Methods of Teaching Graphic Design in HEIs for Art
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".