Using EdTech Data Analytics to Promote Personalized Learning and Close Educational Gaps in Ontario
Bibliographic record
Abstract
The goal of this thesis is to optimize student learning through EdTech by finding and customizing solutions to address differences in learning outcomes. The primary problem is that data analytics is not being used enough to inform teaching methods. Data on how institutions are utilizing EdTech data analytics to enhance student learning outcomes was gathered through a structured survey that included both descriptive and inferential statistics. According to my survey poll, a considerable portion of institutions do not use assessment data at all,even if the majority do use it to guide instruction. My finding is supported by recent research indicating that many higher education institutions struggle with effectively using assessment data (Levy-Feldman & Libman, 2022). Curriculum alignment, at-risk student identification, and professional development were among the areas with moderate use. However, there is little application in domains such as resource allocation and student engagement. The results of my survey indicate that there is a considerable adoption gap even if there may be advantages to applying EdTech data analytics in the classroom. These results are based on data collected from 203 participants through surveys. The data was analyzed using statistical analysis which provided insights into the adoption and application of EdTech data analytics. To completely enhance student learning outcomes, institutions must employ data-driven techniques more frequently. The survey results indicate that while 57.6% of institutions use EdTech data analytics to monitor student performance, a significant portion (25.1%) are unsure about its use, and 12.8% do not use it at all. Specific responses from the survey highlight barriers such as lack of training (45%), concerns about data privacy (30%), and insufficient technological infrastructure (25%). By addressing these barriers, institutions can more effectively leverage data-driven techniques to improve student learning outcomes
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".