University Professors' Perceptions on Blogging as Course Assignments in Southwestern Ontario: A Multiliteracies Framework
Bibliographic record
Abstract
In light of contemporary trends and practices, namely the impact of globalization and the integration of new technologies being promoted in the field of applied linguistics (Byrd Clark, 2012; Kern, 2006; Malinowski & Kramsch, 2014), this study explores the perceptions of professors, who teach graduate courses in Education on the impact and adaptation of new technologies on their pedagogy; namely the use of blogging as an educational activity. This study analyzes the traditional or innovative pedagogical practice of today’s graduate professors. This qualitative case study is based on a multiliteracies theoretical framework and incorporate the following methods: a survey and semi-structured interviews. This study seeks to make an important contribution to both the field of research and to practice with its emphasis on the integration of new technologies in graduate language and literacy education. Many graduate programs highlight the importance of originality, creativity and thoroughness (in other words, alternatives to traditional approaches) however there appear to be few multiliteracy options available in courses. Through the implementation of a survey (N=5) and semi-structured interviews (N=2), perceptions of graduate professors of blogging as an educational activity were investigated. Participants revealed the innovative pedagogical practice of today’s graduate professors. However, there are still various issues in practicality that need to be addressed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".