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Record W4388777662 · doi:10.47852/bonviewijce32021859

Anxiety and Self-Efficacy in STEM Education: A Scoping Review

2023· review· en· W4388777662 on OpenAlexafffund
Emma Christensen, Libby Osgood

Bibliographic record

VenueInternational Journal of Changes in Education · 2023
Typereview
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsUniversity of Prince Edward Island
FundersUniversity of Prince Edward Island
KeywordsAnxietyAttritionTimelineInclusion (mineral)ScopusPsychologyCurriculumMedical educationMedicinePedagogyMEDLINESocial psychologyMathematicsPolitical sciencePsychiatryDentistryStatistics

Abstract

fetched live from OpenAlex

With the increasing inclusion of STEM activities across the K–12 curriculum, it is vital for educators to understand barriers and resistance to learning each of the elements of STEM: science, technology, engineering, and math. Anxiety and self-efficacy for students and teachers have been identified as causes of STEM avoidance; however, this research is not distributed evenly across the elements. Therefore, this study documents potential causes for anxiety of each element, followed by a scoping review for each element of STEM-focused teacher anxiety, one of the major causes of STEM anxiety. The scoping review was guided by PRISMA standards and completed twice: once in Educational Resources Information Center and once in Scopus, with results demonstrating 94%–100% inter-rater reliability. It was found that causes of anxiety differ between the elements, and the scoping review revealed: (1) research on engineering anxiety and i-STEM or integrated STEM is lacking in comparison to the other elements, (2) the term “anxiety” is more established in reference to math than to the other elements, (3) technology appears in research as a tool more often than an area of research, and (4) the timeline of publication dates varies between the elements of STEM. These differences point to the need for more research in the underrepresented elements to develop intervention methods for teachers in order to reduce student attrition rates in STEM 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 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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.102
GPT teacher head0.481
Teacher spread0.379 · 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 designOther design
Domainnot available
GenreReview

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

Citations6
Published2023
Admission routes2
Has abstractyes

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