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Record W3102393980 · doi:10.18278/ijoer.3.2.11

Overcoming Textbook Access Barriers in an Introductory Psychology Course: An OER Study at a Hispanic-Serving Institution

2020· article· en· W3102393980 on OpenAlexaboutno aff
Adam John Privitera

Bibliographic record

VenueInternational Journal of Open Educational Resources · 2020
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsOpen educational resourcesPopulationMedical educationInstitutionQuality (philosophy)Quarter (Canadian coin)Community collegePsychologyScale (ratio)Mathematics educationPedagogySociologyMedicineSocial science

Abstract

fetched live from OpenAlex

The high cost of college textbooks is an access barrier for students to overcome during their pursuit of a college degree. Perhaps most at risk are community college students, an older, more diverse, and lower-income population in comparison with their university peers. Recently, community colleges have considered replacing traditional, commercially produced textbooks with free open educational resources (OERs). In this work, two aims are addressed. First, a small-scale investigation of the need for a low-cost textbook alternative was conducted in an introductory psychology course. In response to the finding that over a quarter of students could not afford the course textbook, a psychology OER was adapted from existing resources and piloted in three sections of this course. The second aim was to assess the impact of this OER textbook. Findings from this second survey found that the psychology OER was easy to use, was high quality, and supported students in their understanding of course content. Students also reported that the money saved from not having to buy a textbook made taking the course easier. Together, these findings support that OER textbooks are suitable replacements that can reduce the financial burden on low-income students and support them in the achievement of their academic goals.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.006
Open science0.0060.001
Research integrity0.0000.000
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 head0.056
GPT teacher head0.423
Teacher spread0.367 · 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.

Study designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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