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

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

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

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBlameReverse engineeringOrder (exchange)Engineering educationComputer scienceWork (physics)PsychologyEngineeringMathematics educationEngineering managementEngineering ethicsSocial psychologyBusiness

Abstract

fetched live from 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.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.341

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)0.0000.000

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 head0.070
GPT teacher head0.335
Teacher spread0.265 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations34
Published2020
Admission routes1
Has abstractyes

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