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Record W571461015

L'incidence de l'utilisation de l'ordinateur a des fins personnelles sur la motivation et l'engagement scolaire

2011· dissertation· fr· W571461015 on OpenAlexaffabout
Roch Chouinard, Normand Roy

Bibliographic record

Venuenot available
Typedissertation
Languagefr
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsContext (archaeology)PsychologyInformation and Communications TechnologySubject matterComputer literacySubject (documents)Mathematics educationPedagogyCurriculumComputer scienceLibrary scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

This thesis’ subject was to study the relationship between the personal uses of computers and motivational attitudes in science. Even though computer uses in educational context have been extensively studied in recent years, the bulk of the research was focused on describing the impact of computer activities and utilizations at school on achievement and motivation in different subject matter. However, little is known about the impact of personal uses of computer on school experience. Moreover, despite an increase of households with computers, not every child has the same opportunity to use them. These children do not develop their computer literacy outside school, and this phenomenon could have an effect on academic related tasks. Some studies have focused on the effect of computers (their presence and use) on academic performance. Although they are unanimous about the positive impact of computers, researchers remain cautious in their conclusions (Beltran et al., 2008; OCDE, 2006). They stress that a direct cause to effect relation is not easy to establish. Moreover, those studies mainly examined school achievement. We wanted to address the issue differently: to determine the relation between personal computer uses and motivation at school. For this purpose, motivation in sciences was chosen. This subject matter often requires skills associated with ICT and computers. To do so, 331 students from public high schools in the Montreal area, in the regular sciences classes in 7th and 8th grade were selected. They completed a questionnaire composed of 7 motivational scales and several ICT usages questions. To meet our objectives, we established ICT profiles, based on personal uses of computer. With those profiles, we examined differences within motivational characteristics (competence beliefs, anxiety, interest and achievement goals) and commitment for the sciences courses. Our results show that time spent on personal computer use does not necessarily have a negative impact on school. We found that students who frequently use computers in their spare time, with a variety of usage, have more positive motivational characteristics. However, by delving deeper in our results, we found that the type of uses could be linked to positive or negative inducing effects on the motivation to learn. Indeed, students who use computers mostly for communication and playing games have less motivation that student with more various usages. Moreover, communication uses seem to be the most problematic type of usage while uses with intellectual nature have a more positive effect. Our study found that having a computer at home is not a synonym of frequent usage by young people. Our results suggest that the key to motivational success is the use made of the time spent in front of the computer not only the time spent. By encouraging uses of intellectual nature, we increase chances of developing positive technological habits that will be essential in tomorrow’s society. Keywords: Motivation, information and communication technology (ICT), personal uses of computer

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.058
GPT teacher head0.327
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes2
Has abstractyes

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