MétaCan
Menu
Back to cohort
Record W4410193770 · doi:10.19173/irrodl.v26i2.8117

Undergraduate Learning Gains and Learning Efficiency in a Focused Open Education Resource

2025· article· en· W4410193770 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2025
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsOpen learningEducational technologyOpen educationComputer scienceDistance educationOpen educational resourcesResource (disambiguation)Electronic learningMathematics educationMultimediaKnowledge managementTeaching methodPsychologyCooperative learningWorld Wide Web

Abstract

fetched live from OpenAlex

The high cost of commercial textbooks in higher education creates barriers to equitable access to learning materials and negatively impacts student performance. Open educational resources (OER) offer a cost-effective alternative, but their impact on student learning remains a critical question. This study directly compared student outcomes between OER and commercial textbooks in a controlled reciprocal design. Forty undergraduate participants completed reading tasks and knowledge assessments using both textbook types, focusing on topics in DNA structure and function and population ecology. Results showed no significant differences in learning gains between OER and commercial textbooks, consistent with prior research. However, participants spent significantly less time on task when using the shorter, learning objective-aligned OER readings, particularly for jargon-heavy DNA content. These findings highlight the potential of OER to reduce cognitive load and improve efficiency without compromising learning outcomes. Future research should explore the role of textbook alignment, length, and student preparation strategies in optimizing learning with OER, particularly in flipped classroom contexts. This study supports OER adoption as a cost-saving measure that maintains academic integrity while enhancing accessibility and efficiency.

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)0.009
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.811
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.002
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 head0.088
GPT teacher head0.471
Teacher spread0.383 · 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