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Student-centered learning (SCL) is considered the key to providing enhanced knowledge transfer and plays a significant role in the construction of a cordial teacher-student relationship. Since the inception of SCL theories (Renate, 2008; Jacob, 2016) the educational psychologists were highly concerned about fully accommodating and responding to the student’s needs and preferences, and allow them to actively participate in deciding the best suitable pedagogy for students, especially with learning disabilities. Due to the inter-dependence of physical, intellectual, psychological and social needs, teaching method selection is a multiple criteria group decision problem. In the group decision-making process, due to the inherently imprecise and uncertainty of the decision maker, it is difficult to determine the criterion or attribute value with a precise equivalent (Yang, Hu, An & Chen, 2017). One of the major challenges in student-centred group decision-making is the determination of the weight of each decision-maker (DM). The next major concern is to represent the view and evaluations of different DMs in linguistic terms. These challenges motivated the development of a hybrid model that employs fruit fly optimization algorithm to evaluate the weights of the DMs and an interval type-2 trapezoidal fuzzy numbers (ITrT2FNs) to represent the fuzziness in the difference in opinions of DMs.
The correctness of the DM’s influence has a high impact on the final results of the group decision-making process as each DM comes from a different professional domain, with varying levels of knowledge, skills, personality, ethics, and values. In particular, each DM looks upon the learning of the student from a different perspective in the student-centred learning environment which signifies that each person who takes part in the decision-making will have different views and thoughts and have different influences on integrated results. Therefore, it is of prime importance to measure the weight of DMs. The concept of linguistic variables used in decision making correspond to different fuzzy sets, such as triangular fuzzy numbers, intuitionistic fuzzy numbers, interval type-2 fuzzy sets, etc. (Pang & Liang, 2012). The type 2 fuzzy sets (T2FSs) is a form of an extension of the traditional fuzzy set theory. In T2FS the degree of membership defined by the membership function of each element is a fuzzy set rather than a real number. In comparison with the traditional fuzzy set, the fuzzy uncertainty associated with the linguistic evaluation term sets can be described more effectively by T2FSs. The T2FSs is capable of representing the views of decision makers effectively, but the computational complexity is high. Fuzzy numbers are the primary information carriers in fuzzy set theory thereby resulting in many enhanced forms of FNs were developed. However, for the users, it is not easy to express preferences through the type-2 fuzzy numbers. For this reason, other researchers proposed qualitative models that do not need semantics represented using fuzzy numbers, for example, multi-granular linguistic modeling, unbalanced linguistic modeling, 2-tuple linguistic modeling (Zhang, Zhang & Wang, 2017). The relative simplicity in the calculation of the interval type-2 trapezoidal fuzzy numbers (ITrT2FNs) resulted in more usage than the T2FSs (Zhang, Zhou, Wu & Li, 2017). Compared to other fuzzy sets, interval trapezoidal type-2 fuzzy numbers (ITrT2FNs) have considerable flexibility to present the uncertainties and inaccuracies of linguistic variables in the practical and student-centred learning application. Apart from the greater ability of ITrT2FNs to deal with uncertainty than traditional fuzzy sets, it is also less computationally expensive compared to T2FS. For SCL, group decision making requires quantifying the DM’s judgments as precise as possible (Abdullah & Zulkifl, 2015). In comparison with the linguistic modeling that directly processes linguistic evaluation information, the benefit of ITrT2FNs is evident for the linguistic term sets with the similar granularity and symmetrical form (Hamdani, 2017; Whelan, 2017).