The Role of Authentic Assessment Tasks in Problem-Based Learning
Bibliographic record
Abstract
Problem-based Learning (PBL) has long been touted as an effective pedagogical approach in higher education to promote students’ authentic learning. As a learner-centered pedagogy, PBL is characterized by students working collaboratively in small groups to solve messy, ill-structured problems that mirror real-world problems encountered by expert professionals in the field. Students are also expected to engage in self-directed learning. PBL instructors play a pivotal role as facilitators of learning. Authentic assessment is deemed to be a viable method in PBL-oriented courses because of its focus on real-world problems. However, little is known about how instructors in higher education institutions perceive the importance of and their satisfaction in using authentic assessment in PBL-oriented courses. Specifically, what instructional decisions do they make to guide their students to use authentic assessment tasks to promote assessment for learning and assessment as learning? In this paper, we reported on instructors’ perspectives of using authentic assessment tasks to engage first-year student teachers in an assessment course.
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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 | 0.026 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".