From Intuition to Evidence: A Data-Driven Approach to Transforming CS Education

Autor/in	Allevato, Anthony J.
Titel	From Intuition to Evidence: A Data-Driven Approach to Transforming CS Education
Quelle	(2012), (112 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, Virginia Polytechnic Institute and State University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-3037-9024-9
Schlagwörter	Hochschulschrift; Dissertation; Computer Science Education; Intuition; Grading; Computer Assisted Testing; Time Management; Memory; Learning Strategies; Retention (Psychology); Student Evaluation; Item Response Theory; Academic Ability; Data Collection; Models; Case Studies + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Computer science lessons; Informatikunterricht; Notengebung; Schulnote; Zeitmanagement; Gedächtnis; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Merkfähigkeit; Studentische Bewertung; Item-Response-Theorie; Data capture; Datensammlung; Analogiemodell; Case study; Fallstudie; Case Study
Abstract	Educators in many disciplines are too often forced to rely on intuition about how students learn and the effectiveness of teaching to guide changes and improvements to their curricula. In computer science, systems that perform automated collection and assessment of programming assignments are seeing increased adoption, and these systems generate a great deal of meaningful intermediate data and statistics during the grading process. Continuous collection of these data and long-term retention of collected data present educators with a new resource to assess both learning (how well students understand a topic or how they behave on assignments) and teaching (how effective a response, intervention, or assessment instrument was in evaluating knowledge or changing behavior), by basing their decisions on evidence rather than intuition. It is only possible to achieve these goals, however, if such data are easily accessible. I present an infrastructure that has been added to one such automated grading system, Web-CAT, in order to facilitate routine data collection and access while requiring very little added effort by instructors. Using this infrastructure, I present three case studies that serve as representative examples of educational questions that can be explored thoroughly using pre-existing data from required student work. The first case study examines student time management habits and finds that students perform better when they start earlier but that offering extra credit for finishing earlier did not encourage them to do so. The second case study evaluates a tool used to improve student understanding of manual memory management and finds that students made fewer errors when using the tool. The third case study evaluates the reference tests used to grade student code on a selected assignment and confirms that the tests are a suitable instrument for assessing student ability. In each case study, I use a data-driven, evidence-based approach spanning multiple semesters and students, allowing me to answer each question in greater detail than was possible using previous methods and giving me significantly increased confidence in my conclusions. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01