Developing Technological Capacity in EAL Learning Environments: The Teacher Candidate Experience
Bibliographic record
Abstract
Abstract: English as an Additional Language and technology are both areas that are underserved in teacher education programs. In an attempt to begin EAL and technology development sooner a volunteer group from the (Name) engaged in a program to create an educational technology experience in an EAL high school classroom. The opportunities for digital learning technologies to support teachers and learners are endless (Borko, Whitcomb and Liston, 2009). Teacher education programs continue to promote the integration of new technologies into training in Universities (Albion, 2008) and in the K- 12 school environment (Dawson, 2006) but developing high levels of competency in pre-service teachers has been difficult. Another issue is that students in Canadian teacher preparation programs are unlikely to have the opportunity to develop skills in EAL (Cummins, Mirza & Stille, 2012; Mistry & Sood, 2010). Also, students in English as an Additional Language need exposure to technologies to help support their academic success and integrate into new cultures. A key aspect of learning to be a successful teacher involves the students learning in the settings in which they will teach Smarkola (2007) This paper uses an action research approach to examine the experiences of pre-service teachers application of technology in an English as an Additional Language high school classroom.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".