Attentional Literacy as a New Literacy: Helping Students Deal with Digital Disarray
Bibliographic record
Abstract
When students learn online, they do so within a wider context of digital disarray, marked by distraction, disorder and disconnection, which research shows to be far from conducive to effective learning. Specific educational issues include a lack of focus, linked to information overload in an environment characterized by misinformation and disinformation, as well as a lack of connection to the self and others. Arguing that today’s growing focus on digital literacies in education already serves as a partial response to digital disarray, this evidence-based position paper proposes the concept of attentional literacy as a macroliteracy which interweaves elements of now established literacies with the emerging educational discourse of mindfulness. Through attentional literacy, students may gain awareness of how to focus their attention intentionally on the self, the relationship with others, and the informational environment, resulting in a more considered approach to learning coupled with an appreciation of multiple shifting perspectives. Armed with this developing skillset, students stand to benefit more fully from digital educational experiences. Considerations for continuing research in this area include the need to adopt a critical stance on mindfulness, and the need to operationalize attentional literacy for the classroom.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".