June 15, 2014
June 15, 2014
June 18, 2014
24.273.1 - 24.273.18
Characterizing and Addressing Student Learning Issues and Misconceptions (SLIMs) in Materials Science with Muddiest Point Reflections and Fast Formative FeedbackIt is generally acknowledged in engineering education research that, prior to instruction, studentsmay have faulty knowledge about how the world works, such as misconceptions. In order tocreate, develop, or restructure instructional materials and activities, instructors must be informedby that prior knowledge. In a sense, however, prior knowledge in a classroom setting alsoconsists of a variety of both Student Learning Issues and Misconceptions (SLIMs). These otherlearning issues can consist of different categories of impediments or barriers to learning. Onegeneral category could be called "gaps". One such type of gap is "knowledge gaps". Anothertype might be "gaps related to words and language" and could include vocabulary gaps ofmisunderstood, misused, incorrect or missing words and language gaps from faulty grammar orforeign language issues. Another gap type, "skill gaps", could include skills that are lacking,faulty, or misapplied in areas such as calculations, analysis, computation, and graph reading andinterpretation. These gaps in knowledge, words and skills fall under the general heading of thestudent learning issues part of SLIMs. Effective instructional materials and classroom practiceneed to be informed by and address such SLIMs, which might be acquired from broad formativeassessment of foundational knowledge of students who are learning new content. Suchassessments can span a broad range and include concept inventories, concept tests, homeworkassignments, tests, etc., but such instruments usually span longer time sequences of instructionthan a single class and may not provide timely and focused feedback to students. While class-by-class formative assessment could provide such information, such data collection and analysis iscumbersome. Presently, short formative assessments such as minute papers and muddiest pointshave been given with a card or sheet of paper to students near the end of a class. Compiling andanalyzing such data is tedious, but new web-enabled approaches can now provide quick and easyturnaround with rich and insightful data. One such approach will now be described.Initially, in a project called JTF (Just-in-Time-Teaching with Interactive Frequent FormativeFeedback), collecting Muddiest Point student response data was tedious, but has been transformedby a powerful tool, called the Concept Warehouse (CW), cw.edudiv.org. It is a web-enabledresource developed by Milo Koretsky at Oregon State University, that has automated Muddiest Pointdata gathering and analysis capabilities. Now, anonymous student Muddiest Point responses can becollected via the web to CW via smart phone, tablet or laptop computer. Then, CW can automaticallyoutput student response data in tabular quotation format, with a student Muddy Point intensity scaleof 0-5 for each response, along with a Word Cloud with word size proportional to word frequency.The word cloud allows instructors to instantly identify key words associated with student learningissues and misconceptions (SLIMs) from Muddiest Points feedback. Instructors can use the wordcloud and quotes with intensities, in order to strategize how to adjust instruction, create interventions,or create student learning resources such as activities, pencast tutorials, or Muddiest Point YouTubevideos. Research has shown that attending to learning issues as quickly as possible withimmediate feedback is most effective for motivation and learning. In this paper, a variety ofMuddiest-Point-generated SLIMs related to introductory materials science will be given asexamples along with strategies for addressing them. Additionally, assessment tools and results todetermine the effectiveness and impact of such cyber-enabled data collection and formativefeedback approaches for improving instructor teaching and student learning will be discussed.
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