How should we incentivize learning? An optimal feedback mechanism for educational games and online courses

2019

Conference Paper

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Online courses offer much-needed opportunities for lifelong self-directed learning, but people rarely follow through on their noble intentions to complete them. To increase student retention educational software often uses game elements to motivate students to engage in and persist in learning activities. However, gamification only works when it is done properly, and there is currently no principled method that educational software could use to achieve this. We develop a principled feedback mechanism for encouraging good study choices and persistence in self-directed learning environments. Rather than giving performance feedback, our method rewards the learner's efforts with optimal brain points that convey the value of practice. To derive these optimal brain points, we applied the theory of optimal gamification to a mathematical model of skill acquisition. In contrast to hand-designed incentive structures, optimal brain points are constructed in such a way that the incentive system cannot be gamed. Evaluating our method in a behavioral experiment, we find that optimal brain points significantly increased the proportion of participants who instead of exploiting an inefficient skill they already knew-attempted to learn a difficult but more efficient skill, persisted through failure, and succeeded to master the new skill. Our method provides a principled approach to designing incentive structures and feedback mechanisms for educational games and online courses. We are optimistic that optimal brain points will prove useful for increasing student retention and helping people overcome the motivational obstacles that stand in the way of self-directed lifelong learning.

@conference{Xu2019CogSci,
  title = {How should we incentivize learning? An optimal feedback mechanism for educational games and online courses},
  author = {Xu, Lin and Wirzberger, Maria and Lieder, Falk},
  month = jul,
  year = {2019},
  doi = {},
  url = {https://cognitivesciencesociety.org/wp-content/uploads/2019/07/cogsci19_proceedings-8July2019-compressed.pdf},
  month_numeric = {7}
}