Making Learning Fun: Implementing a Gamified Approach to Materials Science and Engineering Education
Bibliographic record
Abstract
Abstract Materials science plays a critical role in educating future engineers, where knowledge of materials selection is essential for design and problem-solving. However, many programs rely on traditional lecture styles to convey this fundamental knowledge. While these teaching styles can be effective, they provide little opportunity to actively engage and expose learners to memorable experiential learning elements. The COVID-19 pandemic presented a new opportunity to focus on developing unique teaching tools to reach students on virtual platforms. Although the development of these tools was critical in today's technology-driven society, pandemic teaching and learning remained challenging, which likely contributed to the amplification of virtual gamified learning. In redesigning our first-year engineering curriculum within the Faculty of Engineering at McMaster University into the new Integrated Cornerstone Design Projects in Engineering (ENG 1P13) course, an opportunity to re-evaluate our teaching approach was presented, which allowed us to further explore ways to increase student engagement and learner creativity. This work focuses on the introduction of a gamified active-learning approach to teach materials science within the first-year curriculum. The purpose of this intervention was to enhance the learner experience to demystify the fundamentals by connecting theory to practice. Although pedagogical literature highlights the effectiveness of gamified learning strategies to enhance the learning experience, limited examples were found within the materials science and engineering fields. In this work, two types of materials science games along with other interactive lab components were successfully implemented in an adaptable fashion for in-person and virtual teaching styles for over 1200 learners. The first type is adapted based on popular board games in efforts to design relatable understandable games such that the students could focus on learning the new materials and not the game rules, "Materials Battleships", and "Materials Taboo", where gamified strategies are incorporated to introduce students to materials properties and materials selection. The second involves the design of custom virtual emulators that challenge learners to explore the mechanical and electrical behaviour of materials. The games challenged learners to explore various materials and science concepts in a fun way. Our survey responses from participating students were used to evaluate the approach; these findings highlight that gamification stimulated students' interest in material science and motivation to participate. While the majority of students surveyed found the new activities enjoyable, the results also indicate higher learning engagement and increased interest in materials science for upper-level stream selection choice after the open first year. The analysis of these surveys targets what factors were effective in increasing engagement as well as effectiveness in teaching content. The success of gamified learning for material science coupled with the targeted data for improvement and adaption creates a space for significant improvement in the material science curriculum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".