Regaining focus: Promoting attentional literacy in digital higher education
Bibliographic record
Abstract
The attentional demands placed on digital learners have grown, with online and blended education increasingly impacted by hyperconnectivity and digital disarray. In this context, it is essential for educators to help learners develop attentional literacy (AL). Building on past research on AL which fused insights from the fields of digital literacies and contemplative pedagogy (CP), this Delphi study examined the concept of AL and associated practices in higher education. Starting with a working definition of AL, expert feedback was invited from a global panel of experienced CP practitioners across academic disciplines. Through three Delphi rounds, the AL definition was validated and refined, before an abbreviated version was produced to facilitate operationalisation by digital educators who may not have a CP background. The study further explored how AL can be integrated into online higher education curricula, identifying strategies for educators and students to develop AL practices and address barriers to these practices. Despite systemic and structural constraints, cultivating AL allows educators and students to exercise a greater degree of individual and collective agency over their own attention in a digital world. Implications for practice or policy: Students can be guided in developing AL through a series of stages involving awareness and noticing, focus and intentional choice, openness and curiosity, and consideration of the wider attentional ecosystem. Educators should develop their own AL first, approaching it holistically by integrating personal, pedagogical and professional development-related practices, along with complementary offline activities. Institutions can maximise scope for AL development by increasing technological support and, especially, reducing curricular time pressure on educators and students.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".