Click Click: Over 1,000 International College Students Detail Traditional Computer Usage
Bibliographic record
Abstract
ABSTRACT This article investigates the availability of computers and the tasks for which computers are used by undergraduate students enrolled in business classes at 12 selected universities in 7 countries. This research will expand the literature related to traditional computer usage in the countries represented. Students were surveyed to determine similarities and differences in the use of computer technology at the universities. Countries included in the survey are: Canada, Mexico, United States (US), Belgium, France, Finland, and Spain. While there were significant differences in responses at the .001 level among students at the 12 universities, most students owned their own computers (85.6% and above), and most (86.0% and above) had Internet access outside of their university. There are some differences in computer usage between the various countries. Overall, study results indicate that all students in all countries use the computer for a significant part of their education. The findings generally support previous research on computer use by university students. INTRODUCTION In a YouTube video, Social Media Revolution, Erik Qualman (2009a) reported how quickly technology is changing. Qualman (2009a) is already at work on an updated version of this video which was posted in August 2009. Indeed, social media has now overtaken pornography as the number one activity on the web (Huffington Post, cited by Qualman, 2009a). The following timeline was among the statistics he cited: it took radio 38 years to reach 50 million users; television took 13 years; the Internet took four years; and the iPod took three years (United Nations Cyberschoolbus Document, cited in Qualman, 2009a). Then technology began to move even faster: Facebook added 100 million users in less than nine months (Mashable, cited in Qualman, 2009a) and iPhone applications had been used one billion times in nine months (Apple, cited in Qualman, 2009a). At one time, using technology may have been regarded as a way for colleges to promote independent learning and cut costs by reducing the amount of direct teaching time by staff (Nash & Maxwell, 1994). Today, however, technology and education are synonymous. College professors and IT departments are hard-pressed to keep up with new trends in technology. Indeed, when the music-sharing site, Napster, appeared, students caused college networks to collapse with their intense swapping of music and video clips. Buffalo University had to limit the data packets available to residence halls during the day, when faculty and staff were still on campus (CNN.com, non-retrievable web site, accessed Sept. 22, 2006). A University of Florida student said, My computer is turned on all day long, and I'm connected to the Internet 24 hours a day.(S. Coopersmith, non-retrievable YoungMoney.com web site, accessed Sept 22, 2006.) Twitter became the word of the year in 2009 topping Obama, H1N1, Stimulus, and Vampire(Global Language Monitor, cited in Parr 2009). Indeed, computer/Internet usage changes daily and it would be difficult to project how college students might be using it in only a few months. The latest rage is Chatroulette, which debuted in December of 2009 with 300 users. If you should log on today you would likely find 50,000 users at any one time. Invented by a 17-year old Russian, Audrey Temovskiy, it is a web camera-based chat room. A user logs on and becomes instantly connected to another random person around the world via camera. One can chat, or one can next a person and go on to another stranger (Braiker, 2010). REVIEW OF LITERATURE Technology Use in Universities Statistics are equally staggering: 35% of male college Internet users in the U.S. spend 21 or more hours online per week (EDUCAUSE survey, cited in eMarketer newletter, nonretrievable, accessed Oct. 14, 2008). In another study for Break Media, 7 out of 10 of young men aged 1 8 to 34 said they could not live without the Internet, while only three out of ten said they could not live without television. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".