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An Investigation of Learner Characteristics that Use Massive Open Online Courses (MOOC) in Learning Second Language

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Intelligent and Interactive Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 67))

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Abstract

Learner characteristics are an essential element, in order to design and create an instructional platform/education/assessment according/customized to the target group. In this paper, types of learner characteristics that were using Massive Open Online Courses (MOOC) in the second language were discussed. Literature have emphasized on learner characteristic roles in enhancing learning. A mixed-method approach which involved both quantitative and qualitative research methods was adopted in this study. The results from the survey analysis revealed that the highest dimension among the eight learning styles was the visual learner at 76% while the result from the interview session was also visual (24.24%). The results from the survey analysis revealed that the highest dimension among the eight cognitive styles was thinking learner at 70%, while the result from the interview session was also thinking (21.95%). This study aimed to investigate learner characteristics using a Mandarin Massive Open Online Course (MOOC). This study determines that there are twofold: (i) learning styles of learners that used Mandarin MOOC, (ii) cognitive styles of learners that used Mandarin MOOC. Finding of study aimed to propose a content development model in MOOC based on learner characteristics. Future study will propose an effectiveness assessment model in MOOC based on learner characteristics.

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References

  1. Park Y, Jung I, Reeves TC (2015) Learning from MOOCs: a qualitative case study from the learners’ perspectives. EMI Educ Media Int 52(2):72–87

    Article  Google Scholar 

  2. Amoozegar A, Mohd Daud S, Mahmud R, Ab Jalil H (2018) Exploring learner to institutional factors and learner characteristics as a success factor in distance learning. Int J Innov Res Educ Sci 4(6):2349–5219

    Google Scholar 

  3. Hone KS, El Said GR (2016) Exploring the factors affecting MOOC retention: a survey study. Comput Educ 98:157–168

    Article  Google Scholar 

  4. Pursel BK, Zhang L, Jablokow KW, Choi GW, Velegol D (2016) Understanding MOOC students: motivations and behaviours indicative of MOOC completion. J Comput Assist Learn 32(3):202–217

    Article  Google Scholar 

  5. Lever-Duffy J, McDonald JB (2009) Teaching and learning with technology, 3rd edn. Pearson, Boston

    Google Scholar 

  6. Zhao E, Guo C, Liu L (2017) The construction of MOOC’ s curriculum evaluation system model in China. In: Annual international conference on modern education and social science (MESS 2017), 2017, no. Mess, pp 268–275

    Google Scholar 

  7. Al-azawei A, Parslow P, Lundqvist K (2017) Investigating the effect of learning styles in a blended e-learning system: an extension of the technology acceptance model (TAM). Australas J Educ Technol 33(2):1–23

    Google Scholar 

  8. Baukal CE, Ausburn LJ (2016) Multimedia category preferences of working engineers. Eur J Eng Educ 41(5):482–503

    Article  Google Scholar 

  9. Baukal CE, Ausburn LJ (2016) Relationship of prior knowledge and working engineers’ learning preferences: implications for designing effective instruction. Eur J Eng Educ 3797(April):1–21

    Google Scholar 

  10. Labib AE, Canós JH, Penadés MC (2017) On the way to learning style models integration: a learner’s characteristics ontology. Comput Human Behav 73:433–445

    Article  Google Scholar 

  11. De Paepe L, Zhu C, Depryck K (2018) Learner characteristics, learner achievement and time investment in online courses for Dutch L2 in adult education. Turkish Online J Educ Technol 17(1):101–112

    Google Scholar 

  12. Sadhasivam J, Babu R (2017) MOOC: a framework for learners using learning style. Int Educ Res J 3(2):21–24

    Google Scholar 

  13. Ali R (2016) Learning style construct in student’ s learning. J Indones untuk Kaji Pendidik 1(2):213–222

    Google Scholar 

  14. Abante MER, Almendral BC, Manansala JE, Mañibo J (2014) Learning styles and factors affecting the learning of general engineering students. Int J Acad Res Progress Educ Dev 3(1):16–27

    Google Scholar 

  15. Hmedna B, El Mezouary A, Baz O, Mammass D (2016) A machine learning approach to identify and track learning styles in MOOCs. In: International conference on multimedia computing and systems (ICMCS’16)

    Google Scholar 

  16. Fasihuddin H, Skinner G, Athauda R (2017) Towards adaptive open learning environments: evaluating the precision of identifying learning styles by tracking learners’ behaviours. Educ Inf Technol 22(3):807–825

    Article  Google Scholar 

  17. Mohamad SNM (2014) Model for online teaching tools based on interpersonal, visual and verbal intelligence. Universiti Teknikal Malaysia Melaka

    Google Scholar 

  18. Rohaniyah J (2017) Integrating learning style and multiple intelligences in teaching and learning process. J Pemikir Penelit Pendidik dan Sains 5(1):19–27

    Google Scholar 

  19. Simuth J, Sarmany-Schuller I (2014) Cognitive style variable in E-learning. Procedia—Soc Behav Sci 116:1464–1467

    Article  Google Scholar 

  20. Jablokow K, Defranco JF, Jablokow K, Defranco JF, Richmond SS, Piovoso MJ, Bil SG (July 2015) Cognitive style and concept mapping performance

    Google Scholar 

  21. Mealor AD, Simner J, Rothen N, Carmichael DA, Ward J (2016) Different dimensions of cognitive style in typical and atypical cognition: new evidence and a new measurement tool. PLoS ONE 11(5):1–21

    Article  Google Scholar 

  22. Ancona K, Scully VM, Westney, “Managing For the Future,” 1997. [Online]. Available: http://www.analytictech.com/mb021/cogquest.htm. Accessed 30 Apr 2017

  23. Mukherjee S (2016) Learning style of humanities, commerce and science students: a study on higher secondary students from West Bengal. Int J Indian Psychol 3(3):20–29

    Google Scholar 

  24. Kintakaningrum T (2012) A study of consequences on individual and group learning performance using a web-based and mobile supported learning management system. Universiti Teknikal Malaysia Melaka, Malaysia

    Google Scholar 

  25. Nicholas JS, Francis FS (2016) MOOCs and the need for POOCs. Int J Mod Comput Sci 4(4):211–219

    Google Scholar 

  26. Koc-Januchta M, Hoffler T, Thoma G-B, Prechtl H, Leutner D (2017) Visualizers versus verbalizers: effects of cognitive style on learning with texts and pictures—an eye-tracking study. Comput Human Behav 68:170–179

    Article  Google Scholar 

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Acknowledgements

This research is conducted by the Pervasive Computing & Educational Technology Research Group, C-ACT, Universiti Teknikal Malaysia Melaka (UTeM), and supported by the Ministry of Education (MOE). FRGS grant: FRGS/1/2016/ICT01/FTMK-C-ACT/F00327.

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Authors and Affiliations

  1. Pervasive Computing and Educational Technology (PET) Research Group, Centre for Advanced Computing Technology (C-ACT), Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia

    Hasmaini Hashim, Sazilah Salam & Siti Nurul Mahfuzah Mohamad

Authors
  1. Hasmaini Hashim
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  2. Sazilah Salam
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  3. Siti Nurul Mahfuzah Mohamad
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Corresponding author

Correspondence to Hasmaini Hashim .

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Editors and Affiliations

  1. Department of Computer Science, University of Milan, Milan, Italy

    Vincenzo Piuri

  2. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India

    Samarjeet Borah

  4. Department of Intelligent Computing and Analytics, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Sharifah Sakinah Syed Ahmad

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Hashim, H., Salam, S., Mohamad, S.N.M. (2019). An Investigation of Learner Characteristics that Use Massive Open Online Courses (MOOC) in Learning Second Language. In: Piuri, V., Balas, V., Borah, S., Syed Ahmad, S. (eds) Intelligent and Interactive Computing. Lecture Notes in Networks and Systems, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-13-6031-2_7

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