Constructing Their Learning: A Case Study of the Implementation of Social Bookmarking to Improve Student Learning and Collaboration with a Cohort of Engineering Students Learning in a Second Language Environment.
Bibliographic record
Abstract
In a paper at Global Learn 2010 the author used web 2.0 social bookmarking tools (Diigo and Ning) as a way to transform a course from a knowledge transfer model to one in which students construct their understanding in a socially collaborative environment. This change is underpinned by the considerable evidence in the academic literature to support the argument that learning is a social activity. This paper examines the challenges and successes of broadening and extending that approach across a wider range of students from different disciplines. It will examine the extent to which students shared resources, discussed their learning and collaborated and whether they generated their own content. It will consider the difficulties faced and how, if possible, they were overcome, it will consider the extent to which it was possible to move learning beyond the formal constraints of the classroom to a more flexible, mobile and informal approach. Introduction The case for social, collaborative learning is well documented in literature that can be traced back to Vygotsky and his ideas on the “Zone of Proximal Development”. More recently the work of Brown Collins and Duguid (1989), Lave and Wenger (1991) and Wenger (1998) have made strong arguments for this position. However as Weller (2006) states: There is a good literature therefore about the benefits of a community (be it virtual or real) in the learning process, but being pedagogically sound is not, in itself, sufficient for them to be adopted on a large scale. The traditional approach to teaching, embodied in the face to face lecture, has a good deal of inertia and is supported by an existing framework which is realized through assessment and accreditation strategies, administration, financial structures, physical buildings, etc. (Weller, 2006, p.12) In an effort to overcome some of the barriers identified by Weller above, the author has been experimenting with the use of a web based social bookmarking tool to see if he could improve student engagement and thus encourage deeper levels of learning and enhance student success. The aim was to try to create a: ..‘community of learning’ among the students, with a particular focus on the sharing and discussion of resources using new social bookmarking technologies. (Curcher, 2010, p.1) The results of these initiatives were presented at E-Learn 2009 in Vancouver (Curcher, 2009) and further developed in a presentation at Global Learn 2010 in Penang (Curcher, 2010). One of the limitations of that action research was that it was very narrow in scope, looking at just a few cohorts of students taking the same course in a business program and it was not clear if the same results would be duplicated by students on a different program and taking a different course. The author was keen to extend the scope and so when, in September 2010, the opportunity to teach a course on the Engineering Management program presented itself, the author was enthusiastic to trial the use of social bookmarking with this very different cohort of students taking a different course on a different program.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".