Abstract
The current generation is much accustomed to new technologies which enable them to perform many activities online. More importantly, these technologies have been used by students even for learning. In this research we focused on student initiated learning online. Because students have control over their own learning, they are not bounded by a syllabus or a specific learning task. Apart from learning related activities however, it is possible for them to engage in non-learning related activities. From the data gathered, the k-Means algorithm was used to discover five behaviors exhibited by students as they learned online relative to how they transitioned between viewing learning and non-learning related websites. Since emotion was previously reported to have an effect on how students learned online, differences in emotion transitions for each of the online learning behaviors were also observed. The analysis of these transitions provided possible reasons for why students exhibited these behaviors. Possible interventions were then suggested which can be used for supporting students as they learn online. Systems can later be developed to utilize the developed model for predicting the type of behavior exhibited by a student and provide appropriate support mechanisms for their learning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Craig, S.D., Graesser, A.C., Sullins, J., Gholson, B.: Affect and learning: An exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media 29(3), 241–250 (2004)
Inventado, P.S., Legaspi, R., Suarez, M., Numao, M.: Investigating transitions in affect and activities for online learning interventions. In: Proceedings of the 19th Conference on Computers in Education, Chiang Mai, Thailand, pp. 571–578 (December 2011)
Kort, B., Reilly, R., Picard, R.W.: External representation of learning process and domain knowledge: affective state as a determinate of its structure and function. In: Artificial Intelligence in Education, San Anotonio, Texas, pp. 64–69 (May 2001)
Morris, L., Finnegan, C., Wu, S.: Tracking student behavior, persistence, and achievement in online courses. The Internet and Higher Education 8(3), 221–231 (2005)
Prensky, M.: Digital natives, digital immigrants. On the Horizon 9(5), 1–6 (2001)
Roy, M., Chi, M.T.H.: Gender differences in patterns of searching the web. J. Educational Computing Research 29, 335–348 (2003)
Smith, S.D., Caruso, J.B.: ECAR study of undergraduate students and information technology, 2010 (research study, vol. 6). Tech. rep., EDUCAUSE Center for Applied Research, Boulder, CO (October 2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Tokyo
About this paper
Cite this paper
Inventado, P.S., Legaspi, R., Suarez, M., Numao, M. (2012). Categorizing and Comparing Behaviors of Students Engaged in Self-initiated Learning Online. In: Nishizaki, Sy., Numao, M., Caro, J., Suarez, M.T. (eds) Theory and Practice of Computation. Proceedings in Information and Communications Technology, vol 5. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54106-6_11
Download citation
DOI: https://doi.org/10.1007/978-4-431-54106-6_11
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-54105-9
Online ISBN: 978-4-431-54106-6
eBook Packages: Computer ScienceComputer Science (R0)