E-learning Enhance the Roles of Teacher Librarians as Leadership in Collaborative Teaching and Learning
Bibliographic record
Abstract
Over the past 40 years, the evolution of the computer and Internet Communication Technology (ICT) has enhanced this increasingly information-driven world expanding rapidly. Once people starting to make use of Wi-Fi in communication, they can get information through their digital mobile device whenever and wherever they want. Confronting the rapid changes of ICT and the flooding of information, how to evaluate and select appropriate resources are not only the problems for the educators but also the necessary life-long learning skills for the learners. Moreover, compare with the state of the art learning resources, traditional mode of teaching and learning hardly provoke student’s interest in learning. Thus traditional mode of teaching and learning would hardly stand alone and remain unchanged. To implement e-learning, Government should not only have careful ICT planning and development. However, for the long term development, there should be sufficient funding for schools to acquire, update or upgrade all those necessary hardware and software. Besides that, for educational reforms, educators should be initiative and enthusiastic in understanding more about e-learning. Enhancing effectiveness in teaching and learning, they should think about how to incorporate targets of teaching with the resources and to make use of e-learning in provoking students’ interest and initiative in learning. Nevertheless, innovators encountered many obstacles and problems during the processes of adopting and implementing e-learning. Problems including : How to make use of mobile devices in teaching and learning? How to utilize mobile devices in facilitating learner's participation in learning activities? How to make learning and teaching more interactive? Despite all those problems, the critical one would be that most teachers were not well equipped with sufficient ICT skills. As a matter of fact, e- learning would be the trend and educators could not just keep watching without taking further action. To raise the effectiveness of learning, it is important that educators should be well-equipped themselves with the necessary competences in using mobiledevice and other related electronic applicants in teaching. With our practical experience presented in this paper, we hope to share how a teacher librarian act as a leader in implementing e-learning; to elaborate the strategies promoting collaborative teaching and learning in cross-curricula; to put the roles of “Information specialist” and “Teaching partner” in practice.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.018 |
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".