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abstract

Evidence Based Teaching Practices in CS (Abstract Only)

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Published:08 March 2017Publication History

ABSTRACT

In this workshop participants will receive an overview of teaching practices in computer science that research indicates are effective. While the field of computer science education is young, it has uncovered several teaching practices that can be adopted by instructors that can improve both the retention and performance of students. These evidence based teaching practices include active learning techniques such as peer instruction and prior-knowledge activities, pair programming, and use of subgoal labels. Participants will experience firsthand many of these techniques and will be provided with resources on where to find more information, including the original research papers, on each technique. If you want to attend a workshop that will have an immediate impact in your class -- attend this one. The workshop will be interactive, engaging, and show you how to incorporate teaching practices that are empirically proven to provide benefits. You are guaranteed to leave with a list of many freely available resources and ideas to use in your next class. You will also have the opportunity to "ask the experts" as the authors of many of these research papers will be leading that session of the workshop.

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  1. Evidence Based Teaching Practices in CS (Abstract Only)

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    • Published in

      cover image ACM Conferences
      SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education
      March 2017
      838 pages
      ISBN:9781450346986
      DOI:10.1145/3017680

      Copyright © 2017 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 8 March 2017

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      Qualifiers

      • abstract

      Acceptance Rates

      SIGCSE '17 Paper Acceptance Rate105of348submissions,30%Overall Acceptance Rate1,595of4,542submissions,35%

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