Primary School Pupils’ Attitudes toward Learning Programming through Visual Interactive Environments
Bibliographic record
Abstract
New generations are using and playing with mobile and computer applications extensively. These applications arethe outcomes of programming work that involves skills, such as computational and algorithmic thinking. Learningprogramming is not easy for students children. In recent years, academic institutions like theMassachusetts Institute of Technology (MIT) and hi-tech companies, such as Google and Khan Academy, haveintroduced online environments to facilitate the teaching and learning of programming. Most of these programmingenvironments are web-based, and interactive and are supported with visual multimedia features. Therefore, they havebecome easy to use, very attractive and helpful for teaching children how to program and to develop theiralgorithmic and computational thinking skills. The proposed presentation will describe research that examined theteaching of a course to primary school children based on three on-line interactive environments: "Plastelina" for logicgames, “Code with Anna and Elsa” via the Hour of Code project block-oriented programming environment, forblock programming and "Turtle Academy" for textual programming in the Logo language. The current researchincluded the development, implementation and evaluation of the course at an elementary school. In addition, it wasaimed at investigating the pupils' attitudes toward the learning of computer programming, both before and afterparticipation in the course. The results revealed that the pupils' attitudes towards programming remained positivealso also after the participation in the course. It was also found that programming improved children's problemsolving skills.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".