Title page for ETD etd-07212012-094523

Type of Document Dissertation
Author Allevato, Anthony James
Author's Email Address allevato@vt.edu
URN etd-07212012-094523
Title From Intuition to Evidence: A Data-Driven Approach to Transforming CS Education
Degree PhD
Department Computer Science and Applications
Advisory Committee
Advisor Name Title
Edwards, Stephen H. Committee Chair
Evia, Carlos Committee Member
Pérez-Quiñones, Manuel A. Committee Member
Ramakrishnan, Naren Committee Member
Tatar, Deborah Gail Committee Member
  • intuition
  • evaluation
  • assessment
  • Web-CAT
  • evidence
  • Dereferee
  • C++
  • extra credit
  • student performance
  • procrastination
  • time management
  • student behavior
  • data collection
  • memory management
  • pointers
  • item response theory
  • reference tests
  • JUnit
  • difficulty
  • discriminating ability
  • automated grading
  • Eclipse
  • BIRT
  • reporting
  • Computer science education
Date of Defense 2012-07-16
Availability unrestricted

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 curric- ula. 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.

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