Learning through weblogs: students' perspective and learning evidences
Bibliographic record
Abstract
The study reported in this paper examines students' perception of the use of weblogs as learning tools; it also explores evidence of learning within blog postings. Two Ryerson University courses in Information Technology Management that require students to use weblogs are taken as examples. Twenty-two students from these two courses participated in an online survey concerning their blogging experience. The participants had very good computer knowledge - most of them had previous experience using blogs. Most of them thought that building and maintaining a blog was an easy task. However, the research shows that students' perception concerning the use of blogs as educational tools was neutral-students also perceived the impact of using blogs on their ability to learn the course material as neutral. The study shows a lack of clear communication between instructors and students, which could have had a negative impact on students' learning experience. Furthermore, the study indicates that most students perceived the content they posted in a somewhat negative way. A content analysis performed on 22 blogs demonstrates that that the objectives of each course played a significant impact on the evidences of learning apparent in students' blogs. Students in group B demonstrated more evidences of learning then students in group A. Overall, the study shows that the use of blogs as learning tool in university classrooms is promising. Providing students with clear goals, objectives and expectations could help them to build and maintain their blogs in a way that could be more beneficial to their learning experience.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".