Using Technology in Education from the Pre-service Science and Mathematics Teachers’ Perspectives
Bibliographic record
Abstract
The purpose of this study is to find out pre-service teachers’ views about the use of technology in science and middle school mathematics courses. For this purpose, semi-structured interviews were conducted with 30 pre-service teachers studying in a university in Central Anatolia Region during the spring semester of 2017-2018 academic year. The obtained data were analyzed using content analysis by the researchers. According to the findings, pre-service teachers thought that computer, smart board, projector, laboratory equipment, notebook, and pencil are the main technologies that can be used in the classroom. The participants also thought that presentation and video processing programs make course interesting and enjoyable, visualize the topic, make learning easier, and enable the permanent learning. However, they also stated that preparing a presentation and video processing program may be time consuming and boring if it is used throughout the course; and the program may not be useful for every subject. The participants found blogs and web pages useful since they give opportunities to share course content, materials, and information, make announcements to make students prepared for the course. Almost all the participants believed that technological equipment of their classroom definitely affect their teaching. Namely, in an ill-equipped class, the students would have difficulty in learning, tend to memorize things; also their attitudes, interests, and motivations towards the course, and participation in the lesson would be affected; and their learning would not be permanent.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".