A Narrative Approach to Understanding the Development and Retention of Digital Skills Over Time in Former Middle School Students, a Decade After Having Used One-to-One Laptop Computers
Bibliographic record
Abstract
Although there is a common result in recent research and practice that digital skills are important for every educated citizen, little is known of how these skills are evolving over a long period of time. Our study looks into the development and use of digital skills in former middle school students who had an individual access to the laptops a decade ago and are now entering the workforce. Using a qualitative approach, we look at results from an earlier study on one-to-one laptop use in middle school students, as well as results from follow-up interviews with three students from that cohort. More specifically, we present narrative stories offering a rich description of the personal experience of digital skill acquisition through information and communication technology (ICT)-assisted learning and the sustainability of these skills later in adulthood. Our findings indicate three specific types of digital skills, namely, technological resourcefulness, digital self-efficacy or empowerment, and open-mindedness toward technology, that emerge as key elements of the participants' perception of postsecondary academic and eventual further career success. The results of this study can be used to better understand how ICT skills are acquired and how they evolve over time in young people who grew up (or are growing up) in a digital world and how this process can be enhanced by educational institutions and workplaces. (Keywords: digital literacy, ICT, middle school, one-to-one laptop programs)
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".