New Epistemologies in a Digital Age: Ways of Knowing Beyond Text-Based Literacy in Young Adult Learners Within An Ontario College Context
Bibliographic record
Abstract
The purpose of this study was to examine how young adult learners acquire and construct knowledge in a digital age within a post-secondary context. The research questions were: 1) What digital devices and technologies/tools do young adult learners use for learning and why? 2) How is the digital age (particularly the widespread use of the Internet) impacting knowledge acquisition and construction in young adult learners? 3) How should post-secondary educational curricula and practice be designed or re-designed to support young adult learners? \nA mixed methods (convergent parallel design) study was conducted involving 63 student participants (18 to 24 years of age) attending a large urban community college in Ontario. \nAnalysis of the data revealed five key themes: 1) participants identified and used go-to digital devices and tools (i.e., digital devices and tools participants preferred and opted to use first); 2) participants used a multiplicity of media, modes, and literacies in their learning; 3) participants valued the affordances of old and new educational practices and thus felt to be in a state of transition; 4) participants’ use of multiple media, modes, and literacies led to positive sum outcomes (i.e., increased capacity for learning); and 5) the mix of media, modes, and literacies used by participants resulted in more natural learning (i.e., learning that was more in keeping with participants’ natural ways of engaging in and perceiving the world around them). Another key finding was that although there were commonalities among participants, there also existed a great deal of individual difference in terms of the ways participants preferred to learn in a digital age. \nInterpretation of the findings resulted in a re-conceptualization of meta-literacy. In the context of this study meta-literacy was defined as the set of literacy types that meet the following criteria: 1) use a dominant invention/technology; 2) are disseminated via a dominant medium; 3) are represented by a dominant mode; 4) appeal to a dominant sense (or senses); and 5) provide for the ability to consume and produce meaningful communicative artifacts via a dominant medium and mode. Orality, traditional literacy, and digital literacy were identified as literacy types meeting the above criteria. It is recommended that meta-literacy, in conjunction with other relevant frameworks, be considered in the design or re-design of college curricula.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".