Transparent Assessments: The Potential Benefits of Training Students in the How and Why of Evaluation
Notice bibliographique
Résumé
The ideal assessment accurately measures student knowledge and skills without causing debilitating anxiety. This study investigates the impact of two interventions on student anxiety, perceptions, and performance: increased transparency in evaluative techniques prior to the assessment, and working with peers during the assessment. Evaluation anxiety can reflect discrepancies between self‐perceived assignment quality and the received mark, which may reflect instructors' use of tacit (i.e. knowing good work when I see it) as well as explicit knowledge (e.g. marking schemes). The impact of increased knowledge transfer from instructor to students was examined in an upper year Physiology seminar class using concept maps as summative evaluations. Students engaged in a training procedure involving rubric presentation (explicit knowledge transfer), small group evaluations of exemplar concept maps, and an instructor‐led discussion of the exemplars (tacit knowledge transfer). Students found the instructor‐led discussion the most helpful of the three steps. Students consistently ranked concept maps as better performance indicators than multiple choice exams, but results varied as to their anxiety‐provoking potential. Working in groups was only deemed useful if students already had a preliminary concept map to discuss. In comparison to students in previous classes who were not provided explicit training, students reported more positive perceptions towards concept mapping and generated higher quality maps. The second study involved a large, inquiry‐based, upper year Physiology course using homework, group quizzes, and individual multiple choice and short answer exams. Since these evaluative techniques are relatively straightforward, we focused on differences between individual and group assessments. Group quizzes invoked less anxiety than individual exams in all students, but the difference was greater in self‐reported B students than in self‐reported A students. Yet, B students considered these assessments poorer indicators of their knowledge than A students. Comments indicated that A students worry about “free‐loaders”, and B students often feel that quiz results reflect the smartest student's knowledge, not their own. Explicit instructor explanations of the formative advantages of group quizzes, such as self‐elucidation for stronger students and peer teaching for weaker students, might increase student buy‐in. These results highlight the importance of explicit instructor explanations of how and why instructors evaluate student work. Support or Funding Information This work was supported in part by the Senate Research Committee of Bishop's University. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0,023 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».