As feedback is an important part of learning and motivation, we investigate how to adapt the feedback of a conversational agent to learner personality (as well as to learner performance, as we expect an interaction effect between personality and performance on feedback). We investigate two aspects of feedback. Firstly, we investigate whether the conversational agent should employ a slant (or bias) in its feedback on particular test scores to motivate a learner with a particular personality trait more effectively (for example, using “you are slightly below expectations” versus “you are substantially below expectations” depending on learner conscientiousness). Secondly, we investigate which emotional support messages the conversational agent should use (for example: using praise, emotional reflection, reassurance or advice) given learner personality and performance. We investigate the adaptation of this feedback to a learner personality, in particular the traits in the Five Factor Model. Five experiments were run where participants gave progress feedback and emotional support to students with different personalities and test scores. The type of emotional support given varied between different personalities (e.g. neurotic individuals with poor grades received more emotional reflection). Two algorithms were created using different methods to describe the adaptations and evaluated on how well they described the experimental data using DICE scores. A refined algorithm was created based on the results. Finally, we ran a qualitative study with teachers to investigate the algorithm’s effectiveness and further refine the algorithm.
|Number of pages||55|
|Journal||International Journal of Artificial Intelligence in Education|
|Early online date||12 Aug 2015|
|Publication status||Published - Sep 2016|
- emotional support