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 \ngrade before going on to secondary school and 70 percent of those drop out of high school (Statistics \nCanada, 2001). Currently, school is too often a place that disengages learners, which fails to encourage \nhonest self-assessment, and where learning and evaluation are not meaningful acts of improvement but \ndetached and punitive symbols of failure. Over the past several years, the Québec Ministère de \nl’Education du Loisir et du Sport (MELS) has been phasing in the Québec Education Program (QEP)-a \ncomplete reform of the curriculum favoring an integrated, comprehensive learner-centred approach to \neducation based partly on a co-constructed, inquiry-based curriculum that responds to individual student \nneeds and interests. The cross-curricular competencies, which have become central to the reform, are \ndesigned to ensure that the skills and knowledge being taught in our schools meet the changing demands \nof the 21st \ncentury workforce (Conference Board of Canada, 2001; MEQ, 2001). One way to meet this \nchallenge appears to lie in the use of electronic portfolios which can be designed to support the process of \nstudents’ self-regulated learning. \nThe value of portfolios for exhibiting evidence of learning has been well established and while the \nresearch and debate continue over the best vehicles or formats for portfolios, their use has become \nmandate in Canadian provinces such as Quebec as a means for capturing students’ metacognitive \nprocesses and evidence of learning. Social cognitive theorists like Bandura (1986) identify personal, \nbehavioral and environmental factors as triadic processes which influence student performance. These \nprocesses underlie the self-regulatory processes which Zimmerman (2000) defines as forethought, \nperformance or volitional control and self-reflection. The importance of developing self-regulating ability \nwithin students has been extensively researched for the past two decades and is believed to be essential to \nsuccessful learning within schools and extending self-directed learning into adulthood (Boekaerts, 1999; \nCorno & Randi, 1999). \nConcordia University’s Center for the Study of Learning and Performance (CSLP) has identified the \npotential for portfolios to provide evidence of self-regulation as well as the potential for a an electronic \nportfolio tool to support and scaffold self-regulation (Wade, Abrami, & Sclater, 2005). As the research \ncontinues regarding the effects of portfolios in their various formats, the development of a tool which not \nonly supports the development of a student’s portfolio but also of their self-regulative abilities provides \nopportunities for researching student outcomes in both arenas. This presentation will provide the \ntheoretical background that guided the redesign of the CSLP’s bilingual, web-based electronic portfolio, \nnow called ePEARL, along with some of the key features within the software.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".