ERIC ED466494: The Learning Equation (TLE) Final Report.
Bibliographic record
Abstract
The Learning Equation (TLE) is a software product that provides mathematics programming to students. It was introduced to grade 9 students in Alberta in 1997-1998, and in 1998-1999, its use was extended to grades 7, 8, and 10. TLE is designed to follow the Alberta mathematics curriculum closely. It can be implemented in several ways, and in some schools is the main delivery method, entirely replacing textbooks. In other schools, it is a supplement or a remedial resource. This study examined the effects of TLE on student achievement and attitudes and on teachers' effective delivery of mathematics. Interviews were conducted with 1,186 students, and data, including achievement findings, were available from several sources. Overall, by grade 9, students who used TLE slightly outperformed those who did not. TLE did not appear to make students feel more positive about mathematics, but in some cases, it reduced negative feelings. Teachers reported positive attitudes about TLE, but wished for closer communication with the TLE development community. In general, study findings reflect positively on TLE and indicate that it is a viable alternative to teaching mathematics using a textbook. Three appendixes describe study methodology and present pretest and posttest student responses. (Contains 17 tables and 23 graphs.) (SLD)
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.279 | 0.014 |
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; both teacher heads agree on what is shown here.
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".