Examining teaching clarity: Student engagement and faculty practice
Notice bibliographique
Résumé
Although clarity of instruction is generally promoted as an effective teaching practice, we know little about how widely students are exposed to this practice in undergraduate education, and even less is known about the extent to which different faculty emphasize clarity in their teaching.In addition, little research has been done to link teaching clarity to other forms of effective educational practice such as student-faculty interaction or active and collaborative learning.This study explores the teaching clarity (TC) behaviors students are exposed to and the extent to which these behaviors relate to student engagement, deep learning, and self-reported gains in college using data from the National Survey of Student Engagement.Using complementary data from the Faculty Survey of Student Engagement, this study further explores TC by examining faculty perceptions of the importance of TC as well as the relationship between TC and faculty use of other effective educational practices. Student Data Source and Sample The data come from the 2010 administration of the National Survey of Student Engagement The sample for this study consists of 8102 (41%) first-year students and 11,761 (59%) seniors from 38 baccalaureate granting institutions The teaching clarity items used in this study were adapted from the Wabash National Study Faculty Data Source and Sample The data come from the 2011 administration of the Faculty Survey of Student Engagement The sample for this study consists of nearly 4,400 faculty members from 40 different colleges and universities (two institutions were Canadian) Student ResultsResearch Question 1: What TC behaviors are students exposed to most and least frequently?Percent of Frequently ("Very Often" or "Often") Observed TC Behaviors:Frequencies of teaching clarity items were used to identify which behaviors were "frequently" ("very often" or "often") observed.More frequently observed behaviors were instructors coming to class well-prepared and instructors explaining course goals and requirements clearly.Less frequently observed behaviors include instructors reviewing and summarizing course material effectively and instructors making abstract ideas and theories understandable.Frequencies by disciplinary major fields can be found on our website. Research Question 2: How does TC relate to student engagement?Pearson's r correlations were used to relate the Teaching Clarity Scale to individual NSSE engagement survey items and NSSE Benchmarks of Effective Educational Practice.For both firstyears and seniors, the items with the highest correlations with the Teaching Clarity scale were about students' ratings of their relationships with faculty members, of their institution's emphasis on providing the support they need to succeed academically, and of their entire educational experience at their institution.Correlations between the Teaching Clarity Scale and NSSE Benchmarks can be found below (p < .001).Information about the NSSE Benchmarks can be found on our website. Research Question 3: How does TC relate to deep learning and students' reports of gains in college?Multivariate OLS regressions were used to measure the relationship between the Teaching Clarity scale and measures of deep learning and student-reported gains.The unstandardized coefficients represented below can be interpreted as effect sizes.For both first-years and seniors, the strongest relationships occur between the Teaching Clarity scale and student self-reported gains in college.More information about the scales used in this analysis as well as regression results by major disciplinary field can be found on our website.OLS regression models controlled for gender, transfer status, enrollment status, fraternity or sorority membership, athletic participation, race or ethnicity, primary major field, grades, firstgeneration status, age, institutional control, and institutional Carnegie Classification.All variables standardized before entry into models.Key: all p < .001;+ unstd B > .2,++ unstd B > .3,+++ unstd B > .4,++++ unstd B > .5.
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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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,002 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».