Developing IoT Activities Using the Problem-Solving Method: Proposal for Novice Engineering Students
Bibliographic record
Abstract
The inclusion of technological tools in the teaching-learning process in the university environment has not shown significant positive effects on the development of problemsolving skills.Therefore, it is essential to incorporate key methods in education, particularly in engineering, to help students strengthen their skills from the early years of university.The Internet of Things (IoT) emerges as a promising technological alternative to enhance teaching and learning processes in classrooms, while also fostering problem-solving skills in new university students.This study aims to propose a problem-solving method for implementing IoT activities in the classroom, targeted at first-year university students.The method is structured into four phases: understanding the problem, preparation of the plan, implementation of the plan, and solution review.The current study utilized a quasi-experimental design of the pre-test and post-test type, with intentional non-probabilistic sampling that was proportional to the number of students in the population.The results show that, by combining this four-phase method with technological resources (such as block-based programming with mBlock, Arduino boards, BME680 sensors, the ESP12F WiFi module, and MQTT client applications), out of the 73 students who participated in the IoT activities, 57, 49, 60, and 58 improved their skills in problem understanding, plan development, plan execution, and solution review, respectively.Additionally, it was observed that students developed the skill of plan execution to a greater extent, followed by solution review, problem understanding, and plan development.This teaching method aims to assist students who are starting their university studies in participating more effectively in IoT-related tasks and developing strong problem-solving skills.To meet these challenges, the teacher's role is crucial as they must supervise and provide continuous feedback.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".