MétaCan
Menu
Back to cohort
Record W2947380431 · doi:10.2196/14342

A Badge Design Framework for a Gamified Learning Environment: Cases Analysis and Literature Review for Badge Design

2019· article· en· W2947380431 on OpenAlexvenueno aff
Sungjin Park, Sang‐Kyun Kim

Bibliographic record

VenueJMIR Serious Games · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaNational Research Foundation
KeywordsPsychologyValue (mathematics)Applied psychologyScale (ratio)Mathematics educationComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: In the past, the educational badge was an extrinsic means of rewarding the motivation to learn. Based on continued research, however, the badge began to be recognized as a scale to measure the learner's knowledge and skill and an important means of helping learners to gradually build intrinsic motivation by using certain extrinsic motivators. As the badge's value has grown, the importance of its design has garnered attention. OBJECTIVE: The objective of this research was to establish a badge design framework that can be used in a gamified learning environment. METHODS: Data were collected from previous studies on badge design, 943 badge cases were extracted from 11 online and offline gamification in education contents, and their patterns and features were analyzed. RESULTS: Based on the analysis of results from previous studies and 943 collected badge cases, our study suggests three conditions for badge design. Through the literature review and collected badge cases, our study designed a badge design framework. First, it is necessary to distinguish whether the type of learning activity required for earning badges is physical or conceptual. Second, it is necessary to distinguish whether the scale of an activity required for earning badges requires individual learning or interaction-induced learning. Third, it is important to review whether the time and effort invested in earning badges is simple, repetitive, and short-term or continuous and long-term. Based on these three conditions, collected badge cases were analyzed. To verify self-developed badge types, we conducted a chi-square test on the collected cases and confirmed that there was a significant difference for each of the eight badge types (Pearson chi-square 1117.7, P<.001). CONCLUSIONS: Through its literature review on previous studies, this study demonstrated the badge's educational effectiveness. The badge design framework suggested in our study is expected to resolve some of the difficulties experienced during the badge design process in a gamified learning environment, encourage efficient badge design, and maximize learning effect.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.972

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.341
Teacher spread0.309 · 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 designNot applicable
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

Citations25
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Serious GamesSame topicEducational Games and GamificationFrench-language works237,207