English-learning expert to speak at next UND College of Education and Human Development AAA Lecture Series event, Monday, Oct. 20
Bibliographic record
Abstract
Virginia Pauline Rojas, an expert in the development of young English learners, will speak at the University of North Dakota on Monday, Oct. 20, as part of the Anyone, Anytime, Anywhere (AAA) Lecture Series presented by the UND College of Education and Human Development. The lecture, set for 3 p.m. in the Education Building's Room 5, is titled "Developing Growth-Mindsets for Responsive English Language Learning (ELL) Schooling." The mission of the AAA Lecture Series is to learn about providing access across the rural state of North Dakota, and across the country. The series lectures will be live streamed to other universities, colleges and schools in the state. About the Lecture: ELLs are the fastest growing educational subgroup in the United States compromising about 10 percent of the K-12 student population. Rojas will address questions such as; what challenges does this pose for K-12 schools; how is higher education being impacted as ELL students graduate from high school and increasingly enroll in college; how can educational institutions at all levels be responsive to this diverse and growing student population? About Rojas: Rojas conducts professional training on effective programs and strategies for English learners from pre-school through grade 12 for English as a second language (ESL) and classroom teachers alike. As an Association for Supervision and Curriculum Development (ASCD) faculty member, Rojas uses the Understanding by Design backwards design curriculum model to enable teachers to design high-challenge, high-support learning experiences from strengthening English learners' second language proficiency and academic achievement. She provides professional development trainings, job-embedded coaching and demonstration lessons, and conducts linguistic audits of ESL programs. She has worked in more than 200 international schools throughout the world as well as with numerous school districts in the US and Canada. Rojas is the author of Strategies for Success with English Language Learners: An Action Toolkit for Classroom and ESL Teachers, published by ASCD (2007) and of Educating English Language Learners: Connecting Language, Literacy, and Culture (2010), an ASCD DVD series. Rojas teaches graduate courses for the Association for the Advancement of International Education and is the ESL Coordinator for the World Language Initiative with International School Services.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one teacher head, 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".