The Effect of Online Assessments on Students’ Attitudes Towards Undergraduate-Level Geography Courses
Bibliographic record
Abstract
The improvements in technology made technology tools invade almost every field, including education. Online assessment tools have various functions for students and teachers. Students are able to use their mobile devices in the classroom, while teachers are able to use the tools for formative and summative evaluation purposes and for getting to know their students. Teachers also get feedback on their instructional practices by analyzing student responses. Previous studies found that undergraduate students in Faculty of Education in Turkey do not have a very positive attitude for geography courses, because most of the students take geography courses almost every year until they enter the university. The aim of this study is to determine the effect of in-class online assessment tools on the attitudes of students at undergraduate level towards geography courses. In addition, the changes in students’ attitudes are examined for various variables. The study was designed in a quasi-experimental model with experiment and control groups. The study also implemented pre-tests and post-tests. Geography Attitude Scale developed by Sözen (2019) was used as data collection tool. The study’s sample consists of 70 students whose majors are Primary School Teacher in Faculty of Education. An online assessment tool was implemented for seven weeks in experiment group, while the control group did not receive the said tool. The study found that using online assessment tools significantly improved students’ attitude towards geography course.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".