Gamification of learning in an introductory cell biology class
Bibliographic record
Abstract
Students taking introductory cell biology need to master a large amount of new vocabulary and course content before they can achieve a deeper understanding of fundamental concepts. Games are notoriously good at engaging their target audience and keeping players on task, and if carefully designed, games have the potential to be tremendous pedagogical tools. In order to facilitate learning for our students, we are developing an online game based on cognitive science, which is designed to encourage students to engage with the class material. A pilot trivia game ( https://www.biolingo.ca/ ) designed to test interest in such a game in our students was released in March 2017, with a bank of over 250 questions covering the class content (Bloom levels 1 and 2), which students were at liberty to use as a study tool. Students were able to choose between subject categories and a 15 questions quiz would be randomly generated from the question bank. Students were given access to the game 3 weeks before the final exam, playing time was not limited and quizzes were completed on a voluntary basis. On the final exam, students were asked to self‐report how much time they spent on the game and all 491 students answered. Students were grouped according to two categories: their use of the game (>3h were considered high users, 1–3h mid‐users, <1h low users and 0h are non users) and their exam result (A>80%, B = 70–79%, C= 60–69%, D= 50–59%, E= 40–49%, F<40%). The majority of students (67.3% of all A students, 54.2% of all B students, 54% of all C students, 58.2% of all D students, 56,8% of all E students and 33.3% of F students) accessed the game at least once. Interestingly, the proportion of mid‐users and high‐users was the greatest in F students (23.3% and 13.3% of all F students respectively) and comparatively low in A students (10% and 0.3% respectively). Clearly, students were curious about the online game and were eager to try it. It is also interesting to see that struggling students seemed to be eager to use a tools that could help them practice and improve their learning. Further studies using a modified version of the pilot game, designed to stimulate distributed learning and repeat testing throughout the term, as well as tying the students' game performance to their course mark, will help us research the potential for increased learning through the carefully designed gamification of content. Support or Funding Information Teaching and learning support services (TLSS) University of Ottawa and eCampusOntario This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.001 | 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".