Bibliographic record
Abstract
How does collaborative software help in the formation of a learning community?This study looks at the experiences of students in a first level Computer Science class as they use Manhattan Virtual Classroom (MVC).Although this case study began with the assumption that a learning community would form, it quickly became obvious that student participation in the MVC was a larger issue.The course chosen for this study was CSC-150 -Foundations of Computer Science, as taught in the Spring 2004 semester at a Midwestern university.Two traditional (faceto-face) course sections were given access to Manhattan Virtual Classroom for the purpose of discussions, comments, questions, and virtual office hours.Many students did not take advantage of this collaborative tool.Several reasons are considered, the reluctance of freshmen to participate (Goldberg, 1997;Carlson et al., 1996), professor teaching style, and student perceptions of their own contributions to the class.Several conclusions are drawn from this study how to increase student participation.These include better training in the use of the software, use of smaller groups within the Manhattan Virtual Classroom environment, clearly stated professor expectations, and a general adoption of this technology for other classes.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".