2011_12_I/ITSEC - Enhancing Performance through Pedagogy and Feedback: Domain Considerations for ITSs
Abstract: As computer-based instruction evolves to support more adaptive training, it is becoming increasingly more evident that such programs be designed around an individual trainee's characteristics, rather than focusing just on task performance. In other words, a trainee‟s state (e.g. how they learn, their affect and motivation) is an important factor in performance and retention. To optimize individual performance in computer-based training Intelligent Tutoring System (ITS) technologies (tools and methods) are combining artificial intelligence (AI) knowledge representations and programming techniques with the intent to deliver instructional content and support tailored to the individual (Conati & Manske, 2009). From a holistic perspective, such tools and methods personalize training by considering an individual‟s historical data, real-time behavior, and cognitive measures to predicting comprehension levels and affective states (i.e. frustration, boredom, excitement). This historical and real-time interpretation of the trainee is used for concurrent adaptation of pedagogical and feedback strategies within training content.