Fostering Motivation, Learning, and Transfer in Multi-User Virtual Environments.” Paper presented at the American Education Research Association, Montreal. http://muve.gse.harvard.edu/muvees2003/documents/D ede_Games_Symposium_AERA_2005.pdf (last accessed
Bibliographic record
Abstract
Many researchers are currently exploring the rich types of learning that take place in online game environments, including the acquisition of some worthwhile skills (e.g., collaboration) (Gee, 2003; Steinkuehler, 2004). However, the content acquired typically is neither related to national standards for academic content nor useful if applied to real world contexts, and no studies have yet established the transfer of skills mastered in gaming to life situations. With NSF funding, we are designing and studying a multi-user virtual environment (MUVE) that uses digitized museum resources to enhance middle school students ’ motivation and learning of higher order scientific inquiry skills, as well as standards-based knowledge in biology and ecology. MUVEs enable multiple simultaneous participants to access virtual contexts, to interact with digital artifacts, to represent themselves through “avatars, ” to communicate with other participants and with computer-based agents, and to enact collaborative learning activities of various types. Developing effective educational curricula for students who struggle in school settings is challenging. These students are often characterized by high absentee rates, behavior problems, low interest in science, and low self-efficacy in science. We are
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 | 0.003 | 0.004 |
| 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 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".