ePEARL: Electronic Portfolio Encouraging Active Reflection Learning
Bibliographic record
Abstract
In Québec, like many other places, more than 20 percent of primary-school students have to repeat a grade before going on to secondary school and 70 percent of those drop out of high school (Statistics Canada, 2001). Currently, school is too often a place that disengages learners, which fails to encourage honest self-assessment, and where learning and evaluation are not meaningful acts of improvement but detached and punitive symbols of failure. Over the past several years, the Québec Ministère de l’Education du Loisir et du Sport (MELS) has been phasing in the Québec Education Program (QEP)-a complete reform of the curriculum favoring an integrated, comprehensive learner-centred approach to education based partly on a co-constructed, inquiry-based curriculum that responds to individual student needs and interests. The cross-curricular competencies, which have become central to the reform, are designed to ensure that the skills and knowledge being taught in our schools meet the changing demands of the 21st century workforce (Conference Board of Canada, 2001; MEQ, 2001). One way to meet this challenge appears to lie in the use of electronic portfolios which can be designed to support the process of students’ self-regulated learning. The value of portfolios for exhibiting evidence of learning has been well established and while the research and debate continue over the best vehicles or formats for portfolios, their use has become mandate in Canadian provinces such as Quebec as a means for capturing students’ metacognitive processes and evidence of learning. Social cognitive theorists like Bandura (1986) identify personal, behavioral and environmental factors as triadic processes which influence student performance. These processes underlie the self-regulatory processes which Zimmerman (2000) defines as forethought, performance or volitional control and self-reflection. The importance of developing self-regulating ability within students has been extensively researched for the past two decades and is believed to be essential to successful learning within schools and extending self-directed learning into adulthood (Boekaerts, 1999; Corno & Randi, 1999). Concordia University’s Center for the Study of Learning and Performance (CSLP) has identified the potential for portfolios to provide evidence of self-regulation as well as the potential for a an electronic portfolio tool to support and scaffold self-regulation (Wade, Abrami, & Sclater, 2005). As the research continues regarding the effects of portfolios in their various formats, the development of a tool which not only supports the development of a student’s portfolio but also of their self-regulative abilities provides opportunities for researching student outcomes in both arenas. This presentation will provide the theoretical background that guided the redesign of the CSLP’s bilingual, web-based electronic portfolio, now called ePEARL, along with some of the key features within the software.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.016 |
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".