Experiences with CS Education Research

Experiences with CS Education Research

CS Education Research at Virginia Tech Cliff Shaffer Department of Computer Science Virginia Tech Goals of the Talk 1. Introduce ongoing activities in the Department 2. Describe what we do, what is difficult, and

how it is valuable 3. To relate some past successes and failures [email protected] Role in Digital Education A Major Player in the Field Competing with MIT, CMU, Perdue, GaTech, Duke SIGCSE09 Largest departmental contingent? 7 CS faculty

About that many students About that many CS Alumni (faculty) A Sampler of VT Work CITIDEL/Ensemble (part of NSDL) Ed Fox Collections ETD/Syllabus Repository/AlgoViz Wiki

Web-CAT Steve Edwards Cyber Arts Steve Harrison, Yong Cao Middle School Math Deborah Tatar Algorithm Visualization Cliff Shaffer Visual Debugging Godmar Back

Questions to Consider What are the (some) Goals? Is it scientific research? Is it Computer Science? Goals Improve education Course (and courseware) development Improve understanding Improve proficiencies (programming)

Is it Scientific Research? Does it have Measurable Effects? Is it Reproducible? Is it Novel? Does it Have Measurable Effects? We want results to be quantifiable Performance scores: Absolute: Difference between pretest and

posttest Relative: Performance gains in various treatments No Significant Difference Hawthorne Effect Is it Reproducible? Isolate confounding influences Instructor Environment

Multiple treatments intermingled Is it Novel? Computers are still relatively new in education Even in CS, we have only recently had the opportunity to use computers for education in ways other than as targets for programming exercises Which leads to the next issue

Is it Computer Science? Some scenarios What if I were a Chemistry Professor? Instructional Technology Professor? Engineering Education Professor? CS Professor? In each case, the work is clearly of service to the discipline Note: ACM and IEEE both have Transactions in

Education journals Courseware Development Courseware development work is interdisciplinary: Domain content (CS in our case) Education/Instructional Technology Human Factors/HCI Software Engineering

Improving Data Structures Problem: Improve the retention/success rate in CS2606/CS3114 Key feature of the course is difficult projects So, I focus on improving success rate in projects Interventions Pairs programming Project management Increase student/Instructor interactions

Pairs Programming CS2606, 2007, 2 sections, no control Assigned partners, switch each project. Self-selected at end (but partners generally required) 3-4 week projects (4 total), fairly difficult eXtreme Programming style interactions encouraged Pairs Programming: Outcomes

Result: No difference detected in success rates or other outcome from prior semesters Somewhat contradictory of prior literature. Appears not to help or hurt students, in general. What about individuals? Mixed reception by students Hypothesis: Some benefit, some dont Used free choice in future sections No differences detected

Scheduling Managing large-scale projects involves scheduling activities It is human nature to work better toward intermediate milestones. The same concepts can/should be applied to mid-sized projects encountered in class. For any project that needs more than a week of active work to complete, break into parts and design

a schedule with milestones and deliverables. Scheduling CS2606, CS3114, 2007-2009 several sections, no control Require students to plan interim due dates, predict times required, weekly reports of time spent Mixed reaction from students. Some anecdotal evidence of appreciation afterward

No recognizable change in outcomes Real Results #1 CS2606, Fall 2006 3-4 week projects Kept schedule information: Estimated time required Milestones, estimated times for each Weekly estimates of time spent.

Real Results #2 Real Results #3 Results were significant: 90% of scores below median were students who did less than 50% of the project prior to the last week. Few did poorly who put in > 50% time early Some did well who didnt put in >50% time early, but most who did well put in the early time

Real Results #4 Correlations: Strong correlation between early time and high score No correlation between time spent and score No correlation between % early time and total time What is the Mechanism?

Correlations are not causal Do they behave that way because they are good, or does behaving that way make them good? Spreading projects over time allows the sleep on it heuristic to operate Avoiding the zombie effect makes people more productive (and cuts time requirements) CS 2606/CS 3114

We know scheduling works, but how do we change behavior? Old: 50+ students Little interaction with instructor/TA as needed Solo programming New:

14 students Meet with instructor for each project Pairs if desired Schedule sheets Outcome No recognizable difference

Algorithm Visualization: Features Pseudocode display Back Button Animation vs. next step Tutorials vs. AVs

Integrated text and activities (applets) Guide questions/directed activity Built-in quizzing (future) Explanatory applets vs. analysis applets Takes a long time to develop (several students over two years) In progress:

Hashing Memory management Search Trees AVs: Hashing Tutorial Section 1: Standard lecture and textbook for one week Section 2: In-class tutorial use for one week (same material) Student reaction: Universally positive for

tutorial Section 2 had significantly better score in post-test AV Community (AlgoViz) NSF CCLI grant, connections to NSDL/Ensemble project Problem: Some identifiable successes for AVs Have High faculty and student favorability ratings

But AVs have little overall impact on education Solution Build a community of users/developers Better disseminate best practices information AlgoViz Wiki Catalog Data

A collection of links to nearly 450 Avs Some results: Topical Distribution Who/where Quality Access Stability

NSDL Project Proposal Create a new model of dissemination to lower barriers to access Move away from the digital library model of users coming to collections Notification via social networks Focus on community-driven content development Discussion, review, ratings

Think Amazon, but we have critical mass issues

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