Abstract
Learning activities and environments have changed dramatically in the last 50 years, in large part due to information and communications technologies. New technologies make it possible to create a smart learning engine in the form of an intelligent personalized learning system that can be integrated with a variety of interaction devices. In addition, on the instructional design and personalization/customization side of things, a smart learning environment can structure or recommend specific interactions for individual learners based on subject matter, learning goals and a learner’s preferences, interests, ability, and knowledge. This means that a smart learning environment can develop meaningful learning pathways for individual learners and create optimal learning activities to help learners attain intended goals by deploying suitable learning resources and interactive tools to enhance learning, performance, and the student’s experience.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Huang, R., Yang J., Hu Y. From Digital Learning Environment to Smart Learning Environment—Reform and Trend of Learning Environment. Open Education Research, (01), 75-84 (2012).
Juxtalearn, Retrieved April 12, 2016. Available at http://www.open.ac.uk/iet/main/research-innovation/research-projects/juxtalearn.
U of i’s Plato: the Foundation of Electronic Learning, Retrieved March 10, 2016. Available at http://research.uillinois.edu/news-blog/blog/2015/12/21/u-plato-foundation-electronic-learning.
Santhi, R., Priya, B., & Nandhini, J. M.. Review of intelligent tutoring systems using bayesian approach. arXiv preprint arXiv:1302.7081 (2013).
Vanlehn, K., Lynch, C., Schulze, K., Shapiro, J. A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., & Wintersgill, M.. The Andes physics tutoring system: Lessons learned. International Journal of Artificial Intelligence in Education, 15(3), 147-204 (2005).
Hadwin, A., Nesbit, J., Jamieson-Noel, D., Winne, P., & Kumar, V. Tracing self-regulated learning in an e-learning environment. Paper presented at the annual meeting of the American Educational Research Association, Montreal, Quebec, Canada (2005).
Greller, W., & Drachsler, H. Translating learning into numbers: A generic framework for learning analytics. Educational Technology and Society, 15(3), 42–57 (2012).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Huang, R., Du, J., Chang, Tw., Spector, M., Zhang, Y., Zhang, A. (2017). A Conceptual Framework for a Smart Learning Engine. In: Popescu, E., et al. Innovations in Smart Learning. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-2419-1_11
Download citation
DOI: https://doi.org/10.1007/978-981-10-2419-1_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2418-4
Online ISBN: 978-981-10-2419-1
eBook Packages: EducationEducation (R0)