Exams and Student Feedback: An experiment in Marking Efficiencies
Bibliographic record
Abstract
Providing summative feedback to studentsin a timely fashion, and managing the associated markingin larger classes has been a perpetual challenge in aneducation environment, and is even more so in a resourcechallenged environment. This paper discusses the resultsof an experiment in evaluation in an engineering courseby implementing a modified evaluation and gradingapproach. The objectives were to i) provide timelyfeedback to students, ii) improve engagement and reduceoverall course loading for students, and iii) reducemarking effort for instructors, all without negativelyaffecting student grade performance. The results showthat improvements over traditional methods can be madein two of the three areas.The course in question, (redacted), covers basicelectrical concepts and devices for non-electricalengineering students. The course had been offered in fourprevious years using a traditional evaluative approach:weekly assignments (submitted, marked and returned),laboratory exercises (comprehensive reporting orexercises submitted, marked and returned), midterm(s)(graded and returned), and a final examination. Themodified approach was implemented over the past twoyears and included the same learning strategies, but witha potentially lower resource commitment for students andinstructors. Modifications to the strategy wereimplemented the first and second years. The experimentintroduced procedural and administrative modificationsin assignments, laboratories and examinations, and theaddition of short weekly quizzes to improve engagementin an active learning environment.Approximately ten assignments were offered to helpstudents test and improve their understanding andknowledge. In the first year, assignments and solutionswere posted simultaneously; no submission was requiredand there was no grade contribution offered. Therationale for this strategy was that students would receivevirtually instant feedback by having solutions immediatelyavailable, and the freedom to judge the quantity and levelof completion required to meet their individual learningneeds. In the second year, assignments wereadministered through an online assignment system formark credit. This was intended to reinstate the incentiveof mark credit to improve student engagement while stillproviding instantaneous feedback on correctness.The course has always included a critical “hands-on”laboratory component which was traditionally timeintensive for both students and instructors. While thelaboratory submissions were still required for markcredit, the reporting requirement was reduced to aminimal, specified sampling of results to provide evidencethat the practical work was addressed. Expectedoutcomes were again provided for students to providerelevant and timely feedback. In the second year, a 3-bingrading system was adopted to improve the granularity ofthe marks while still requiring considerably less markingeffort.Examinations were also modified to improve timelinessof feedback and reduce marking effort. In the first year,three “midterm” examinations were distributed throughthe term to monitor student learning and verify studentparticipation in the self-directed parts of the course.Each of these exams consisted of 12 questions and weresimply graded on a correct response (no “partial marks)to reduce marking effort. In the second year, two midtermexaminations were deemed sufficient, but were gradedusing a 3-bin approach, thus allowing for “partialmarks”. Exams were returned to students in the nextlecture period in both cases. The Final examination inthe first year was designed using a 3-bin scheme to allowfor partial marks while still reducing marking effort. Inthe second year, this was increased to a 4-bin scheme toimproved granularity. The increase in granularity hadvery little effect on marking effort for both the midtermand final.One additional modification was made in the secondyear with the addition of brief weekly quizzes, for markcredit, to encourage students to complete assigned prereadingexercises and keep up with course work. Thequizzes consisted of two brief questions: one on assignedreading for the coming week and one on the previousweek’s material. These quizzes were administered andgraded using a classroom response system and automatically integrated with the learning managementsystem.Analysis consisted of comparison of grades withprevious years, anecdotal evidence and observations onstudent effort, course evaluation data and survey results.Preliminary results indicate student load and instructormarking effort were significantly reduced. While graderesults were approximately the same. A direct objectivecomparison with previous years is not significant due tovariations in course content and cohort.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".