The use of a computerized automated feedback system

Trevor BarkerDept. Computer Science

Contents

• Feedback considerations• Approaches to feedback• Automated feedback– Previous research

• Examples• Discussion

Chickering and Gamson’s Seven Principles: Good practice in higher education…

1. Encourages contact between students and lecturers

2. Develops reciprocity and cooperation among students

3. Encourages active learning 4. Gives prompt feedback 5. Emphasises time on task 6. Communicates high expectations 7. Respects diverse talents and ways of learning

Gives prompt feedback

• Feedback must be prompt but it must also be good i.e.– Appropriate– Useful– Accurate– Individual– Fast– Facilitate feed forward

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Reasons for automated approaches to testing and learning

• Vast investment in infrastructure• Availability of MLE systems such as UH

Studynet• Changes in nature of Higher Education• Online and distance education• Increase in student numbers (SSR)• Increasing pressures on time and cost

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Previous researchComputer-Adaptive Test

• Based on Item Response Theory (IRT) • If a student answers a question correctly, the

estimate of his/her ability is raised and a more difficult question is presented

• If a student answers a question incorrectly, the estimate of his/her ability is lowered and an easier question follows

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Previous researchComputer Adaptive Testing

• Computer-Based Tests (CBTs) mimic aspects of a paper-and-pencil test – Accuracy and speed of marking – Predefined set of questions presented to all participants

and thus questions are not tailored for each individual student

• Computer-Adaptive Tests (CATs) mimic aspects of an oral interview– Accuracy and speed of marking – Questions are dynamically selected according to student

performance

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Benefits of the adaptive approach

• Questions that are too easy or too difficult are likely to – Be de-motivating – Provide little or no valuable information about

student knowledge • The CAT level identifies a unique boundary

between what the student knows and what he or she does not know

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Providing individual feedback based on CAT.

• An application of the CAT approach is in the provision of automated individual feedback

• This approach has been in operation for several years at the University of Hertfordshire in two BSc. Computer Science modules

• Recently this model has been extended to make it easier to use on other modules

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About the Feedback • Learners received feedback on:– Overall proficiency level; – Performance in each topic; – Recommended topics for revision– Cognitive level (Bloom)

• Feedback on assessment performance was initially made available to learners via a web-based application

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Bloom’s taxonomy

Example questions

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Performance Summary

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Points for Revision

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Results: tutors’ opinions• Tutors consider that the fast feedback provided by

a CAT is as good as or better than that currently provided in many cases.

• The link to Bloom’s levels was positive• The approach was considered to be efficient,

possibly freeing time for other activities• CAT considered to be best as a formative tool,

rather than for summative assessment• Some tutors were concerned that the approach

was ‘impersonal’• There is a need for a monitoring role for tutors,

for practical and ethical reasons

Recent research• The CAT automated feedback system has been

extended from objective testing to include written and practical tests

• Testing and evaluation of the new system with approximately – 350 first yearBSc (1 final practical test), – 120 second year BSc(2 written and practical tests) and – 80 final year BSc (2 final practical tests) – 70 MSc students ( 2 written tests)

First prototype

Detailed marking scheme for one question showing feedback

Converted manually into simple database file

Output from system - email

Output from system - email

Later prototype

Final summary screen

Added features• Markers able to comment on the completeness of the hand-

in – In this version, the hand-in information is presented to the marker

who may then make additional comments on the completeness or nature of the hand-in.

• Feedback was determined by the system based on the mark awarded in each section of a question, reading it from the database file for the assignment.

• After all the question sections had been marked, the system presented a final summary screen so that the marker could check that the marks had been awarded accurately.

• The marker can add additional feedback at the end

Results• Student attitude to feedback was good

irrespective of score on test– Useful– Fair– Convenient– Quantity– Quality

• Internal moderator happy with feedback• Suggestions from moderator were

included in the next prototype

Latest version

Modifications

• Easy to set up feedback database automatically

• Tutors can modify and add to feedback for each question

• Additions to feedback saved for re-use later

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In summary• Larger class sizes, greater use of online and distance

assessment ensures that feedback is often too slow and too general to be of any real use to learners.

• Personalised automated feedback is likely to become increasingly important in the future. It is being used in four modules currently at UH.

• Learners and tutors accept the need for automated feedback and most appreciate the benefits of such systems.

• The system is being further developed to make it simpler for general use