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Type :thesis
Subject :PE English
Main Author :Alglwom, Nu'as Kawther Ibrahim
Title :Multicriteria analysis for the evaluation and benchmarking of english language mobile apps for young learners
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2019
Notes :with CD
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Guest :Click to view PDF file

Abstract : Universiti Pendidikan Sultan Idris
This  study  aimed  to  construct  an  evaluation  and  benchmarking  Decision  Matrix  (DM) based  on  multi-criteria  analysis  for  English  mobile  applications  (E-apps)  for  young  learners in terms of Listening, Speaking, Reading, and Writing (LSRW) skills. The DM was  constructed based on the intersection between evaluation criteria in terms of LSRW and  E-apps  for   young  learners.  The  criteria  were  adopted  from  a  preschool  education curriculum  known   as  the  National  Preschool  Standard  Curriculum  2016  standard.  The data  presented  as  the   DM  include  six  E-apps  as  alternatives  and  17  skills  as  criteria. Thereafter, the six  E-apps were evaluated by distributing a checklist form amongst three English learning lecturers in  early childhood learning department from local university in Perak.  These  apps  were  then   benchmarked  by  utilising  two  experimental  MCDM methods, namely, best–worst method (BWM) and  Technique for Order of Preference by Similarity  to  Ideal  Solution  (TOPSIS).  BWM  is  used  for   weighting  the  evaluation criteria,  whereas  TOPSIS  is  used  to  benchmark  the  apps  and   rank  them  from  best  to worst.  TOPSIS  was  utilized  in  two  decision-making  contexts,   namely  individual  and group contexts. In group decision making, internal and external group  aggregations are applied. For  validating the proposed  DM, objective and subjective methods were  used. The  results  showed  that  (1)  the  integration  of  BWM  and  TOPSIS  was  effective  for  solving benchmarking and ranking problems of E-apps. (2) The ranks of E-apps obtained from internal  and external TOPSIS group decision making were the same, with the first index  app  being   ‘Montessori’  and  the  last  index  app  being  ‘FunWithFlupe’.  (3)  For objective validation,  remarkable differences were observed between the group scores, and they  indicated   that   the    internal   and   external   ranking   results   are   identical.(4)   For subjective  validation,   the  ranking  of  experts   was  exactly  similar  to   the  proposed benchmarking  DM  ranking   results.  As  conclusion,  the  proposed  DM  can  be  used  for evaluation and benchmarking  different E-apps. The implications of this study will benefit (1)  English  language  teachers/designers  for  understanding  how  English  course  content  should be presented; (2) parents for screening and choosing suitable and reliable English learning  apps that will help their children; and (3) kindergarten teachers for choosing an appropriate English app.  

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