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Record W2945276602 · doi:10.3390/informatics6020022

Teaching HCI Skills in Higher Education through Game Design: A Study of Students’ Perceptions

2019· article· en· W2945276602 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

VenueInformatics · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsAlgoma University
Fundersnot available
KeywordsCreativityPerceptionTask (project management)Design educationMathematics educationPsychologyHuman–computer interactionHigher educationComputer scienceEmpirical researchEngineeringSocial psychology

Abstract

fetched live from OpenAlex

Human-computer interaction (HCI) is an area with a wide range of concepts and knowledge. Therefore, a need to innovate in the teaching-learning processes to achieve an effective education arises. This article describes a proposal for teaching HCI through the development of projects that allow students to acquire higher education competencies through the design and evaluation of computer games. Finally, an empirical validation (questionnaires and case study) with 40 undergraduate students (studying their fifth semester of software engineering) was applied at the end of the semester. The results indicated that this teaching method provides the students with the HCI skills (psychology of everyday things, involving users, task-centered system design, models of human behavior, creativity and metaphors, and graphical screen design) and, more importantly, they have a positive perception on the efficacy of the use of videogame design in a higher education course.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.050
GPT teacher head0.396
Teacher spread0.346 · 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