A Study on the Influence of Halo Effect: Teaching Evaluation in Junior and Senior High Schools
Bibliographic record
Abstract
Teaching evaluation plays an indispensable role in continuously improving the education system and promoting talent cultivation in today's society. The Halo effect, as a common cognitive bias, also affects educational evaluation all the time. Therefore, understanding how the Halo effect affects education evaluation, how it affects, and how to find ways to reduce the deviation caused by the Halo effect are the current core content. Based on previous scholars' research on the Halo effect and teaching evaluation, this paper adopts the method of systematic literature review, and analyzes, incorporates, and summarizes how the Halo effect operates in education evaluation and how much it has influenced from different perspectives, including students and teachers, subjects, grade of students, and different types of schools. Through literature collection, it has been found that both parents and students map to other aspects due to a certain characteristic of the teacher, such as the teaching ability of a certain course. First impressions can affect the evaluation of other abilities. And students often have biases based on the appearance and clothing of teachers when they first meet them and then pass these biases on to the teachers, which can have a significant impact on teaching evaluation. These are all common. The deviation caused by the Halo effect can be reduced mainly through three aspects: first impression, understanding in detail and view from a developmental perspective, and improving professional quality.
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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 | 0.024 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".