3 Ways Technology Supports Ell Instruction: By Extending the Learning beyond Class Time, Scaffolding Instruction, and Personalizing Education, Technology Is Helping English Language Learners Succeed
Bibliographic record
Abstract
[ILLUSTRATION OMITTED] THE WSLACO INDEPENDENT School District is located at the southern tip of Texas, only seven miles from the Mexican border, and 40 percent of its 18,000 students are English language learners. With so many Weslaco students beginning school without any exposure to English, Superintendent Ruben Alejandro knew he had to do something dramatic to move the needle on achievement. To meet the needs of these students, he has put together a comprehensive plan for ELL instruction--the centerpiece of which is an early literacy program that provides access to an online library of digital books for the entire Weslaco community. The initiative, called Zero to Three Weslaco Reads, is intended to develop early literacy skills among children before they even enter the school system--and to encourage students to continue reading as often as possible through elementary and middle We needed to do something to build English vocabulary and comprehension skills among our students as quickly as possible, Alejandro said. The challenges that Weslaco educators face aren't unique to that district. Nationwide, nearly four and a half million students participate in ELL programs, according to the United States Department of Education, and this number continues to rise. Here are three key ways that technology is improving instruction for English language learners in Weslaco and many other K-12 districts around the nation. Extending Learning Although basic conversational fluency typically occurs in one to two years, it often takes English language learners at least five years to catch up to native English speakers academically, said Jim Cummins, a University of Toronto professor and ELL expert. That's academic language is more complex and less accessible than conversational language, he said: It contains many low-frequency words that students find only in classrooms and in printed texts, rather than in conversation with their peers. Also, native English speakers continue to develop their proficiency with academic English as students who are learning English are trying to catch up. In essence, English language learners have to run faster, Cummins said, because they're trying to chase a moving target. Spending extra time developing English literacy skills during and outside school is essential to this process, said Cummins, who recommended that educators create more opportunities for what he calls engaged literacy--and technology can help by extending students' learning beyond the allotted class time. For instance, Weslaco's partnership with myON allows parents and students to download digital books from myON's library of more than 10,000 titles to any device for reading before, during or after About 70 percent of these titles are nonfiction, which helps students learn the vocabulary they'll need to support academic discourse. We saw this as a great opportunity to help students build their English literacy, Alejandro said. We wanted to expose them to the vocabulary they would need to be successful in school. In Nashville, TN, nearly 14,000 students (about 15 percent of the total student population) are English language learners who speak more than 130 different languages, said Kevin Stacy, ELL director for Metropolitan Nashville Public Schools. The district has outlined a $38 million plan to meet their instructional needs. As in Weslaco, the plan extends beyond students and out into the larger community--and technology plays a key role. Metro Nashville holds frequent community nights in which it hosts language and literacy programs for entire families, Stacy said. In addition, the district uses cloud-based software such as Imagine Learning and Achieve3000 to support English literacy instruction, so students can access the programs from home as well as …
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".