Teacher, Talk, and Technology: Exploring Teaching and Learning in Grade 4 Mathematics
Bibliographic record
Abstract
This doctoral dissertation sought to explore teachers’ experiences and challenges as they learn to use technology to support their students’ mathematics learning. The purpose of this study was to identify good, efficient, innovative teaching practices and strategies when technology is implemented and integrated in Grade 4 mathematics. The data for these cases were collected through the observation and interviews with two Grade 4 teachers. The diverse levels and quality of the communication, collaboration, cooperation, cross-curricular connections, and curriculum development emerged during their mathematics sessions with their students. The current research study revolves around these two Grade 4 teachers’ pedagogical practices, and conceptions of technology adoption and integration in their mathematics teaching practice, while evaluating how the technology was implemented and integrated into mathematics teaching and learning activities to enhance their students’ mathematics conceptual understanding. The qualitative study also explores the teachers’ experiences and perceptions concerning technology as a tool that mediates learning and discourse in their Mathematics instructional sessions. Teachers, their students, and their peers are continuously negotiating and navigating the pedagogical spaces that promote and enhance mathematics learning and interactions across cross curricular subject content, communication, and collaboration. The following are key factors, based on the findings of this study, that can contribute to the effective implementation of technology to enhance mathematical discourse: A willingness of teachers to improve their practice in the area of technology integration, the technology-related learning tasks need to be developmentally appropriate, and teachers need to be able to successfully identify, acknowledge, and respect how each student learns with technology.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".