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Year 2018, Volume: 19 Issue: 1, 18 - 30, 01.01.2018
https://doi.org/10.17718/tojde.382655

Abstract

References

  • Armstrong, T. (2009). Multiple intelligences in the classroom. Alexandria, Virginia: Association for Supervision and Curriculum Development (ASCD). Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267-270). ACM. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning Analytics (pp. 61-75). Springer: New York. Blikstein, P. (2013, April). Multimodal learning analytics. In Proceedings of the third international conference on learning analytics and knowledge (pp. 102-106). ACM. Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220-238. http://dx.doi.org/10.18608/jla.2016.32.11. Barrington, E. (2004). Teaching to student diversity in higher education: how Multiple Intelligence Theory can help. Teaching in Higher Education, 9 (4), 421-434. http://dx.doi.org/10.1080/1356251042000252363 Campbell, L., Campbell, B., & Dickinson, D. (1999). Through multiple intelligences. Needham Heights, MA: Allyn & Bacon. Campbell, J. & Oblinger, D. (2007). Academic Analytics. EDUCAUSE Quarterly. October (2007). Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thus, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318- 331. http://dx.doi.org/10.1504/IJTEL.2012.051815 Clow, D. (2012, April). The learning analytics cycle: closing the loop effectively. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 134- 138). ACM. Cooper, A. (2012). What is analytics? Definition and essential characteristics. Cetis Analytics Series, 1(5).Retrieved from http://citeseerx.ist.psu.edu

Facilitating Multiple Intelligences Through Multimodal Learning Analytics

Year 2018, Volume: 19 Issue: 1, 18 - 30, 01.01.2018
https://doi.org/10.17718/tojde.382655

Abstract

This paper develops a theoretical framework for employing learning analytics in online education to trace multiple learning variations of online students by considering their potential of being multiple intelligences based on Howard Gardner’s 1983 theory of multiple intelligences. The study first emphasizes the need to facilitate students as multiple intelligences by online education systems and then suggests a framework of the advanced form of learning analytics i.e., multimodal learning analytics for tracing and facilitating multiple intelligences while they are engaged in online ubiquitous learning. As multimodal learning analytics is still an evolving area, it poses many challenges for technologists, educationists as well as organizational managers. Learning analytics make machines meet humans, therefore, the educationists with an expertise in learning theories can help technologists devise latest technological methods for multimodal learning analytics and organizational managers can implement them for the improvement of online education. Therefore, a careful instructional design based on a deep understanding of students’ learning abilities, is required to develop teaching plans and technological possibilities for monitoring students’ learning paths. This is how learning analytics can help design an adaptive instructional design based on a quick analysis of the data gathered. Based on that analysis, the academicians can critically reflect upon the quick or delayed implementation of the existing instructional design based on students’ cognitive abilities or even about the single or double loop learning design. The researcher concludes that the online education is multimodal in nature, has the capacity to endorse multiliteracies and, therefore, multiple intelligences can be tracked and facilitated through multimodal learning analytics in an online mode. However, online teachers’ training both in technological implementations and adapting educational theories to online education is necessary to achieve this ideal.

References

  • Armstrong, T. (2009). Multiple intelligences in the classroom. Alexandria, Virginia: Association for Supervision and Curriculum Development (ASCD). Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267-270). ACM. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning Analytics (pp. 61-75). Springer: New York. Blikstein, P. (2013, April). Multimodal learning analytics. In Proceedings of the third international conference on learning analytics and knowledge (pp. 102-106). ACM. Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220-238. http://dx.doi.org/10.18608/jla.2016.32.11. Barrington, E. (2004). Teaching to student diversity in higher education: how Multiple Intelligence Theory can help. Teaching in Higher Education, 9 (4), 421-434. http://dx.doi.org/10.1080/1356251042000252363 Campbell, L., Campbell, B., & Dickinson, D. (1999). Through multiple intelligences. Needham Heights, MA: Allyn & Bacon. Campbell, J. & Oblinger, D. (2007). Academic Analytics. EDUCAUSE Quarterly. October (2007). Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thus, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318- 331. http://dx.doi.org/10.1504/IJTEL.2012.051815 Clow, D. (2012, April). The learning analytics cycle: closing the loop effectively. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 134- 138). ACM. Cooper, A. (2012). What is analytics? Definition and essential characteristics. Cetis Analytics Series, 1(5).Retrieved from http://citeseerx.ist.psu.edu
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Details

Journal Section Articles
Authors

Ayesha Perveen This is me

Publication Date January 1, 2018
Submission Date July 11, 2017
Published in Issue Year 2018 Volume: 19 Issue: 1

Cite

APA Perveen, A. (2018). Facilitating Multiple Intelligences Through Multimodal Learning Analytics. Turkish Online Journal of Distance Education, 19(1), 18-30. https://doi.org/10.17718/tojde.382655