Design of personalised m-learning curriculum implementation model for diploma in hospitality management / R Moganadass Ramalingam
Personalised m-learning allows learner to create learning experience around his mobile devices by tailoring learning materials according to his demand. This could be possible by incorporating personalised m-learning into formal education to assist students to fulfil their learning needs and learning...
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| Format: | Thesis |
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2020
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| Online Access: | http://studentsrepo.um.edu.my/14579/1/R_Moganadass.pdf http://studentsrepo.um.edu.my/14579/2/R_Moganadass.pdf http://studentsrepo.um.edu.my/14579/ |
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| Summary: | Personalised m-learning allows learner to create learning experience around his mobile devices by tailoring learning materials according to his demand. This could be possible by incorporating personalised m-learning into formal education to assist students to fulfil their learning needs and learning outcomes. Therefore, this study was conducted to develop a personalised m-learning curriculum implementation model for students enrolled in Food and Beverage Service course in their diploma in hospitality programme. This study employed the Design and Development Research (DDR) approach. The Needs Analysis phases is the first phase which aimed to investigate problems and justifications for developing the personalised m-learning curriculum implementation model for Food and Beverage Service course in diploma in hospitality programme at a private higher education institution in Malaysia. The instrument used for this phase was a need analysis survey questionnaire which was constructed based on Unified Theory of Acceptance and Use of Technology (UTAUT). The survey was conducted among fifty (50) students enrolled in Food and Beverage Service course to get their feedback on the current situation of their learning and what they expected from the implementation of personalised m-learning in this course. The second phase was the development phase which adopted Nominal Group Technique (NGT) and Interpretive structural modeling (ISM) techniques to develop the proposed model. There were 31 personalised m-learning elements finalised through NGT process. The outcomes of this phase was the experts' view on personalised m-learning elements and the relationship among these elements. The third phase is the evaluation phase where it focuses on the suitability of the model to support formal face-to-face classroom teaching and learning. This phase employed a modified Fuzzy Delphi Method (FDM) to analyse a five-linguistic scale evaluation survey questionnaire to get consensus views and opinions from 25 selected panel of experts. The experts' consensus for questionnaire items determined by the threshold value 'd' while defuzzification (Amax) values for the items used to determine the agreement of the experts. The findings from Phase 1 showed the need to develop personalised m-learning model whereas the outcomes from Phase 2 was the development of the model. The experts proposed that the final ISM model for personalised m-learning to be divided into three domains: Device Adaptation domain, Learner Adaptation domain and Situated Adaptation domain. Based on elements' driving power and dependence power, the personalised m-learning elements are further classified according to clusters using MICMAC analysis. These four clusters are Autonomous elements; Dependent elements; Linkage elements; and Independent elements. The findings from Phase 3 revealed that all experts consensually agreed with the evaluation's questionnaire items. This can be viewed from the triangular fuzzy number and deffuzification process which shows that all the items have met the requirements needed. Finally, the findings of the study can be used as a guideline when implementing personalised m-learning in formal teaching and learning process.
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