Instructional Strategies and Learning Technologies to Support Student Learning
Bibliographic record
Abstract
The use of learning technologies is prevalent in post-secondary education and can provide opportunities for learning in different ways (Bernardo & Duarte, 2020; Johnson etal., 2014). Integration of learning technologies requires an understanding of learning technologies within the context of “what it takes to learn” (Laurillard, 2009, p. 7) in a postsecondary environment.In this study, a generic qualitative research approach was used to explore instructional strategies and learning technologies instructors used in their teaching. Purposeful sampling was used to select 12 instructors teaching at a university in Alberta. Recorded interviews obtained in-depth information about the experiences of instructors. I used a self-reflexive journal to document my opinions and as a way to review and refine my research. Laurillard’s Conversational Framework (2009, 2013) was used as a theoretical framework. Data analysis identified themes pertinent to my research question and theoretical framework.Instructors used a variety of instructional strategies and learning technologies to present concepts to students, design opportunities for students to clarify their understanding of course concepts, and create engaging practice tasks. Instructors integrated learning technologies into their teaching in ways that recognized the benefits of learning technologies and non-technological strategies.A learning-centred framework was created to capture themes and included teacher and student conceptions, safe teaching and learning environments, learning technologies, and workload. I provided recommendations for university administrators, instructors, professional development leaders, and researchers and concluded with additional questions around policy and practice.
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".