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Well-designed formative feedback has the potential to enhance both instructor teaching and student learning. Initially, developing a formative feedback process takes some effort, but once established, requires little effort. The process includes four steps: 1) acquiring data from student reflections; 2) assessing and characterizing student responses in order to diagnose the learning issues that can impede students from achieving their learning goals; 3) designing and synthesizing the type and mode of formative feedback that best addresses the learning issues; and 4) selecting a formative feedback delivery method that quickly communicates to students the information and/or resources that they can use to enhance progress toward their learning goals. Over time, feedback to students on their performance and reflections on topical content has been divided into two general types one is outcome feedback and the other is information or process feedback. Outcome feedback basically describes whether an outcome of a task is correct or incorrect, which only provides limited guidance for the student. In particular, with traditional lecture-and-test pedagogy, instructors communicate outcome feedback to students that is mainly composed of assessment by grading of homework, quizzes and tests as to whether the work is correct or incorrect. As such, instructors often assume that students can use this limited information to subsequently improve their knowledge and understanding of the content. On the other hand, information or process feedback from data analysis and synthesis of directed feedback provides rich and insightful information to address issues related to students’ learning processes. It helps students monitor their construction of knowledge and contributes to the selfregulation that leads to deeper conceptual learning and the achievement of their learning goals. There are many methods for acquiring student reflection responses, but in this paper we will focus on the steps of the feedback process when using end-of-class “Muddiest Point” (MP) student reflections. These arise from a class topic for which students are monitoring the learning issues that arise in the course of class instruction and may impede their understanding of content. The first step in the process is data collection, which is now automated with Concept Warehouse (CW), cw.edudiv.org, a web-enabled resource developed by Milo Koretsky at Oregon State University. The second step is using the responses to characterize and diagnose student learning issues. There are a variety of types of Student Learning Issues and Misconceptions (SLIMs) that impede learning. Some of these include knowledge gaps, vocabulary gaps, (misunderstood, misused, incorrect or missing words) and skill gaps which can include missing or faulty skills in problem calculations, analysis, computation, and graph construction, reading and interpretation. The third step is to address the nature of the type of learning issue and synthesize the formative process feedback response using the most suitable mode (verbal, visual, videos, graphical, tabular, etc.). This helps facilitate self-regulation of their learning by monitoring, assessing, and adjusting their learning strategies to achieve their desired learning goals. The fourth step is to communicate the feedback response with a simple delivery method, such as next-class slides, email, Blackboard, or the web. In this paper, an example of MP-generated SLIMs related to the introductory materials science topic of eutectic phase diagrams will be given as an example, along with strategies for addressing them. Results on effectiveness and impact of such formative process feedback for a whole materials course will also be presented and discussed. P ge 24273.3

Characterizing and Addressing Student Learning Issues and Misconceptions (SLIM) with Muddiest Point Reflections and Fast Formative Feedback