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Modelling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning

Journal Article


Abstract


  • With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) for delivering adapted learning content to mobile learners. The ANFIS model was designed using trial and error based on various experiments. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the adaptation of learning content according to learners' needs. Study results show that ANFIS has been successfully implemented for learning content adaptation within different learning context scenarios. The performance of the ANFIS model was evaluated using standard error measurements which revealed the optimal setting necessary for better predictability. The MATLAB simulation results indicate that the performance of the ANFIS approach is valuable and easy to implement. The study results are based on analysis of different model settings; they confirm that the m-learning application is functional. However, it should be noted that an increase in the number of inputs being considered by the model will increase the system response time, and hence the delay for the mobile learner.

Authors


  •   Al-Hmouz, Ahmed (external author)
  •   Shen, Jun
  •   Al-Hmouz, Rami (external author)
  •   Yan, Jun

Publication Date


  • 2012

Citation


  • Al-Hmouz, A., Shen, J., Al-Hmouz, R. & Yan, J. (2012). Modelling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE Transactions on Learning Technologies, 5 (3), 226-237.

Scopus Eid


  • 2-s2.0-84866111296

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2101

Has Global Citation Frequency


Number Of Pages


  • 11

Start Page


  • 226

End Page


  • 237

Volume


  • 5

Issue


  • 3

Place Of Publication


  • United States

Abstract


  • With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) for delivering adapted learning content to mobile learners. The ANFIS model was designed using trial and error based on various experiments. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the adaptation of learning content according to learners' needs. Study results show that ANFIS has been successfully implemented for learning content adaptation within different learning context scenarios. The performance of the ANFIS model was evaluated using standard error measurements which revealed the optimal setting necessary for better predictability. The MATLAB simulation results indicate that the performance of the ANFIS approach is valuable and easy to implement. The study results are based on analysis of different model settings; they confirm that the m-learning application is functional. However, it should be noted that an increase in the number of inputs being considered by the model will increase the system response time, and hence the delay for the mobile learner.

Authors


  •   Al-Hmouz, Ahmed (external author)
  •   Shen, Jun
  •   Al-Hmouz, Rami (external author)
  •   Yan, Jun

Publication Date


  • 2012

Citation


  • Al-Hmouz, A., Shen, J., Al-Hmouz, R. & Yan, J. (2012). Modelling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE Transactions on Learning Technologies, 5 (3), 226-237.

Scopus Eid


  • 2-s2.0-84866111296

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2101

Has Global Citation Frequency


Number Of Pages


  • 11

Start Page


  • 226

End Page


  • 237

Volume


  • 5

Issue


  • 3

Place Of Publication


  • United States