MétaCan
Menu
Back to cohort
Record W2809387589 · doi:10.5539/jel.v7n5p52

Applying Design Thinking as a Method for Teaching Packaging Design

2018· article· en· W2809387589 on OpenAlexvenueno aff
Chao-Ming Yang

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Packaging Perceptions and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsDesign thinkingCreativityPackaging engineeringCreative thinkingProduct (mathematics)Visual thinkingProduct designCritical thinkingComputer scienceDesign educationPackaging and labelingMeaning (existential)Mathematics educationManagement sciencePsychologyEngineeringHuman–computer interactionMathematicsMechanical engineeringSocial psychologyAdvertising

Abstract

fetched live from OpenAlex

Design thinking is a human-centered creative method that can be used to seek innovative solutions for life and social topics. Moreover, design thinking can enable developing innovative ideas that can satisfy consumer needs. A packaging design course is a professional course that combines material application, design aesthetics, and branding. It is also a comprehensive science course that emphasizes developing students’ creative thinking and the ability to use practical technologies. This study applied an experimental teaching method to introduce design thinking in a packaging design course. The aim was to guide students to identify problems from the perspectives of product packaging, brand image, spatial structure, and marketing. Students were expected to be able to reconsider the meaning and importance of packaging design and thus enhance their structural creativity, visual aesthetics, and design thinking, in addition to improving their rational analysis and design problem-solving abilities.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.040
GPT teacher head0.341
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations26
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Education and LearningSame topicConsumer Packaging Perceptions and TrendsFrench-language works237,207