Effects of Infographics on Students Achievement and Attitude towards Geography Lessons
Bibliographic record
Abstract
Geography is a very comprehensive field of study with many subjects to study topics. Using a wide range of materials in the teaching of this course can this lesson be made effective and permanent because we do not have chances to observe natural phenomena. Therefore, in geography education materials natural environment is to be brought to class by using materials. One of these materials is infographics. Within the scope of this study, the effects of use of infographics on students’ achievement and attitude in geography course. The study is important as it can contribute to design of new instructional materials to be used in classes. The study was designed as quasi-experimental study, which is one of the quantitative study methods. In the study, “Solomon Four Group Design” has been used. As a result, it can be said that using infographics in geography lessons increase academic achievement and attitude levels of the students. It can also contribute to visual and verbal learning levels. Besides, these results can also provide guidance to teachers as they provide alternative and different instructional materials in geography lessons. Infographics can be effectively and widely used in geography lessons in different grade levels and learning areas when visual and information are to be given together. It is also suggested to use infographics in cases where achievement and attitudes of students in geography lesson is low.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".