Application of Matrix Outcome Mapping to Constructively Align Program Outcomes and Course Outcomes in Higher Education
Bibliographic record
Abstract
Establishing a link between Course Learning Outcomes (LOs) and Program Outcomes (POs) while assessing thecourse contents and delivery are among the most challenging issues in Higher Education. In the present studytwo forms were generated based on specific Course Learning Outcomes identified in the syllabus at thebeginning of the teaching term: a Student outcome evaluation form and a Faculty outcome evaluation form. Theobjective is was to assess if the outcomes specified in the syllabus are being delivered and are being deliveredthroughout the term. At the end of the semester, a student survey was given to students to evaluate the courseoutcomes. In addition, the faculty evaluated the course outcomes. A matrix was developed mapping the results ofthe student, the faculty and each assessment contributing to the specified outcome, all are on a similar scale. Amapped matrix was then generated based on the results. The results from the mapped Matrix pinpoint whichassignment contributed to the specified outcome, and show the gaps between the student evaluation and facultyevaluation. All data and results are set within a dashboard. The Dashboard is used as a tool to help see whereimprovements are needed, whether an assessment has contributed to the LOs or not and how much hascontributed to the PO, thus constructively aligning POs and LOs with continuous improvement as a focus.
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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".