MétaCan
Menu
Back to cohort
Record W4292771936 · doi:10.3102/1446462

Understanding Students' Game Design: A Complexity Perspective

2019· article· en· W4292771936 on OpenAlexaffabout
Reyhaneh Bastani

Bibliographic record

VenueProceedings of the 2019 AERA Annual Meeting · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPerspective (graphical)Computer scienceGame designGame theoryHuman–computer interactionArtificial intelligenceMathematicsMathematical economics

Abstract

fetched live from OpenAlex

This paper explores the conception of learning as a complex phenomenon and the design of learning environments with insights from complexity research.We used design considerations from a complexity perspective to explore a structure that invited students to the collaborative design of games.We discuss our research on students' design of card/board games for their disciplinary and interdisciplinary learning in a western Canadian middle school.Our analysis highlights the variations in the students' designed games while they rely on their available resources to construct and communicate individual and collective meanings.We argue that engaging students in the design of games provide the opportunities for them to experience and understand the complex and interdisciplinary nature of any school topics.creativity and interest as well as new opportunities for collective learning (Davis & Sumara, 2006).Our use of complexity perspectives in this paper, is mainly focused on this pragmatic approach.We explore the affordances of learners' collaborative design of games based on our research in a western Canadian middle school, using the design conditions elaborated by complexity perspectives. Complexity perspectives of individual and collective learningComplexity research probes how diverse entities (e.g., living organisms) interact and shape systems with collective emergent behaviors (e.g., ecosystems) (Mitchell, 2009).These entities or agents adapt or learn, as they gain experience in interaction with one another and with new circumstances.Davis and Sumara (2010) discussed that complexity assesses the complementarities of the perspectives on individual and collectivie learning, elaborating on the interconnections of the systems of individual sense making and collective understanding.From this view, language, culture, social relations, and artifacts, which are of interest in sociocultural theories, act as the context of individual understanding, which emerges out of the interaction of sets of ideas.Complexity views indicate that such emergent phenomena cannot be pre-set, but more possibilities for learners' understanding and action might be stimulated through certain conditions.The following describes our design framework, informed by Davis and Sumara's ( 2006) conditions for emergent learning systems. Framework for Designing Learning Environments with Complexity PerspectivesA fundamental strategy for supporting learning communities has been suggested as setting enabling constraints.This notion is associated with complex systems' being "simultaneously rule-bound (constrained) and capable of flexible, unanticipated possibilities (enabled)" (Davis et al., 2015, p. 219).Designs for learning, then, entail a balance between sufficient structure to constraint the vast possibilities and sufficient openness to enable diverse responses.This approach underlies other interrelated conditions that include randomness, coherence, diversity, redundancy, and decentralized control (Davis & Sumara, 2006).We suggest that these conditions could move from a more conceptual level, at the top, towards more practical advice, at the bottom (Figure 1).Enabling constraints suggest a balance between coherence that supports the collective to keep its purpose and identity, and randomness that allows it to adapt and evolve (Figure 1, level 1).The elements in complex phenomena act within certain frames that both make possible and constrain their actions, similar to how individual players act within rules of games.Commonalities of agents, or the redundancy within the system, enable their interactivity and the system's sustainment.On the other hand, internal diversity is about agents' expressing their creativity, enabling the system to respond to new circumstances (levels

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.101
GPT teacher head0.350
Teacher spread0.250 · 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 designTheoretical or conceptual
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

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the 2019 AERA Annual MeetingSame topicEducational Games and GamificationFrench-language works237,207