The Effect of Lego Wedo 2.0 Education on Academic Achievement and Attitudes and Computational Thinking Skills of Learners toward Science
Bibliographic record
Abstract
Today, the essential skills and characteristics of individuals change within the frame of changing needs. The acquisition of these skills to individuals is not sufficient with traditional education and difficulties are experienced in adapting to the age. While gaining 21st century skills, conducting interdisciplinary studies is becoming importance and increasing the efficiency. In this study, the effect of LEGO WeDo 2.0 robotics education on academic achievement, attitude and computational thinking skills of the learners toward science was examined. The study was conducted with 5 th grade students (N=36) in a private school in Elazığ in the 2017-2018 school year. The study model was the "pretest-posttest control group design" of the experimental method. As data collection tool, “Science Course Academic Achievement Test”, “Science Course Attitude Scale”, and “Computational Thinking Scale” were used in the study. While the activities in the experimental group were carried out with LEGO WeDo 2.0 Robotic Education Set, the same activities in the control group were implemented using the traditional direct instruction technique as in the curriculum. The application was continued for eleven weeks and the obtained quantitative data were evaluated at the significance level of 0.05 with SPSS packaged software. It was seen as a result of the study that attitudes, academic achievements and computational thinking skills of the experimental group students, who received robotic-assisted science education, toward science course differed significantly compared to the students in the control group.
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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".