Preservice Teachers’ Perceived Preparedness to Integrate Technology Into Teaching of Mathematics: A Mixed Method Study
Bibliographic record
Abstract
This study explored preservice teachers’ (PTs’) perceptions of their preparedness to effectively integrate technology into mathematics teaching and the pedagogical strategies that contributed to developing their competencies in this regard. Their perceived preparedness was examined in terms of their knowledge within the TPACK domains and self-efficacy beliefs. Using a concurrent mixed method design, data were collected from 59 PTs in their last semester of study at a Canadian university. Quantitative data were collected through an online survey via three widely used instruments, namely: the TPACK survey, the Computer Technology Integration Survey (CTIS), and the Synthesis of Qualitative Evidence (SQD) Scale. Qualitative data obtained from three open-ended survey questions and follow-up interviews with six participants provided broader insights about PTs’ experiences and activities regarding technology integration into mathematics teaching. The results of descriptive statistics and thematic analysis indicated that PTs perceived their knowledge and self-efficacy beliefs related to integrating technology into mathematics teaching at a moderate to a high level. Correlation analysis also indicated positive relationships between the seven subscales of the TPACK domains and the confidence scale. Participants shared that while their respective programs’ ICT for Teaching and Learning course played an important role in developing their knowledge in the TK and TPK domains, activities such as coding processes, math games, dynamic mathematics software, and graphic calculators were effective tools that encouraged them to use technology in their teaching of mathematics (TPCK). Experiential learning, including practicum experiences, role modeling strategy, and collaboration with peers were identified by participants as effective pedagogical strategies that developed their preparedness to integrate technology into their teaching of mathematics. Some recommendations of this study for teacher education programs include providing math-specific technology courses; incorporating appropriate instructional design that connects the content course to curriculum to promote PTs’ active engagement in meaningful technology-rich learning activities; and using all six pedagogical strategies presented in the SQD model to prepare future teachers to effectively use technology in mathematics teaching.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".