The Impact of Instructing Quadratic Functions with the Use of Geogebra Software on Students’ Achievement and Level of Reaching Acquisitions
Bibliographic record
Abstract
The objective of this study is to examine the impact of instructing quadratic functions with the use of GeoGebra software and guided discovery worksheets on students’ achievement and level of reaching acquisitions. This study utilized a pretest-posttest control group experimental design. It was conducted in the 2017-2018 academic year with 62 tenth grade students of a high school in the center of Balikesir, Turkey. The study data was collected using quadratic functions achievement test. The quantitative data collected in the study were analyzed using independent samples t-test. The findings of the study indicated that the instruction exercised with the use of GeoGebra software and guided discovery worksheets was effective in increasing achievement and the level of the capability to reach acquisitions compared to the control group; the findings also indicated that the instruction created a significant difference. This study also found that the students in the control group students focused on algebraic operations and formulas, mainly memorized the formulas, and were unable to build the relation between graphics and the algebraic expression of functions. On the other hand, the students in the experimental group were better in building the relation between algebraic expressions and graphics, and more successful in interpreting graphics.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".