Tracking Classroom Teaching and Learning: An SPC Application
Bibliographic record
Abstract
The importance of measuring performance in higher education has long been understood by all stakeholders, including teachers, students, administrators, and researchers. However, the majority of indicators used for this purpose focus on educational outputs (e.g., graduation rates) and outcomes (e.g., final examination scores), rather than processes that create such outcomes and outputs. The problem with this focus is that the output and outcome data usually become available far too late in order to effectively respond to a problem. Because the process of knowledge transfer is an important function of educational organizations, tracking this process while it actually happens represents an on-going, rather than a post-mortem measurement strategy, and can help in the detection of existing and impeding troubles in the teaching and learning processes. This paper illustrates a model for measuring classroom performance which makes use of Statistical Process Control (SPC) in combination with classroom assessment techniques (CATs). The purpose of the model is to measure both the teacher's contribution to increasing student knowledge and the student learning outcomes. Examples of SPC charts that were used to monitor teaching and learning performance in an undergraduate engineering management course are given, together with an analysis of the obtained results. Recommendations and guidelines for an effective and efficient application of the model are provided, including an implementation algorithm, suggestions for CAT design, and a discussion of some important statistical issues. The paper is concluded with several considerations for future research.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".