DOCUMENTING STUDENT CONNECTIVITY AND USE OF DIGITAL ANNOTATION DEVICES IN VIRGINIA COMMONWEALTH UNIVERSITY CONNECTED COURSES: AN ASSESSMENT TOOLKIT FOR DIGITAL PEDAGOGIES IN HIGHER EDUCATION
Bibliographic record
Abstract
Virginia Commonwealth University (VCU) is implementing a large scale exploration of digital pedagogies, including connected learning and open education, in an effort to promote digital fluency and integrative thinking among students. The purpose of this study was to develop a classroom assessment toolkit for faculty who wish to document student connectivity in course-related blogging and microblogging (“tweeting”) activities. Student use of digital annotation devices, including hyperlinks, embedded images, mentions, and hashtags, were studied in four university courses as potential indicators of student connectivity, defined as the ability to connect current thoughts and experience with other concepts and people across space and time. One thousand one hundred and eighty six (1186) hyperlinks and embedded images, 2708 mentions, and 135 hashtags were collected from 498 learner blog posts and 5343 tweets through mostly automated, digital workflows and analyzed through a combination of statistical, content, and network analysis. General criteria for “connected course” design, a model for connectivity as a form of learning, connectivity-based learning goals, and integrated, potentially scalable assessment practices are discussed. Content analysis led to the development of classification systems for the types, sources, and communicative impact of hyperlinked and embedded materials in blogging and tweeting contexts. Network analysis was adapted to visualize, document, and describe course-related social interactions and student use of web-based information sources. Real student data are used to describe annotation-focused assessment criteria, analytic assessment dashboards, rubrics, and approaches to real-time graphic visualization of student performance.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".