Utilizing Learning Management System (LMS) Tools to Foster Innovative Teaching
Bibliographic record
Abstract
Many post-secondary institutions utilize learning management systems (LMSs) to deliver online, blended, and face-to-face courses. LMSs have a wide variety of built-in functionality that can be used to facilitate innovative teaching. This chapter provides useful information, critical thinking questions, and insights that instructors can use to expand their adoption, knowledge, and usage of LMS tools to build upon innovative teaching practices. Three instructional approaches are discussed: case-based learning (CBL), scenario-based learning (SBL), and gamified learning. Additionally, specific examples are provided to demonstrate how LMS tools can be used to support CBL, SBL, and gamified learning. This chapter invites instructors to critically reflect on how they use LMSs and other educational technologies to carry out ineffective instructional strategies. Furthermore, it provides concrete examples of how LMS tools can help instructors improve their teaching practice and adopt creative instructional approaches with thoughtful use of technology grounded in sound pedagogical practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".