The librarian is in: a consultation model for information literacy formative assessment

Formative assessment was introduced in Rehabilitation Therapy students’ information literacy programs in Fall Term 2006 in the course OT/PT 892: Evidence-Based Practice. It was subsequently employed in the Winter Term 2008 and again in the Spring Term 2008. Formative assessment during student/librarian face-to-face consultations was one of a variety of teaching techniques used in the program. Other techniques included: a required reading; an interactive hands-on searching session; and a summative assessment of the final revised search strategy assignment (these techniques varied somewhat over the 3 classes). With the 2008 entrance class, this course content moved to OT/PT 898: Critical Enquiry, largely in Module 3: Reviewing the Literature. One of the Critical Enquiry’s learning objectives is: “recognize and reflect on the complexity of gathering evidence to inform decision-making.”

The Optimality of Decision Making During Motor Learning

In our daily lives, we often must predict how well we are going to perform in the future based on an evaluation of our current performance and an assessment of how much we will improve with practice. Such predictions can be used to decide whether to invest our time and energy in learning and, if we opt to invest, what rewards we may gain. This thesis investigated whether people are capable of tracking their own learning (i.e. current and future motor ability) and exploiting that information to make decisions related to task reward. In experiment one, participants performed a target aiming task under a visuomotor rotation such that they initially missed the target but gradually improved. After briefly practicing the task, they were asked to select rewards for hits and misses applied to subsequent performance in the task, where selecting a higher reward for hits came at a cost of receiving a lower reward for misses. We found that participants made decisions that were in the direction of optimal and therefore demonstrated knowledge of future task performance. In experiment two, participants learned a novel target aiming task in which they were rewarded for target hits. Every five trials, they could choose a target size which varied inversely with reward value. Although participants’ decisions deviated from optimal, a model suggested that they took into account both past performance, and predicted future performance, when making their decisions. Together, these experiments suggest that people are capable of tracking their own learning and using that information to make sensible decisions related to reward maximization.