Understanding Motivational Differences through the Lens of Gamification User Types
Bibliographic record
Abstract
In the context of an educational technology course, teacher candidates completed Marczewski’s User Types Hexad Test, a questionnaire based on a typology for classifying both intrinsic and extrinsic motivational tendencies. The test results showed teacher candidates' motivational tendencies, through indicating their resonance with six different User Types, including Socializers, Free Spirits, Achievers, Philanthropists, Players, and Disruptors. Knowing their User Type allowed teacher candidates to reflect on their own personal motivations to use various types of digital tools, as well as to consider how their peers and their students with different user profiles may be motivated differently than themselves. The main research questions in this study were related to discovering the motivational differences present in a group of teacher candidates, and whether knowing about these differences would empower them to acknowledge differences in motivation among their learners. The findings include teacher candidates’ understandings of the importance of differentiating for motivational tendencies through insight, relationships, and effective teaching, as well the different language teacher candidates use to describe their own developing teacher identity, according to their resonant User Types. These data provide examples of how User Type awareness can help teacher candidates take practical steps toward differentiating instructional design based not only on consideration of learners’ abilities and interests, but also on learners’ motivational profiles. Suggestions for language that would resonate with various User Types is presented, and may help teacher educators as they coach pre-service teachers through their identity development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".