We are not numbers: The use of identification codes in online learning
Bibliographic record
Abstract
This paper discusses students’ experiences with the use of identification codes in a graduate course delivered asynchronously via the Internet. While teaching an introductory masters level graduate course in distance learning, the authors discovered that the learning management system, Moodle, was programmed to display identification codes rather than student names when in the Student View mode. Consequently, when students participated in Computer-Mediated Communication (CMC) text discussions, their posts were attributed to their computer-generated IDs. Investigation into the identification protocol revealed that the institution had adopted a policy of using identification codes to comply with Alberta’s Freedom of Information and Protection of Privacy (FOIP) Act. We wondered what it meant to graduate students to be identified by a computer generated code rather than by name. In the context of an asynchronous CMC discussion forum, we asked how the use of an identification code affected students’ sense of identity within the online learning environment. Analysis of their responses revealed categories relating to personal identity (depersonalization and anonymity), social identity (community, learning, and engagement), and questions concerning suitable names for identification purposes. Most learners felt strongly that they should not be known through a numeric code and that their name was more personable.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".