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Impact of the Quick-Response Code Based In-Class Assignments on Students Engagement and Retention

2017· article· en· W2772966946 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePapers on postsecondary learning and teaching. · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of CalgaryMemorial University of Newfoundland
Fundersnot available
KeywordsClass (philosophy)Computer scienceCode (set theory)Mathematics educationPsychologyProgramming languageArtificial intelligence

Abstract

fetched live from OpenAlex

The paper describes a teaching technique alternative to online-based student response systems. We explore the potential of quick response (QR) code sheets as a tool for formative assessment, feedback, as well as a way to increase class engagement, student participation, information retention, and as a method to develop communication skills. The technique is neither institution nor course bound and can be implemented in classes of numerous sizes and levels. While it resembles more traditional teaching methods than computerized student response systems, it is student centered and accommodates digital natives’ approach to information gathering.

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)0.012
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.413
Teacher spread0.368 · 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