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Record W4360986251 · doi:10.5430/jct.v12n2p154

Methods of Teaching Graphic Design in HEIs for Art

2023· article· en· W4360986251 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldComputer Science
TopicDigital Media and Visual Art
Canadian institutionsnot available
Fundersnot available
KeywordsGraphic designDesign educationEnvironmental graphic designCreativityComputer scienceCommunication designProduct designHigher educationResource (disambiguation)MultimediaProduct (mathematics)Human–computer interactionPsychologyVisual arts

Abstract

fetched live from OpenAlex

Under the conditions of digitalization in technology and art, there is a sense of significant changes in visual creativity, where design projects are given not only new artistic expressiveness, but everything becomes an immeasurable resource base and source of inspiration for future graphic designers. This new reality encourages the permanent updating of approaches and the use of innovative methods of teaching graphic design in HEIs for art in the process of preparing for the professional activity of a future graphic designer. Therefore, a modern graphic designer, being the creator of a demanded graphic product that meets the requirements of business and clients, must have a sense of style, and artistic taste, track new trends in graphic design, be versed in styles, be creative, able to create original innovative design projects, have technical knowledge and computer programs, etc. The research aims to establish a pattern of promoting the use of differentiated methods of teaching graphic design in higher education institutions for art. The goal can be achieved by surveying higher education students on the Internet to determine the ability of HEIs for art to apply effective methods of teaching graphic design. Research methods: comparative analysis; systematization; generalization; survey. Results. The survey among higher education students found that online graphic design platforms such as Canva (98.3%), Pixlr (94.1%), Design Wizard (93.8%), Visme (92.4%), Snappa (89.8%), BeFunky (89.2%), etc. contribute to the formation of professional skills in the field of graphic design. It has been found that the following graphic design programs on PCs, namely Adobe Photoshop (95.5%), Affinity Designer (95.3%), Gravit Designer (91.5%), Adobe InDesign (89.9%), Adobe Illustrator (89.5%), best contribute to the formation of professional skills in future graphic designers. It has been determined that 3D graphic design programs such as Paint3D (91.9%), Autodesk Maya (89%), Sumo3D (87.9%), Blender (85.7%), Autodesk 3ds Max (83.3%), ZBrush (81.5%), etc. are the most conducive to the formation of professional skills. The study has revealed which methods of teaching graphic design in HEIs for art are used by teachers. These include innovative methods (91.8%), the method of graphic modeling (89.8%), the method of observation and independent reproduction of certain artistic images through graphic design (87.3%), the method of graphic illustration (86.2%), the method of projective and graphic form finding (81%), etc. It has been established that the successful implementation of differentiated methods of teaching graphic design in HEIs for art contributes to the formation of skills in using modern computer graphics programs to create design objects (91.2%), the ability to use basic skills in project graphics (89.3%), the ability to know color science to create a coloristic solution for a future design object (89%), etc.

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)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.823
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.042
GPT teacher head0.376
Teacher spread0.335 · 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