Teaching Students to Think - Faculty Recommendations for Teaching Evaluations Employing Automated Content Analysis
Notice bibliographique
Résumé
Many studies have been conducted on teaching evaluations completed by students and on myths and facts concerning these evaluations performed by students at academic institutions. The current study is unique in examining the meaning of teaching evaluations as perceived by academic faculty members in Israel through direct questions, with an emphasis on faculty's recommendations for improving the evaluations to make students' comments meaningful for enhancing and advancing their teaching. The perception of evaluations is unique too. Evaluations are part of faculty's learning outputs in their courses, with the aim being for graduates of academic systems to have the ability to provide objective and fair assessments.One hundred seventy seven questionnaires were gathered from senior faculty at several academic institutions. Qualitative and statistical research tools were used in order to form a model that expresses the negative implications as seen by faculty members and alternatives for measuring the performance of faculty in academic teaching. The research findings indicate that lecturers note "professional" alternatives and see teaching evaluations as a populist rather than a professional tool. Moreover, although the lecturers gauge the damage caused to them as a result of student evaluations, where the enormous damage caused to them is disproportionate to the number of respondents, and although faculty members believe that student evaluations are untrustworthy, students' opinions on the courses are important. Their recommendation is that the evaluation should be a tool for teaching how to perform evaluations and convey criticism – and in this field not much has been done in academic institutions, if at all. Academia sees evaluations as a technical matter, a means of satisfying students by letting them express their opinions and of giving students a feeling that the system is attentive to their voice, to their views.Indeed, students' voice is important to the lecturers – their opinions of teaching are important – and that is precisely why action should be taken to render these evaluations fair. Students should understand the power of the words that express their evaluation of the lecturers. This point of view is a first of its kind, where academic faculty members support students' opinions and provide recommendations aimed at their improvement.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».