Assessing Students' Conceptual Knowledge of Computer Networks in Open Wonderland
Bibliographic record
Abstract
Computer Networks, an undergraduate computer science course, is taught through a variety of new technological tools but more attention on assessment strategies using those technological tools is desirable. Research studies have shown that assessment methods also influence students' learning. Current assessment techniques, even those which include technology, focus more on summative assessment rather than formative. Formative assessment is necessary for teaching and assessment of complex skills like troubleshooting or network design. Therefore, there exists a need for assessments to capture students' problem solving process and chosen approaches during the assessment. By tracking a student's behaviors in an immersive assessment environment, the path that the student takes towards the solution can be studied. Virtual Worlds are naturally immersive environments and can be effectively used to record interactions that students made in the worlds. We have developed NetWorld in Open Wonderland, an open source software for creating 3D virtual worlds. NetWorld is a 3D virtual world built for detailed assessment of computer network concepts for computer science undergraduate students. This detailed assessment happens at 3 levels -- conceptual understanding, diagnostic abilities and the ability to design a network. This paper describes the design principles and implementation of NetWorld and a plan to evaluate its usability.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".