Individual Differences as Predictors of Learning and Engagement

Individual Differences as Predictors of Learning and Engagement Sidney D’Mello (sdmello@memphis.edu) Claire Williams (mcwllams@memphis.edu) Patrick Hays (dphays@memphis.edu) Andrew Olney (aolney@memphis.edu) Institute for Intelligent Systems, University of Memphis Memphis, TN 38152 USA data on how individual differences influence engagement and learning within the context of intelligent learning environments such as Intelligent Tutoring Systems (ITSs). Understanding how individual differences impact learning sessions with ITSs is important, because ITSs are emerging as effective alternatives to deliver individualized instruction to large numbers of students (Corbett, Anderson, Graesser, Koedinger, & VanLehn, 1999; Graesser, Person, & Magliano, 1995; Koedinger & Corbett, 2006). It is generally acknowledged that all students do not benefit equally from learning sessions with ITSs (VanLehn et al., 2007). Some learners show dramatic improvements in learning gains from pre to post tests, while tutoring has a negligible impact on others. Some learners actively attend the session by carefully listening to the tutor, taking initiative by asking questions, and providing verbose responses to the tutor’s questions (Graesser et al., 1995). However, other non-critical learners, socially attend the session, and are comfortable being passive information receivers rather than active problem solvers. Who are these learners? Can they be discriminated from standard individual difference measures? What are the individual differences that are predictive of engagement and learning gains? These are the questions that motivated the present study. The present study investigated whether trait measures of individual differences in (a) motivation, engagement, and burnout, (b) cognitive abilities, and (c) task related measures, could predict state measures consisting of engagement levels and learning gains in a one-on-one tutoring session with an ITS. Our focus on trait measures of motivation, engagement, and burnout is motivated by numerous studies that have related these measures to engagement and learning (Bartels & Magun-Jackson, 2009; Pekrun, Elliot, & Maier, 2006). For example, learners with mastery-approach motivation orientations are expected to be absorbed in the learning process (i.e., more engaged) and process the material deeply, presumably resulting in higher learning gains (Elliot & McGregor, 2001). In contrast, learners with performance-approach characteristics process the material at relatively shallow levels and do not demonstrate impressive learning gains. Similarly, some research has linked trait measures of engagement and burnout to performance outcomes (Schaufeli, Martinez, Pinto, Salanova, & Bakker, 2002). Abstract We investigated the possibility of predicting students’ engagement and learning gains during a tutoring session from trait measures of motivation, engagement, burnout, cognitive ability, prior knowledge, and task related measures. Participants completed a multiple choice pretest, a learning session, a posttest, and a battery of individual differences tests and questionnaires. Multiple regression and exploratory factor analyses indicated that the individual differences measures yielded medium sized effects at predicting learning gains as well as engagement levels that were self-reported during the tutorial session. In general, self-reported interest in the task and confidence in learning from a computer tutor coupled with working memory capacity and attentional abilities were the major predictors of both engagement and learning. Keywords: learning, engagement, individual differences, cognitive abilities, motivation, burnout, ITS Introduction There is no one-size-fits-all approach when it comes to promoting student engagement and learning. Engagement and learning are affected by a number of factors such as, the learning environment (classroom, human tutor, high stakes learning), the task (acquiring shallow facts versus obtaining a deeper conceptual understanding), and characteristics of the learners themselves (e.g., visual versus verbal learners, performance versus mastery-oriented learners) (Ackerman, Sternberg, & Glaser, 1989; Jonassen & Grabowski, 1993; Schmeck & Geisler-Brenstein, 1989). Therefore, understanding how a particular student will be engaged in and benefit from a learning session requires an analysis of how the learning environment, the task, and the characteristics of the learner (i.e. individual differences) interact and influence learning outcomes. For a given learning activity (e.g., learning conceptual physics from a human tutor), the context and the task are fixed, however the individuals involved in the activity vary. Hence, it is important to discriminate learners that actively engage and benefit from a learning session from others who passively attend the session and do not demonstrate dramatic improvements in their knowledge levels. Consequently, individual differences research has been a long standing and valuable tradition in the fields of psychology and education (Ackerman et al., 1989; Jonassen & Grabowski, 1993). Although research efforts along this front have yielded some important insights, there is little