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We implemented SMILE through classroom observations, analysis of classroom photos and videos, analysis of the artifacts produced by participants, assessment of learner performance, and interviews with the teachers and students. 2). , 2 hours a day) of the mobile inquiry-based math class using SMILE. Students identified how questions created by their peers may have been incorrectly designed, how certain questions generated multiple correct answers, and why the assumed correct answer was not indeed correct.
Participants identified how questions created by their peers may have been incorrectly designed, how certain questions generated multiple correct answers and, in some cases, why their assumed correct answers were not indeed correct. 3c SMILE in Indonesia—other students peeking ©2014 All Rights Reserved by Seeds of Empowerment their lives), and also personal interests (which tell us how we might further engage them in future math learning). One of the tremendous opportunities with SMILE is that all their creations, activities, outcomes, and evaluations remain in the server as big data for later learning analytics and learning objects for further research.
E-tivities: The key to active online learning. London, United Kingdom: Kogan Page. , & Kothmeier, G. (2012). It’s just about learning the multiplication table. In S. Buckingham Shum, D. Gasevic, & R. ), Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ’12) (pp. 73–81). New York, NY: Association for Computing Machinery (ACM). Schönhart, J. (2013). MatheBingo—Case study in an elementary school. Unpublished term paper at Graz University of Technology. , & Vavoula, G.