Computer programming and kindergarten children in two learning environments
Bibliographic record
Abstract
This study examined the appropriateness of introducing computer programming to kindergarten children. Three issues were explored in the research: 1. the programming capabilities of kindergarten children using a single keystroke program 2. suitable teaching techniques and learning environments for introducing programming 3. the benefits of programming at the kindergarten level. The subjects for the study were 40 kindergarten students from a surburban community in British Columbia, Canada. All students used the single keystroke program, DELTA DRAWING. Two teaching techniques were used—a structured method and a guided discovery method. Quantitative data were collected by administering five skills tests (skills relating to programming) as pretests and postests to both groups. A programming posttest was also given. Qualitative data were obtained by recording detailed observation reports for each of the 22 lessons (11 for each group), conducting an interview with each child at the end of the study and distributing a parent questionnaire. It can be concluded that it is appropriate to introduce computer programming to kindergarten students. The children in this study showed they are capable of programming. All students mastered some programming commands to instruct the "turtle" to move on the screen. DELTA DRAWING was determined to be a suitable means to introduce programming to kindergarten children. A combination of a structured teaching method and a guided discovery method is recommended for introducing a single keystroke program. It was observed that students in a guided discovery learning environment are more enthusiastic and motivated than students in a structured environment. Students need time to explore and make discoveries, but some structure is necessary to teach specific commands and procedures which may otherwise not be discovered. Social interaction should be encouraged while children use the computer, however most kindergarten children prefer to work on their own computer. There was no significant difference between the two groups on all but one of the five skills tests for both the pretests and the posttests. On the Programming Test the two groups did not perform significantly different. It can also be concluded that learning to program promotes cognitive development in certain areas. On all but one of the five skills test both the Structured Group and the Guided Discovery Group scored significantly better on the posttest than on the pretest. Lesson observation reports, student interviews and responses on parent questionnaires suggested that the computer experience was positive and rewarding for the kindergarten students.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".