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Record W3138243901 · doi:10.5539/mas.v15n2p45

The Magic of Project Resolution in a Short Period of Time: Design Sprint Applied in Higher Education

2021· article· en· W3138243901 on OpenAlexvenueno aff
Gastón Sanglier, C.B. Martínez Cepa, Inés Serrano Fernández, Aurora Hernández González, Juan Carlos Zuíl Escobar

Bibliographic record

VenueModern Applied Science · 2021
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsnot available
FundersUniversidad San Pablo - CEU
KeywordsSprintSet (abstract data type)Work (physics)Multidisciplinary approachAdaptation (eye)Computer scienceAbandonment (legal)PsychologyMathematics educationMedical educationSociologyEngineeringMedicinePolitical science

Abstract

fetched live from OpenAlex

The research conducted in this study was applied to multidisciplinary groups of Higher Education belonging to different degrees using the methodology proposed by the Design Sprint (DS) tool for the achievement of different challenges/objectives in a very short time. The methodology used is an adaptation of the one proposed by the DS, carried out in five non-consecutive stages/sessions focused on students of the first two years of different degrees of the CEU San Pablo University. The students, in general, have valued very positively the collaborative work in small groups, the time management and the administration of work under stress. The abandonment of the different challenges was set at 32%. A high level of commitment has been appreciated among the students to reach the proposed challenges, however, the work of the mentors as guides, becomes essential in the first courses of the different degrees in a general way. The application of the SD methodology provides students with an increase in their performance, in their ability to work in teams and to adapt in the best possible way to the demands of a society that is increasingly demanding new technologies. Students have increased their ability to reflect, transform and innovate in the different objectives/challenges/projects demanded by the new circumstances and social strategies. The support of good mentors, critics and specialists in the different areas to be addressed is necessary to offer students a better learning experience.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.283
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2021
Admission routes1
Has abstractyes

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