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Record W2226718308 · doi:10.1080/2331186x.2015.1127745

College-based case studies in using PowerPoint effectively

2016· article· en· W2226718308 on OpenAlexfundno aff
Yukiko Inoue-Smith

Bibliographic record

VenueCogent Education · 2016
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsnot available
FundersBritish Columbia Institute of Technology
KeywordsVariety (cybernetics)Mathematics educationPsychologyInstitutionTeaching methodGraduate studentsStyle (visual arts)PedagogySociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

This study reexamined PowerPoint’s potential to enhance traditional pedagogical practices in higher education. The study addressed (1) the conditions under which PowerPoint meets students’ needs in typical lecture-based classrooms, (2) whether professors consider PowerPoint-based lectures more effective than lectures supported by material on chalkboards, and (3) whether PowerPoint is the best tool for what professors want to accomplish in the classroom. The study’s participants were seven faculty members at a four-year US Land Grant institution in the western Pacific serving both undergraduate and graduate students. The participants represented a variety of teaching disciplines from Psychology to English and from Art to Political Science. In the study, data were obtained through non-participant observations and follow-up questions. The findings of this study suggest the ways of using PowerPoint to meet students’ needs, as well as the professor’s needs, by shifting from a passive, teacher-centered (thus lecture-style) classroom to an interactive, student-centered classroom.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.004
Scholarly communication0.0050.005
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.119
GPT teacher head0.467
Teacher spread0.348 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations24
Published2016
Admission routes1
Has abstractyes

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