Academic Excellent Educators: A Student Election or an Administrator Selection?
Bibliographic record
Abstract
Dear Editor in chief Each year, Iranian university authorities, as a good tradition on Teacher’s Day, choose and introduce their top educators to their academic community. Undoubtedly, this effort is a good action, generally leading to effective promotion of educational system. However, criteria for these selections are always a matter of big debate. In overview, an academic excellent educator is supposed to be at a superior level of educational competency in comparison with his/her colleagues. University administrators and students are two individual references may rate to criteria. However, many surveys have revealed that students distinctively highlight different criteria for teacher excellence (1, 2). From academic authorities’ perspective, faculty members must be excelled in a combination of abilities and activities in three fields: Ability of teaching, quantity of teaching, and scholar engagement (3). In contrast, students commonly believe that an excellent educator is distinct from his/her colleagues by the quality of his/her course delivery and type of his/her educational interactions with learners. (4). In essence, from learners’ point of view, quantity of teaching is not generally a privilege for a teacher. Moreover, learners are not concerned about scholarship engagement of their educators. Consequently, neither quantity of teaching activities, nor scholar engagement of educators does affect on learners’ assessment of their educational competency. What learners notice sharply and consider it well in their teacher evaluation is quality of teaching and type of educational communication, behavior and characteristics of an educator. Despite some disagreement, many educational researchers believe that learner assessment provides a legitimate measurement tool with multi criteria to rate excellent educators. The same researchers have also shown that professional ability of faculty members in course delivery, their personality and type of their communications with learners both inside and outside classrooms are of utmost plausible criteria in learner assessments (3-7). In a student-centered academic system and especially in terms of customer approval, student satisfaction is one of major evidence of a successful teaching. If learners accept their teacher professionally and emotionally, then they are ready to hear, understand, communicate with, and especially learn from him/her. We all have heard this Persian old statement: “If an educator teaches with passion and love, then he/ she can make an uninterested learner to take part in classroom even on weekends willingly”. The above statement clearly indicates that from old days, learner’s viewpoint has had a key value in enhancement of educational communication of learners as well as their enthusiasm to learn. Therefore, it seems that in a student-centered educational system, learner assessment make significant contribution to rate educators and allocates their level of excellence. The attitude of students towards teachers is highly important in American and Canadian universities. There are many local and national websites where students can vote for choosing best academic teachers. For example, site www.ratemyprofessors.com works daily to record university students’ assessments for more than 3.1 million educators. In some modern universities, learner assessments weigh up to 30-40% of a comprehensive assessment (360-Degree Assessment) aiming educator excellence (3، 7 (. This means that less weight of learner assessment, more lose of teaching quality - a criterion considered as focal point for annual teaching excellence and educator promotion. In addition, in a 360-Degree Assessment of educators, any decline weight of learner assessment, leads to wider gap between students’ selections and university administrators’ elections. This may consequently distrust students to academic educational evaluation system.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".