"Lear.Ning Together". A case study examining the introduction of social collaborative learning supported by technology.
Bibliographic record
Abstract
There is strong evidence in the literature to support the argument that learning is a social and community activity and there are good arguments for the adoption of a social constructivist pedagogy to underpin learning activities. The author, teaching economics courses in an English as second language environment, was keen to move the delivery of courses away from a traditional knowledge transfer model to one in which students could socially construct their learning in a collaborative environment. To facilitate this a Ning community website was established, where students would maintain blogs and share and discuss resources and their learning. This case study looks at the results of this initiative over three cohorts of students, the lessons learned, the successes and failures and implications for further course design and development. In particular the case focuses on the student’s perception and attitudes to these changes and how they affect their learning. Introduction The Monetary Theory course at Dubai Men’s College (DMC) has been taught in a fairly traditional ‘knowledge transfer’ model since its inception. There is a strong argument in the literature that learning is a social activity and the author was keen to see if he could improve student engagement and thus encourage deeper levels of learning and enhance student success by using a blended learning model that included a much wider use of educational technology. The aim was to try and create a ‘community of learning’ among the students, with a particular focus on the sharing and discussion of resources using new social bookmarking technologies. The first cohort of students to use these new technologies studied the course in the second semester of academic year 2008/2009. The initial response to this, which focused on the use of the Diigo bookmarking tool, was reported in a paper presented under the ‘Best Practice” stream in October at E-Learn 2009 in Vancouver. Since then another cohort of students have taken the course and we have made some changes to the tools being used, thus this paper seeks to build on the earlier paper by including more details of student perceptions and reactions and include a larger sample of learners. The research is based on an action research model and results are gathered from an anonymous online survey completed by the students at the end of the course. Context The Monetary Theory course at DMC is taught in the final year of the Bachelors of Applied Science (BAS) in Business program. Students have normally taken four or five years to reach this stage of their studies and have previously taken a course in Micro and Macro Economics as well as a wide range of business courses. DMC is a part of the Higher Colleges of Technology (HCT), the federal vocational higher educational system for the United Arab Emirates (UAE). There are sixteen colleges spread across the cities of the UAE in the system, divided into male and female campuses. The students in this study are all male business students working towards a Bachelor of Applied Science in Business. Although chronologically mature, few DMC students display the characteristics of adult learners as described by Knowles’ Theory of Andragogy in (Moore and Kearley, 1996). Experience of working in this context for almost fifteen years suggests that in many ways they are still very much like child learners. Students expect teachers to make all the key decisions in relation to learning and tend to have a rather polarized view of the world where questions have answers that are either right or wrong.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".