Moving from Analogue to High Definition e-Tools to Support Empowering Social Learning Approaches
Bibliographic record
Abstract
Traditional educational and training settings have dictated that the act of learning is an activity that is motivated by learners, directed by a teacher expert and based on information transfer and data manipulation. In this scenario, it has been assumed that learners more or less acquire knowledge or develop sets of skills as a result of such activity. With this model in place, learning ends when the training activities cease and implies that repeated doses of similar training are required over time. Various computer technologies, as they have been generally integrated into educational settings, have taken on the role as tools to support such a model. In some cases they are used to replace the teacher in these contexts although not without serious implications for learners and their learning it has been argued. During the last three decades, a growing movement in educational research, based on the theoretical support of Leon Vygtosky and Mikhail Bakhtin, is advocating that the traditional conceptualization of the learning process is misconceived. From the perspective of this movement, learning is understood as a life-long, social act of constructing knowledge in a dialogic activity with others. Within this model, social interaction is the precursor to higher order thinking rather than the reverse. The challenging question emerging for many educators is how new technologies can support knowledge and skill building in social constructivist-based learning settings. And a corollary to this question arises: Depending on the particular technology chosen, what are the implications for learning and identity construction? In this paper, we describe the Language Learning Through Conferencing project (LLTC) in which an affordable video-based web conferencing technology and desktop computers were used to conduct language learning sessions via the Internet. The project description, project content, and the experiences that took place over a sustained period, as well as the potential future for this approach to distance learning in a variety of fields are presented. The aim of the Language Learning Through Conferencing project (LLTC) has been to exploit a particular Web 2.0 technology to connect language learners internationally between Canada and new democracies in Central and Eastern Europe and more recently in the public sector in Canada. More specifically, the project was a means to respond to learners who faced challenges in finding opportunities for language learning both in Europe and in Canada. Outcomes from ongoing qualitative and quantitative findings gathered by the respective authors are indicating that these dialogic opportunities are also having a powerful influence on learners’ professional, linguistic and personal identities as well as their views of technology and learning.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".