Using a Delphi Approach to Develop Rubric Criteria
Bibliographic record
Abstract
Recent developments in post-secondary institutions have motivated a shift towards outcomesbased education.A major impetus for this agenda has been the growing need to provide concrete evidence of student learning and institutional effectiveness to various stakeholders.Given this trend, it is important that research be undertaken to explore valid approaches to learning outcomes assessment.The research described here involves the development of valid, non-discipline specific, analytic rubrics that assess learning outcomes in five key areas: communication, design, teamwork, problem analysis and investigation.This paper reports on the methodology used to complete the first stage of rubric development; identifying the standards through which student work is evaluated.In particular, a two-stage Delphi study was designed to identify rubric criteria for assessing problem analysis and investigation.The Delphi technique is an iterative research tool used to elicit input from a panel of experts.It typically involves a series of virtual survey rounds in which experts offer their views anonymously and have the opportunity to refine them based on controlled feedback from earlier rounds.Panel members include 11 experts for investigation and 15 experts for problem analysis from faculty and staff.In the first round, participants were asked to propose learning outcome statements or "indicators" that are important for assessing problem analysis or investigation.In the second and final round, these responses were arranged by major outcome areas and sent to participants for their feedback.They were asked to rate how likely they were to use the indicators, and their importance in the curriculum.The focus of this paper is not the results of this study, but the methodological processes involved in designing and administering a Delphi survey to develop tools for learning outcomes assessment.This includes expert selection, survey design, and analysis of expert responses.Special attention is paid to the challenges of conducting a Delphi study.1. What are the specific skills/behaviours/attitudes that are important for assessing investigation? 2. What are the specific skills/behaviours/attitudes that are important for assessing problem analysis?This study is part of a larger research project which seeks to develop non-discipline specific analytic rubrics in problem analysis, investigation, design, communication and teamwork.The paper is organized into three sections.The first section provides background information on rubrics and the Delphi method.This is followed by a description of the research team's application of the Delphi process.The final section provides commentary on the Delphi method with a particular focus on the challenges of conducting a Delphi study.
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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 | 0.146 | 0.189 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.027 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".