Mobile Design: Interactive Multimedia to Support Learning and Teaching" and "Mobile Library: Connecting New Generations of Learners to the Library in the Mobile Age"
Bibliographic record
Abstract
I attended m-library 2009 conference on Jun 23-24, 2009 in Vancouver. I participated in two pre-conference workshops and made presentations as well. I made another presentation in session 13 of the conference with my colleagues. \n \nIn the pre-conference workshops, I presented “Mobile-friendly Knowledge Management System” (MKMS) which I developed for the conference. The workshop “Mobile Design: Interactive Multimedia to Support Learning and Teaching” includes a hands-on session to allow audiences to practice MKMS on desktop PCs and mobile phones. I received positive response from the audiences, such as the system is mobile-friendly, easy to use, and flexible, etc. They also suggested adding some functionality to the system, e.g. improving user input interface and adding authentication module. \n \nIn the session 13 held on Jun 24, I, along with Colin E and Guangbing Yang, presented “Create Mobile Content Using a Mobile-friendly Knowledge Management System: The Athabasca University Experience”. I gave more detailed information on MKMS. \n \nI think my presentation was pretty successful since most of audiences expressed interest in MKMS and they gave us comments on it as well. \n \nIn the future, I will enhance the MKMS by adding functionality as follows: \n•\tAuthentication and authorization modules \n•\tQuiz module (at least it can handle multiple-choice questions) \n•\tSearchable \n•\tOAI-compatible \n \nIn general, people have interest in moving educational resources onto mobile devices. I will carry out research on mobile devices and find out how to make existing system available for mobile devices.
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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".