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Record W4400837522 · doi:10.17770/etr2024vol2.8089

ALGORITHM FOR IMPLEMENTING QUEST TECHNOLOGIES IN RESEARCH WORK WITH PRESCHOOL AND PRIMARY SCHOOL CHILDREN

2024· article· en· W4400837522 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

VenueEnvironment Technology Resources Proceedings of the International Scientific and Practical Conference · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicForeign Language Teaching Methods
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsWork (physics)Primary (astronomy)Mathematics educationPsychologyComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

The origin of the definition "quest" and the use of quest technologies in traditional education have been well theoretically analysed and substantiated. In particular, a quest could be a type of collective creative work, scientific/educational competitions or excursions in the traditional form of organising educational activities. However, with the transition to digital education, the implementation of quest technology has changed. It continues to be seen as a motivational tool for learning, a creative form of work in the educational programme. But, according to our observations, this type of work is declining in the online format. We will discuss the reasons for this and options for replacing quest technologies in this description. It is worth noting that in order to achieve the goal of our study, we turned to the basic requirements for organising a Web Quest, recommendations for its design, convenience according to the age category of children, clarity of instructions, etc. The next step was to compare online education technologies that had similar characteristics to the quest (computer games, educational applications and platforms, RPGs, video quests, VR audiences, etc.) This allowed us to draw a conclusion about their common roots and concepts, as well as to understand the reasons for the decline in scientific interest in the topic of the quest itself. After analysing the available open access publications, we concluded that this is not enough to understand the reasons for the decline in interest in this form of online work. After all, street quests are still popular. That is why we interviewed students specialising in preschool and primary education who had the opportunity to create Web Quests and participated in them. The feedback from future specialists on the implementation of quest technology in online and offline formats directed us to describe the reasons for refusing this technology in the online format of the educational process and allowed us to confirm the list of possible substitutions for quests for the research activities of preschool and primary school students suggested above.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score1.000

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 studies0.0010.003
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.051
GPT teacher head0.370
Teacher spread0.318 · 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