AHP-based evaluation model for context-aware mobile learning system

Chih Chien Kao, Po Sheng Chiu, Tzung Shi Chen, Ting-Wei Hou

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

There has been a dramatic growth of research concerned about the mobile learning (m-learning) in recent years. It has an urgent need that M-learning systems have to be continuously evaluated and improved for ensuring the system reliability. However, there are only few or none of existent m-learning system evaluation researches. Therefore, this study develops a m-learning evaluation method supporting meaningful learning. According to the principles of meaningful learning and m-learning, the study blends the characters of them to form five dimensions and ten criteria based on which the evaluation method is developed. The evaluation method adopts both the statistics and the analytic hierarchy process (AHP) to check the degree of meaningful learning that includes overall and inner aspects. Initially, the statistics approach can be treated as entirety analysis to develop a scale of meaningful learning for m-learning which can present the overall degree of meaningful learning. Subsequently, the AHP method can be an inner inspection to collect learners' opinions after practicing learning activities and then the results could reveal the related importance of functions in m-learning. According to the overall and inner analysis, m-learning system developers and designers can realize the strength and weakness of the m-learning system. Consequently, existing m-learning systems can be reassessed by our evaluation method and m-learning system can be improved toward the meaningful learning according to the results of the evaluation method.

Original languageEnglish
Pages (from-to)267-275
Number of pages9
JournalJournal of Internet Technology
Volume16
Issue number2
DOIs
Publication statusPublished - 2015 Jan 1

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

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