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Effectiveness of Learning Style in Popularity of Personalized Mobile Intelligent Tutoring System from View of Learners

Affiliations

  • Young Researchers and Elite Club, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran, Islamic Republic of
  • Computer department, Faculty of Engineering, Islamic Azad University, Saveh Branch, Saveh, Iran, Islamic Republic of
  • Dean of the Software Engineering and Artificial Intelligence Department, Iran, Islamic Republic of
  • Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran, Islamic Republic of

Abstract


Objectives: Each learner shows unique behavior in educational environments to acquire knowledge and skills. It leads to form variant learning styles. Methods/Analysis: This paper develops a tutoring system called the Personalized Mobile Intelligent Tutoring System (PMITS). The purpose is evaluating the effectiveness of the learning style (according to the Felder-Silverman model) in rate desire users to use from PMITS. In this paper 93 users exercises by PMITS Then a questionnaire was distributed between them, and the results obtained by the software SPSS Version 22.0 and analyzed by Kruskal-Wallis test. Findings: In this analysis p-value=0.000 calculated and the null hypothesis was rejected. Therefore, PMITS popularity was different between users with different learning style; Also, most of the learning style of combined was {Active, Sensing, Visual, Sequential}. Finally, this paper suggests that a PMITS based on learning style is so useful experience to users. Novelty/Improvement: PMITS can support learning styles to provide an effective education system for users (according to the Felder-Silverman model).

Keywords

Felder-silverman Learning Style Model, Intelligent Tutoring System, Learning Style, Kruskal-Wallis Test.

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References


  • Ghadirli HM, Rastgarpour M. An adaptive and intelligent tutor by expert systems for mobile devices. International Journal of Managing Public Sector Information and Communication Technologies. 2012; 3(1).
  • Chang C-Y, Sheu J, Chan T. Concept and design of ad hoc and mobile classrooms. Journal of Computer Assisted Learning. 2003; 19(3):336–46.
  • Özdemir S. Supporting printed books with multimedia: A new way to use mobile technology for learning. British Journal of Educational Technology. 2010; 41(6):135–8.
  • Raiciu T. 190.5 Million cell phones sold - In the second quarter of 2005; 2005.
  • Rogers T. Mobile technologies for informal learning–a theoretical review of the literature. Proceedings of the European Workshop on Mobile and Contextual Learning; 2002.
  • Waycott J. An investigation into the use of mobile computing devices as tools for supporting learning and workplace activities. In 5th Human Centered Technology Postgraduate Workshop (HCT-2001), Brighton, UK; 2001.
  • Magnisalis I, Demetriadis S, Karakostas A. Adaptive and intelligent systems for collaborative learning support: A review of the field. IEEE Transactions on Learning Technologies. 2011; 4(1):5–20.
  • Myneni LS, N. H. Narayanan, Rebello S, Rouinfar A, Pumtambekar S. An interactive and intelligent learning system for physics education. IEEE Transactions on Learning Technologies. 2013; 6(3):228–39.
  • Cetintas S, Si L, Xin YPP, Hord C. Automatic detection of off-task behaviors in intelligent tutoring systems with machine learning techniques. IEEE Transactions on Learning Technologies. 2010; 3(3):228–36.
  • Facer K, Faux F, McFarlane A. Challenges and opportunities: Making mobile learning a reality in schools. Proceeding of mlearn; 2005.
  • Nkambou R, Nguifo EM, Mayers A, Faghihi U. A multi paradigm intelligent tutoring system for robotic arm training. IEEE Transactions on Learning Technologies. 2013; 6(4):364–77.
  • Vahey P, Tater D, Roschelle J. Leveraging handhelds to increase student learning: engaging middle school students with the mathematics of change. 6th International Conference on Learning Sciences; 2004.
  • Brown E, Brailsford TJ, Fisher T, Moore A. Evaluating learning style personalization in adaptive systems: Quantitative methods and approaches. IEEE Transactions on Learning Technologies. 2011; 4(1):5–20.
  • Papanikolaou KA, Mabbott A, Bull S, Grigoriadou M. Designing learner-controlled educational interactions based on learning/cognitive style and learner behavior. Interacting with Computers. 2006; 18(3):356–84.
  • Graf S, Viola SR, Leo T. In-depth analysis of the Felder-Silverman learning style dimensions. Journal of Research on Technology in Education. 2007; 40(1):79.
  • Kuljis J, Liu F. A comparison of learning style theories on the suitability for eLearning. Web Technologies, Applications, and Services. 2005; 191–7.
  • Felder RM, Spurlin J. Applications, reliability and validity of the index of learning styles. International Journal of Engineering Education. 2005; 21(1):103–12.
  • Felder RM, Woods DR, Stice JE, Rugarcia A. The future of engineering education II. Teaching methods that work. Chemical Engineering Education. 2000; 34(1):26–39.
  • Index of Learning Styles Questionnaire [Internet]. [Cited 2015 Oct 09]. Available from: http://www.engr.ncsu.edu/learningstyles/ilsweb.html.
  • Chan Y, Walmsley RP. Learning and understanding the Kruskal-Wallis one-way analysis-of-variance-by-ranks test for differences among three or more independent groups. Physical Therapy. 1997; 77(12):1755–61.

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