Evaluation of software tools supporting outcomes-based continuous program improvement processes: Part 2
Bibliographic record
Abstract
The Canadian engineering accreditationboard (CEAB) mandate tasked each engineering programto assess student outcomes in the form of graduateattributes and develop a data-informed continuousprogram improvement stemming from those assessments.Administering, collecting and organizing the breadthassessment data is an extensive process, typicallycentralized through the use of software tools such aslearning management systems (LMS), contentmanagement systems (CMS), Assessment Platforms (AP)and Curriculum Planning & Mapping tools. Thesesystems serve a variety of roles, ranging from coursecontent delivery, e-learning, distance education, learningoutcomes assessment, outcomes data management andlearning outcomes analytics. Vendors have beendeveloping various solutions to accommodate the shifttowards outcomes based assessment as part of acontinuous improvement process.This paper will continue where the original paperpresented at CEEA 2013 left off. It will introduce the newclassifications of tools, how well each tool aligns with theEGAD (Engineering Graduate Attribute Development)project 5-step process and compare and contrast softwaretools supporting outcomes based assessment as part of acontinuous improvement process such as Chalk & Wire,Atlas Curriculum Mapping, Entrada, CoursePeer andother systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".