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Record W3138631304 · doi:10.19173/irrodl.v22i1.4970

Using Open Educational Resources at Viterbo University: Faculty and Student Feedback

2021· article· en· W3138631304 on OpenAlexvenueno aff
Alissa L. Oelfke, Jennifer A. Sadowski, Cari Mathwig Ramseier, Christopher Iremonger, Katrina Volkert, Emily Dykman, Lynne Kuhl, Annie Baumann

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2021
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsOpen educational resourcesQuality (philosophy)UsabilityMedical educationComputer sciencePsychologyPedagogyMedicine

Abstract

fetched live from OpenAlex

This study evaluated a coordinated and collaborative pilot implementation of open educational resources (OER) across multiple disciplines including nursing, accounting, environmental science, religious studies, and finance. Participating faculty were qualitatively surveyed regarding their experience creating and implementing OER in a course. Students were surveyed on their perceptions of OER quality, cost savings, and ease of use. Faculty had an overall positive experience with OER, believing there was a significant benefit to students in cost savings while maintaining learning quality. Faculty felt the OER implementation process took a significant investment of time and recommended that faculty should be compensated for creating and implementing OER materials in future courses. Students overall showed positive responses to using OER in their course; the majority of students agreed with the OER cost savings, quality of OER resources, ease of using OER, and they trusted the use of OER materials. Older students (over 30 years) were more likely to state they would print out OER materials rather than read them online (as compared with students 30 and under). Senior-level students agreed significantly more than did freshman-level students that OER presented a cost savings. Faculty recommendations from this study included focusing on courses with very high textbook costs and courses that would impact the greatest number of students. Additionally, faculty recommended a follow-up revision process to keep OER materials current after implementation.

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 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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.006
Research integrity0.0000.000
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 head0.152
GPT teacher head0.473
Teacher spread0.321 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations10
Published2021
Admission routes1
Has abstractyes

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