A Comparative Study on the Effects of Project-Based Learning and Online Lessons on the Learning Achievement on the Internet of Things Among Thai Grade 9 Students
Bibliographic record
Abstract
This study aimed to compare the effects of project-based learning (PjBL) and online lessons on the learning achievement of Thai Grade 9 students in the topic of the Internet of Things (IoT), with conventional teaching included as a control group. The study utilized a quasi-experimental design, with three groups: PjBL, online lessons, and conventional teaching. The participants included 125 Grade 9 students from a secondary school in Thailand, divided into three groups: 42 in the PjBL group, 41 in the online lessons group, and 42 in the conventional teaching group. The instruments included a PjBL learning management plan, an online lesson, a learning achievement test, and a satisfaction questionnaire. Data were analyzed using descriptive statistics (means, standard deviations) and inferential statistics (ANOVA and Tamhane’s post-hoc test) to identify significant differences between the groups. The findings revealed that students in the online lessons group achieved significantly higher scores than those in the PjBL and conventional teaching groups. Additionally, the PjBL group outperformed the conventional teaching group. Moreover, the participants in both PjBL and Online lessons were satisfied with the instructional methods. The study provides evidence of the comparative effectiveness of online lessons and PjBL for teaching IoT, offering insights to enhance technology-integrated curricula and promote self-regulated learning in 21st-century education.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".