How Should a Theory of Learning and Cognition Inform Instruction?

Abstract

for high school mathematics that have been effective in improving learning. These tutors interact with students at the grain size of about 20 seconds during which the student might transform an equation or calculate an angle and the tutor will respond to that. The tutors are able to interact with the student at this grain size on the basis of a cognitive model that can solve these problems in the various ways students are supposed to be able to solve the problems. Compared with most computer-based instruction, this is a rather fine grain size. However, compared with current theories of human cognition, including our own Automatic Components of Thought-Rational (ACT-R; Anderson & Lebiere, 1998, this is a very gross level of interaction. Temporally, ACT-R postulates primitive acts of cognition taking under 1 second and often as little as 50 ms. Componentwise, the theory postulates separate visual, memory, imaginal, and motor components to a student action such as transforming an equation. Although our focus is on ACT-R, its grain size is typical of modern theories of cognition. This chapter examines whether there is a role for a theory of cognition at this grain size in affecting something like mastery of high school algebra. Although not offering any definitive answers to the question, we review some of our research on eye movements that has attempted to track a finer temporal grain size in mathematical problem solving, and we review some of our research on brain imaging that has attempted to track different cognitive components in mathematical problem solving. Using Eye Movements to InstructEye movements are behaviors that occur at a temporal grain size of under a second and therefore offer the opportunity of fine-grained tracking of mathematical problem solving. Gluck (1999; see also Anderson & Gluck, 2001) looked at the instructional leverage one might gain by monitoring student eye movements while interacting with an algebra tutor. The tutor Gluck developed is 47

Using Social Psychology to Motivate Contributions to Online Communities

“…As Axelrod (2003) notes, simulation approaches to expressing theory allow one to take complicated input and generate their consequences as predictions, allowing researchers and designers to reason about processes that would otherwise be too complex. The simulation approach has led to major advances in cognitive science (Anderson et al, 2004; Simon, 1995), including the application of theories of psychology to inform the technological design (e.g., Anderson, Corbett, Koedinger, & Pelletier, 1995; Anderson, Douglass, & Qin, 2005). Perhaps it is time to use these techniques to synthesize knowledge in the social sciences and package the knowledge for design.…”

Using Instructional Design Process to Improve Design and Development of Internet Interventions

“…Theories are drawn from a range of fields including education and learning psychology, behavior change, and motivation [16-18,35-38]. Theories that have been widely applied include cognitive learning [18,35,39,40], information processing [41], and multimedia learning [42]. The structure and type of learning required by each objective influences which learning theories are most applicable.…”

AutoTutor and affective autotutor

“…d = (M 1 − M 2 )/ (s 2 1 + s 2 2 )/2 mining [Anderson et al 2005;Baker and Yacef 2009;Corbett and Anderson 1994;Romero and Ventura 2007;]. The ITSs that have been successfully implemented and tested have produced learning gains with an average effect size of one sigma, which is roughly equivalent to one letter grade [Corbett 2001;VanLehn, Graesser et al 2007].…”

“…The late 1990s and the early 2000s witnessed an exciting infusion of ITSs that implemented sophisticated tutoring strategies such as error identification and correction, building on prerequisites, frontier learning (expanding on what the student already knows), student modeling (inferring what the student knows and using that information to guide tutoring), building coherent explanations, and natural language dialogs [Aleven and Koedinger 2002;Anderson et al 2005;Gertner and VanLehn 2000;Koedinger et al 1997;Lesgold et al 1992;Sleeman and Brown 1982;]. It was around this time that affective computing was beginning to emerge as a new and exciting research area.…”