Bibliographic record
Abstract
Purpose This conceptual paper aims to stimulate discussion on the growing influence of digital technologies on the success of learners in tertiary institutions. Design/methodology/approach The paper is grounded in a synthesis of the professional literature showing how learners entering tertiary institutions have been influenced by their pervasive use of digital technologies. This evidence suggests that a more integrated use of libraries is needed, and more collaboration among librarians, educators and learners will be fundamental to responding to the changing learning landscape. Findings The findings indicate that today's “new learners” expect more control of their learning situations, prefer active learning and they engage in networked communities for their social and professional lives. Research also shows that, while these learners are committed users of a wide range of digital technologies, they require support in developing their information and critical literacies. The paper also identified the tendency for educators to limit their uses of digital technologies for course management and the addition of online resources to their teaching, while resisting any fundamental change in the structure and delivery of courses. Academic libraries are responding to these challenges by creating more social learning spaces, integrating services for instructors and students and becoming more active partners in the educational community. Research limitations/implications The limitations identified here include the widening gap between those living and learning with digital technologies and the majority of the world youth still struggling to gain full access. Originality/value The research is one of the few position papers to begin its argument with a description of today's digital learners and then to connect their learning needs with the many changes in libraries at the tertiary level. The paper attempts to conceptualize how libraries can take a leadership role in bridging the gap between the learning styles and needs of today's students and the challenges of building new learning landscapes in tertiary institutions.
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.023 |
| Scholarly communication | 0.034 | 0.014 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".