Facing-Off in the Sport Management Classroom: Using Facebook as an educational tool
Bibliographic record
Abstract
Facebook has become firmly integrated into our communications infrastructure, and its hold only appears to be gaining in strength. Researchers in education are examining the implications of social media in the classroom setting. Perez (2009) found that students were logging into Facebook five days a week, upwards of four times per day. EDUCAUSE (2011) reported that 90 percent of undergraduate students have adopted Facebook and 58 percent have incorporated Facebook consumption into their daily routines. Over one quarter of the students surveyed reported spending six to 10 hours on social networking services each week; on the high end of the scale, a staggering eight hours of Facebook consumption per day was reported (Perez, 2009). These statistics coincide with reports that suggest course management systems and the use of e-mail are losing popularity among students (Joosten, 2009). Schroeder and Greenbowe (2009) found that the number of student posts were almost 400 percent greater on Facebook when compared to the popular course management software, WebCT. This same study rated Facebook postings to be superior in quality to those on WebCT; it further found that discussions were often continued throughout the entire semester, whereas those in WebCT tended to end more abruptly.\nThis paper addresses how educators might take advantage of Facebook as an educational tool. The following workshop outline will discuss strategies for implementing Facebook into a course and provide insight into the educational benefits inherent in this technology, while taking care to address potential challenges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".