Recommendations to Solve the Problem of a Lack of Computer Skills Among CLB 4 and 5 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 Instruction for Newcomers to Canada (LINC) Center. The problem was that CLB 4 and 5 learners do not have basic computer skills. When the school switched to online mode during the pandemic, most teachers were not sure how to teach and assess learners using technology. Around 75% of learners asked the coordinator to withdraw from the program as they felt they did not get the same teaching quality as the traditional method. The rationale for this study was that learning with technology may enhance the learners’ academic achievements and equip them with all the necessary skills needed in the workplace so the community would have well-trained immigrants who attract more businesses, and employers would consider the graduates of this school for employment. Consequently, the provincial government would notice a decrease in social assistance applications, and schools would get more funds. The school may also earn higher online rankings and reviews. 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?” Three forms of data were collected. The first data collection method was interviews with teachers and administrators at LINC in Mississauga, Ontario. The second form of data collection was a focus group with teachers, and the third was a survey administered to all instructors. Recommendations to solve the problem included creating professional learning communities (PLCs) and providing blended professional development to teachers.
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 | 0.019 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".