Automated Feedback

Towards immediate in-context feedback Paul Calder Computer Science, Engineering, & Maths

Automatic Feedback

• Students • Timely: as soon as they have something worth trying • Frequent: encourages continual improvement • No surprises: known to satisfy requirements • Teachers • Assessment: testing functional performance is (almost) free • Coverage: thoughtful test cases ensure full conformance • Scaffolding: basic capabilities before advanced • Consulting: students come with more focussed questions Flinders University / Computer Science, Engineering, and Mathematics 2

Where We Are Now

• Capabilities • Computer programming labs, tutes, assignments • “Functional specification” conformance • “Progressive” tasks: levels of performance • Limitations • Only on campus: CSEM lab opening hours • Cumbersome interface • Feedback is not in context • Not integrated with submission (or assessment) Flinders University / Computer Science, Engineering, and Mathematics 3

Where we’re heading

• Immediate goals • Wherever and whenever the student is working: home, on-line, ...

• Embedded into authoring tools: feedback in context • Non-functional feedback: “it

works

, but is it

good

?” • Integrate with electronic submission: Moodle plugin • Longer term • FLO: need thorough testing first!

• Other “easy” domains: engineering, mathematics, ...

• More challenging: physical sciences, economics, ...

• Aspects of

many

domains: Clippy done right?

Flinders University / Computer Science, Engineering, and Mathematics 4