Data Dashboards to Support Facilitating Online Problem-Based Learning
Bibliographic record
Abstract
Problem-based learning (PBL) is an instructional approach that begins with a complex and ill-structured problem; in small groups, students collaboratively engage in cycles of problem formulation and analysis, selfdirected learning, and evaluation of their ideas. Over the last decade, student-generated data and metadata has been increasingly monitored, analyzed, and interpreted to inform instructors’ understanding of student learning. This practice, referred to as learning analytics (LA), allows instructors to make informed decisions. Early LA efforts focused on use of available data to predict student outcomes. However, researchers are calling for LA use and research to be more substantially informed by learning and instructional theory. This study describes the design and enactment of pedagogy-specific LA, which presents a visual dashboard to facilitate PBL instructors in their understanding of student learning activity. We present the design of the HOWARD (Helping Others with Argumentation and Reasoning Dashboard) environment that supports both students and instructors in PBL. In this research, we focus on the challenges for instructors in incorporating LA tools into their instructional practices, and discuss implications for design and use of LA.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".