Moving Toward a Universal Design for Learning Mindset: A Case Study Transforming a Pre-Service Teacher Field
Bibliographic record
Abstract
Contemporary learning in higher education embraces an array of instructional strategies and approaches, including online and blended learning. Blended learning involves between 30-79 percent of the class occurring in an online environment (Allen & Seaman, 2013). As we design and develop online and blended learning environments, consideration needs to be given to the three principles of Universal Design for Learning (UDL): 1) “provide multiple means of engagement”; 2) “multiple means of representation”; and 3) “provide multiple means of action and expression” (p. 89). The integration of UDL principles in learning helps to facilitate motivation, persistence, self-regulation, personalization of learning, and learning community participation (Meyer et al., 2014). In this interactive session, we will share a UDL approach that conceptualized a framework for planning, implementing, and assessing a University of Calgary blended learning approach used for a pre-service teacher education field experience course. The participants of the session will engage in a discussion focused on the following questions: 1) What factors influence the shift of using UDL principles in designing online and blended learning; and 2) What key strategies support the implementation of UDL principles in online and blended learning environments (i.e., design, develop, and evaluate)? The aim of this session is to examine of how principles of UDL enhance learning for all students in online and blended environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".