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Record W2889108366 · doi:10.24059/olj.v22i3.1467

Student Perceptions of the Most Effective and Engaging Online Learning Activities in a Blended Graduate Seminar

2018· article· en· W2889108366 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOnline Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsConcordia University
FundersConcordia University
KeywordsBlended learningMedical educationPerceptionPsychologyQuality (philosophy)Mathematics educationGraduate studentsComputer sciencePedagogyEducational technologyMedicine

Abstract

fetched live from OpenAlex

The principal concern of this research was to learn more about effective designs of learning activities in blended courses. A questionnaire was administered in three sections of a not-for-credit intensive blended graduate seminar in university teaching. The online activities included readings, videos, discussion forum activities and other activities using a range of web-based technologies. Students rated each of the activities on four target criteria: alignment with the course learning outcomes, deep learning, engagement, and value. Students also were asked to identify the most useful activities for each of the five modules and evaluate the course as a whole in terms of navigation, expectations, instructions, availability of materials, instructor presence, and technical quality of media. The results suggest that students’ perceptions of the activities followed very similar patterns across the four target criteria. The most highly-rated activities had four distinct design, which are discussed.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.348
Teacher spread0.330 · 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