How to Develop a Memory Game for Clinical Courses: A Leading Approach to the Interaction of Education
Bibliographic record
Abstract
: The integration of educational games has significantly transformed pedagogical approaches, emphasizing the importance of constantly updating educational programs. High-quality games motivate students and foster engagement, surpassing traditional methods. Addressing this need, this protocol study aimed to provide practical instruction on developing a memory game. Educational memory games have been found to enhance intrinsic motivation in learners and are particularly beneficial for clinical subjects. The design of memory games involves three components: Pedagogical level, design level, and modeling of learning content. Adjusting elements, such as the number and nature of items, time limits, feedback mechanisms, and task difficulty, can fine-tune the effectiveness of a memory game. The memory game consists of 2 main components: Learning and scoring, and it is designed using Articulate Storyline software. The learning phase focuses on mastering correct methods; however, the scoring phase evaluates learning retention and comprehension. Memory games can be adapted to different formats supported by educational websites and learning management systems. They are an effective tool for enhancing cognitive development and memory retention; however, it is important to consider the audience’s age, learning goals, genre, and assessment approaches when determining the ideal content level. Memory games can be used to identify learning challenges, promote active student engagement and collaboration, and improve learning outcomes. Educators must continually evolve their methods to resonate with new generations of learners, and success in implementing memory games depends on considering lesson characteristics, student needs, and instructor expertise.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".