Measuring Students’ Perception of Learning: The Systematic Development of An Instrument
Bibliographic record
Abstract
Within the education sector various tools have been used to measure effectiveness of instruction. It is typical that measures of teaching effectiveness include, but are not limited to, the student’s perception of their experience in the classroom and with a given instructor. Student evaluations of teaching (SETs) are one form of measurement commonly used in American universities. It is important to determine whether these SETs are helpful in assessing effective teaching and the instructor’s work in and out of the classroom, in general. To determine whether these SETs are helpful in assessing effective teaching and the instructor’s work in and out of the classroom, in general, we sought to develop an instrument to measure the students’ perception of teaching and learning as represented by three concepts: Student, Course, and Instructor. We used scaled survey items, some of which we borrowed from other instruments to operationalize the concepts and create a pilot test. We analyzed the data using Factor analysis techniques. The result was an instrument that included 24 items scaled on a five-point Likert scale. Key words: Teaching evaluation, instructor evaluation, course evaluation, student evaluation of teaching, students’ perception of learning. Dans le secteur de l’éducation, divers outils ont servi à l’évaluation de l’efficacité de l’enseignement. Typiquement, les mesures de l’efficacité de l’enseignement comprennent, entre autres, la perception qu’a l’étudiant de son expérience en classe et avec son professeur. Les évaluations par les étudiants de l’enseignement sont une mesure couramment utilisée dans les universités américaines. Il est important de déterminer si ces évaluations par les étudiants sont utiles dans l’évaluation générale de l’efficacité de l’enseignement et du travail du professeur en salle de classe et à l’extérieur de celle-ci. Pour le faire, nous avons tenté de développer un instrument permettant de mesurer la perception qu’ont les étudiants de l’enseignement et de l’apprentissage en fonction de trois concepts : l’étudiant, le cours et le professeur. Pour mettre en œuvre les concepts et créer un essai pilote, nous nous sommes servi de questions de sondage échelonnées, dont certaines ont été empruntées à d’autres instruments. Nous avons analysé les données avec des techniques d’analyse factorielle. Le résultat est un instrument à 24 items gradués selon l’échelle de Likert. Mots clés : évaluation de l’enseignement, évaluation de l’enseignant, évaluation de cours, évaluation par les étudiants de l’enseignement, perception des étudiants de l’apprentissage
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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 | 0.125 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".