|Published Online: September 23, 2015||$US5.00|
Educators recognize the need to provide users with context-appropriate challenges. Despite this belief, online learning games typically provide uninformed adaptive selection of learning tasks. Instead, we present our design of a quantified flow-channel metric, a ratio of user skill over problem difficulty. By mining and clustering historical play data from brainrush.com, a crowdsourced online learning platform, we weigh distractors for each learning objective. Our algorithm adaptively constructs questions based on our metric to maintain Flow. Our approach provides in-game adaptation to maintain the user's Flow experience and is applicable to a wide range of learning tasks.
|Keywords:||Csíkszentmihályi, Flow, Task-Centric Adaptation, Computer Adaptive Testing, Multiple-Choice Testing|
The International Journal of Science in Society, Volume 7, Issue 4, December 2015, pp.7-17. Article: Print (Spiral Bound). Published Online: September 23, 2015 (Article: Electronic (PDF File; 691.446KB)).
Associate Professor, Department of Mathematics and Computer Science, College of Science & Engineering, Wilkes University, Wilkes-Barre, Pennsylvania, USA
Chief Technical Officer, BrainRush, Mountain Top, Pennsylvania, USA