Incorporating Farming Feature into MEGA World for Improving Learning Motivation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although educational games have been proved to be useful to get students motivated in doing learning activities, one of the most attractive game feature – farming – has rarely taken into consideration while designing and assessing an educational game. In this research, we design and develop the farming feature, also known as player versus environment (PvE) subsystem, for an educational game platform MEGA World (Multiplayer Educational Game for All). We discuss the operation workflow that the PvE subsystem communicates with MEGA World main system and design correspondent mechanic and required modules to assist students’ learning. The subsystem has two game modes and the students can use the knowledge or skills they have learned in the course to fight with the monsters and earn the rewards. We expect this subsystem can improve students’ learning motivation and performance. In order to verify our expectation, we design a semester-long experiment that involves four groups of undergraduate students.
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.
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 | 0.000 | 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 it