Empowering Students Through Choice: The Impact of a Gamified Course on Well-Being and Achievement
Bibliographic record
Abstract
Our paper explores the impact of the ENTI 217: Design Your Life course, an innovative and gamified pedagogical approach offered at the Haskayne School of Business in Calgary, Alberta, Canada. Rooted in Friedrich Nietzsche’s metaphor of personal transformation and Mezirow’s Transformational Learning Theory, the course guides students through a journey of critical self-reflection and the exploration of their values, definition of success, and well-being. Drawing on Nietzsche’s stages of transformation—the camel, lion, and child—the course encourages students to challenge societal expectations and develop new, more authentic perspectives. Based on the work of the The Canadian Council of Learning, the paper also integrates the First Nations Holistic Lifelong Learning Model, which further emphasizes interconnectedness and personal growth. Through gamified assessments, students earn points by completing assignments, fostering agency, reducing stress, and promoting personal growth, rather than adhering to traditional grading systems and standardized testing. This study, based on pre- and post-course surveys and qualitative interviews, demonstrates significant improvements in student engagement, leadership, emotional intelligence, and overall well-being. The results highlight the effectiveness of gamification and transformational learning in promoting intrinsic motivation, supporting student-centered learning, and challenging the dominant achievement culture in higher education. The paper underscores the need for future research to deepen the integration of Indigenous pedagogies and to reimagine assessment models that prioritize mental health and holistic development. The findings make a compelling case for transforming educational systems to foster more inclusive, reflective, and resilient learning environments that support both personal and academic growth.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".