Is social software really a “killer app” in the education of net generation students? Findings from a case study
Bibliographic record
Abstract
Purpose Social software is increasingly viewed as the new “killer application” in higher education – a potential answer to needs ranging from active learning and student engagement, to faculty empowerment. The purpose of this study is to explore this potential in the context of participating net generation students in a science and technology oriented, laptop‐based university located in Southern Ontario. The study is interested in the efficacy and pedagogical impact of social software (SSW) technologies in the students' learning experience. Design/methodology/approach The research model used an exploratory, descriptive, quantitative case study. The focus of the study was on the impacts of SSW on students' information literacy skills. A quasi‐experimental model was used to compare the effects of SSW use in information literacy instruction with those of traditional educational technologies such as learning management systems (LMS). Findings A total of 80 students participated. Twenty‐four students in the treatment group used SSW during the instruction phase, while in the control group, 56 used the LMS. The pre‐test showed a relatively moderate use of SSW technologies among the participants, with the exception of social networking technologies. At the completion of the study, students showed moderate willingness to employ SSW to enhance their learning. Barriers to the adoption of these technologies were highlighted. The study findings could not demonstrate that the use of SSW, compared with more established technologies such as the LMS would lead to different information literacy scores. Originality/value This is a summary of my original PhD research completed in 2009. A shorter poster version was presented at the 2011 IATUL Conference in June 2011 at Warsaw, Poland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".