. Copyright © International Journal of Mathematical, Engineering and Management Sciences.
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.