Designing a Collaborative Blog about Student Success
Bibliographic record
Abstract
The term “web log,” or “blog,” was first coined in 1997 by Jorn Barger (Blood). Blogs have been used in education as online journals, discussion platforms, course websites, and alternatives to mainstream media publications (EDUCAUSE, 2005). Two of the more common blogging platforms, Wordpress and Blogger , are relatively simple to use, requiring no knowledge of HTML to post entries. One of the many advantages of using blogs is that they can foster interaction among peers, thereby building community (EDUCAUSE, 2005; Richardson). For further explanation of how blogs work, Common Craft has created an easy-to-follow video entitled Blogs in Plain English. According to the EDUCAUSE Center for Applied Research’s 2010 Study of Undergraduate Students and Information Technology, which surveyed close to 37,000 college students in the United States and Canada, 36% of the students noted that they contributed to blogs on at least a monthly basis; 11.6% of the students were using blogs in a course they were taking at the time of the survey, 37.6% of whom were using blogs collaboratively as part of the course; 15% of the students read or contributed to blogs via an Internet-capable handheld device; and 37.3% of the students noted that they liked to learn through contributing to blogs, wikis, and websites. The primary author has used blogs in honors courses since 2005 to post online discussion questions, course announcements, and project photos as part of a course blog (see Johnson) as well as to prompt students’ personal reflections on their own individual blogs. The purpose of this article is to describe the most recent blogging project in an honors course—a collaborative student-success blog written for and by honors students.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".