The Athabasca University Library Digital Reading Room: an iPhoneprototype implementation
Bibliographic record
Abstract
Introduction The iPhone, with its ability to support various text and multimedia formats, provides a unique opportunity for libraries to open up access to their digital collections. At Athabasca University (AU) researchers have initiated a process for the implementation of the AU Digital Reading Room (DRR). The DRR was one of Canada's first digital libraries to open up access to library materials on mobile devices. The iPhone presently represents the state of the art in mobile computing. With its touch screen, novelty of design, broadband access, visual display and multimedia capabilities, it offers enhanced possibilities for facilitating learning. As a first step in the deployment of any iPhone app, even for testing purposes, AU joined in the iPhone Developer Program. The DRR is an online course reserve repository that provides service both to AU students (providing accessibility) and to the university and its various centres (providing protection). It comprises many digital reading files filled with course readings and other supplementary courserelated content. These are faculty-chosen learning resources, housed in a repository in various formats, including learning objects, e-books, ejournals, audio and video clips, websites and book chapters. The available resources have been organized by course and by lesson for the convenience of students and they provide learners with easy access to course materials via PCs and mobile devices. The DRR currently supports 251 online courses, with links to more than 24,000 online resources. Literature review Waycott and Kukulska-Hulme (2003) focused exclusively on students’ experiences with reading course materials and taking notes using mobile devices. They found that students were able to make notes only with great difficulty. However, this early investigation was conducted using a relatively affordable first-generation mobile device with limited capabilities. According to Clyde (2004), the challenge was ‘to identify the forms of education and training for which Mlearning is particularly appropriate, the potential students who most need it and the best strategies for delivering mobile education’ (46). Lippincott (2008), Cheeseman and Jackson (2009) and Ally et al. (2008; 2009) have all focused on delivering m-library services to the next generation of students, recognizing the social changes being wrought by the ubiquity of mobile devices. Researchers have already begun investigations into the use of iPods (Coombs, 2009), and now of iPhones, in providing library services (Sierra and Wust, 2009).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.032 |
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".