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Record W7038509216

Instructional Designers and Their Use of Self-Regulated Learning Practices in Higher Education

2024· dissertation· en· W7038509216 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2024
Typedissertation
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsnot available
Fundersnot available
KeywordsInstructional designHigher educationLifelong learningBachelorQuality (philosophy)PerceptionEducational technologyOnline learningLearning sciences
DOInot available

Abstract

fetched live from OpenAlex

Instructional Designers are informal leaders in higher education and play a critical role in maintaining the high quality of education expected from these institutions. Yet, there is minimal research on Instructional Designers and their integral role in higher education. The purpose of this study was to investigate how Instructional Designers use self-regulated learning (SRL) strategies in their practice. The study used a qualitative approach with multiple methods – semi-structured interviews and review of an online learning module. Five Instructional Designers at a university in Ontario, Canada were interviewed to gain insights about their perceptions of SRL and their use of SRL strategies in instructional materials. Participants reviewed an online learning module to demonstrate and apply their knowledge of SRL, identifying features within the module that would prompt SRL skills in students. Results of the study suggest that Instructional Designers have some form of declarative and procedural knowledge of SRL. Participants described SRL as critical for students in terms of designing personalized learning plans, becoming lifelong learners, and developing critical thinking and problem-solving skills. Participants incorporated SRL into their work through the design of courses and of online learning modules. In addition, participants were able to identify various features within the online learning module that would prompt SRL in students. However, this knowledge of SRL is not the result of instructional design training, but other educational and professional endeavors undertaken before entering the profession (e.g., Bachelor of Education, Master of Education, independent professional development). Most Instructional Designers use their professional experiences, intuition, observations of what they have seen others do, and past experiences as a learner in combination with learning theories and SRL strategies to design courses and instructional materials in higher education.

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 imitation

Not 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.

metaresearch head score (Codex)0.058
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.309

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.094
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0040.012
Scholarly communication0.0100.004
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.220
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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