Navigating the Tensions of Innovative Assessment and Pedagogy in Higher Education
Bibliographic record
Abstract
Innovative practice in a classroom adds challenges and tensions to programs and institutional structures in higher education. With the recent emphasis on curricula reform, there is a great focus on assessment and pedagogical practices to support student learning. To illustrate the tensions arising from these efforts, we present four pedagogical and assessment innovation approaches using both Shulman’s (2005) Signature Pedagogies and Tatar’s (2007) Design Tensions frameworks. The four approaches include problem-based learning, game-based learning, case-based learning, and technology-enhanced learning. A narrative for each approach examines and addresses tensions using Shulman’s (2005) surface, deep and implicit structures. We argue that there is an interconnected complexity and conflicting visions among the micro- (e.g., classroom or practicum), meso- (e.g., program), and macro- (e.g., institution) levels. We acknowledge that dynamic tensions continually exist and needs to be thoughtfully navigated in support of innovative assessment and pedagogies in higher education. Dans l’enseignement supérieur, les pratiques innovatrices en salle de classe ajoutent des défis et des tensions aux programmes et aux structures institutionnelles. Suite à l’importance accrue récemment attachée à la réforme des programmes d’études, l’accent est mis sur l’évaluation et les pratiques pédagogiques pour soutenir l’apprentissage des étudiants. Afin d’illustrer les tensions qui découlent de ces efforts, nous présentons quatre approches de pédagogie et d’évaluation innovatrices qui font appel à la fois aux cadres de Shulman, Signature Pedagogies (2005), et à ceux de Tatar, Design Tensions (2007). Les quatre approches comprennent l’apprentissage par problèmes, l’apprentissage fondé sur le jeu, l’apprentissage basé sur des cas et l’apprentissage amélioré par les technologies. Chaque approche est examinée et traite des tensions qui en découlent en faisant appel aux structures de surface, profondes et implicites de Shulman (2005). Nous soutenons qu’il existe une complexité inter-connectée et des visions conflictuelles aux niveaux micro (par ex. en salle de classe ou durant les stages), meso (par ex. dans les programmes) et macro (par ex. au niveau de l’établissement). Nous reconnaissons que les tensions dynamiques existent de façon continue et doivent être soigneusement examinées pour soutenir l’évaluation et les pédagogies innovatrices dans l’enseignement supérieur.
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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 | 0.079 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.013 | 0.077 |
| Scholarly communication | 0.036 | 0.038 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".