The Effects of Integrating Technology on Students’ Conceptual and Procedural Understandings in Integral Calculus
Bibliographic record
Abstract
This paper discusses the effects of using two different learning approaches to students’ understanding ofintegral calculus. Experimental and control groups were formed at random to participate in this research. Each group was divided into three sub groups which are low ability, medium ability and high ability groups. The formation of these subgroups was done according to their marks in an integral calculus pre-test given to them prior to the lessons. In general, students in the experimental group outperformed their peers in the control group in terms of their grasp of both conceptual and procedural understandings of integral calculus. By using mathematical software in learning integral calculus, the medium ability and the high ability students in the experimental group progressed better than the low ability students. On the contrary, in the control group, the maximum percentages of improvement in both conceptual and procedural understandings were from the low ability group. Since the main objective of integrating technology in the learning of integral calculus is to enhance every student’s understanding, a better implementation strategy needs to be drafted in the future. One possible way is to expand the usage of the technology in other calculus topics.
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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 | 0.002 | 0.002 |
| 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 | 0.004 |
| 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".