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Enregistrement W2605231070 · doi:10.18260/1-2--21419

Gamification as a Strategy for Promoting Deeper Investigation in a Reverse Engineering Activity

2020· article· en· W2605231070 sur OpenAlexaff
Jason Foster, Patricia Sheridan, Robert Irish, Geoffrey Frost

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomainePsychology
ThématiqueEducational Games and Gamification
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésBlameReverse engineeringOrder (exchange)Engineering educationComputer scienceWork (physics)PsychologyEngineeringMathematics educationEngineering managementEngineering ethicsSocial psychologyBusiness

Résumé

récupéré en direct d'OpenAlex

Abstract Gamification as a Strategy for Promoting Deeper Investigation in a Reverse Engineering ActivityThis paper explores the impacts of gamification on students’ investigations in a reverseengineering activity. This existing activity was gamified in order to promote increasedmotivation and to provide additional scaffolding, in order to push students into explorations thatwent beyond simplistic critique.For the past four years, freshmen students in the [name of engineering program] at University of[Name] have engaged in reverse engineering activity in the first month of their freshman year.Referred to as “device teardowns”, students have been challenged to develop an understanding ofhow design decisions are made and the trade-offs involved in realizing a work of engineeringdesign. This challenge differs from that in a more traditional reverse engineering exercise, inthat the focus is on the design of the device and not on how it functions. Students engage in twoiterative teardowns of household electromechanical devices (e.g. toasters, blenders). The resultsof the teardowns are not themselves assessed, but the evidence gathered by the students duringthe activities is used as the basis for a written report. In previous years, students have reportedenjoying the exercise, but were observed not pushing themselves to explore ideas beyond themost obvious. For instance, they would quickly blame “cost” on any design decisions thatseemed to them substandard.In the most recent iteration of the exercise, we created a game whereby students were awardedachievement levels for (1) practicing safety, (2) developing an understanding of key designdecisions (construed as Design for X [DfX]), and (3) making inferences for logicalargumentation. In Jane McGonigal’s recent work, she suggests that gamers are much less likelyto quit on a challenge because in the game world they not only believe they can figure it out, butalso that the reward for doing so is significant [1]. Although the teardowns did not directlyinclude significant rewards, we employed gamification to challenge students to achieve a broaderset of tasks and to achieve these tasks in deeper and more nuanced ways.By presenting the teardown as a set of achievements that could be earned (and acknowledged bythe teaching team using simple stamps on a paper record) we created an environment of play inwhich students appeared more committed and more deeply engaged than in previous years. Byexplicitly integrating argument into the process of earning achievements, students were pushedto continually construct logical and well-reasoned cases for their understanding of the designdecisions they had identified. Students had to present their achievements to the teaching teamand demonstrate that they had both reached the achievement and understood what it meant to doso. The interactive approach allowed the teaching team to question the students and to demandanswers from any member of a team. The activity as a whole enhanced the students’collaboration, their ability to handle rebuttals and make solid arguments based on physicalevidence, and their understanding of the significance of DfX.In exploring the impacts of the gamification, we investigate the teaching team experience of theteardowns, and in particular the natrure of their interations with the students, the students’perception of the quality of the experience, and the students’ results in the form of a writtenreport on their device teardown. We found improvement in all three areas over previousiterations of the activiy, with the most notable improvement in the students’ use of argument.Through the teardowns, the students, who had only recently been introduced to the Toulminmodel of argumentation [2] as a theoretical construct, developed a solid understanding ofevidence-based argument as grounding for engineering design and communication.[1] McGonigal, Jane. Reality Is Broken: Why Games Make Us Better and How They Can Changethe World. Penguin, 2011.[2] Toulmin, Stephen. Uses of Argument. Cambridge, 1958.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,043

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,025
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,001
Études des sciences et des technologies0,0010,002
Communication savante0,0050,004
Science ouverte0,0020,007
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,070
Tête enseignante GPT0,335
Écart entre enseignants0,265 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations34
Publié2020
Routes d'admission1
Résumé présentoui

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