Recommendations for Addressing the Deficiency in Computer Skills Among Intermediate Language Learners at the LINC Center
Bibliographic record
Abstract
The purpose of this study was to provide recommendations to solve the problem of a lack of computer skills among Canadian Language Benchmark (CLB) 4 and 5 learners at the Language Center for Newcomers to Canada (LINC) Center. The problem was learners did not have basic computer skills, so during the pandemic, teachers struggled to teach and assess learners using technology. Therefore, most learners chose to withdraw from the program, citing a perceived decline in teaching quality compared to traditional methods. This study aimed to explore the potential benefits of technology-based learning on academic achievement and workplace skills. The community would have well-trained immigrants, and employers would consider the graduates of this school. Consequently, the provincial government would notice a decrease in social assistance applications, and schools would get more funds. For this reason, the central research question was, “How can the problem of a lack of computer skills among CLB 4 and 5 learners be solved at the LINC Center?” Data were collected in three forms, namely interviews with teachers and administrators, a focus group with teachers, and a survey administered to all instructors. Recommendations to solve the problem included creating professional learning communities (PLCs) and providing blended professional development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".