Using Technology in Education from the Pre-service Science and Mathematics Teachers’ Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it