Academic Journal

Integrating ESP32-Based IoT Architectures and Cloud Visualization to Foster Data Literacy in Early Engineering Education.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Integrating ESP32-Based IoT Architectures and Cloud Visualization to Foster Data Literacy in Early Engineering Education.
Συγγραφείς: Zambrano-Mieles, Jael, Tupac-Yupanqui, Miguel, Mari-Loardo, Salutar, Vidal-Silva, Cristian
Πηγή: Computers (2073-431X); Jan2026, Vol. 15 Issue 1, p51, 14p
Θεματικοί όροι: Engineering education, Microcontrollers, Environmental monitoring, User interfaces, Data visualization, Project method in teaching, Internet of things, Statistical literacy
Περίληψη: This study presents the design and implementation of a full-stack IoT ecosystem based on ESP32 microcontrollers and web-based visualization dashboards to support scientific reasoning in first-year engineering students. The proposed architecture integrates a four-layer model—perception, network, service, and application—enabling students to deploy real-time environmental monitoring systems for agriculture and beekeeping. Through a sixteen-week Project-Based Learning (PBL) intervention with 91 participants, we evaluated how this technological stack influences technical proficiency. Results indicate that the transition from local code execution to cloud-based telemetry increased perceived learning confidence from μ = 3.9 (Challenge phase) to μ = 4.6 (Reflection phase) on a 5-point scale. Furthermore, 96% of students identified the visualization dashboards as essential Human–Computer Interfaces (HCI) for debugging, effectively bridging the gap between raw sensor data and evidence-based argumentation. These findings demonstrate that integrating open-source IoT architectures provides a scalable mechanism to cultivate data literacy in early engineering education. [ABSTRACT FROM AUTHOR]
Copyright of Computers (2073-431X) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
Περιγραφή
ISSN:2073431X
DOI:10.3390/computers15010051