V3I7P4

Integrating IoT-Based Monitoring and System-Oriented Learning in Electromedical Vocational Education: Development of the BIONEST Model

St. Fatimang ^1*, Gufran Darma Dirawan ², Syahrul ¹

Abstract

The digital transformation of medical technology requires electromedical vocational education to develop students’ competence in integrated sensing, embedded systems, network communication, data interpretation, and technical problem solving. This study aimed to develop and preliminarily evaluate BIONEST (Bio-Incubator Network Education System Trainer), an Internet of Things–based infant-incubator monitoring trainer integrated with a system-oriented laboratory-learning model. The study employed an educational design-and-development approach based on the ADDIE framework, comprising analysis, design, development, implementation, and evaluation. Qualitative data were collected through observations, instructor consultation, document analysis, expert feedback, and field notes, while quantitative data were obtained from expert-validation instruments, technical testing, student-response questionnaires, and a one-group pretest–posttest assessment. BIONEST integrated temperature, relative-humidity, and light-intensity sensors with an ESP32 microcontroller, local and remote monitoring interfaces, and threshold-based alarms. Technical testing produced mean absolute percentage errors of 0.52% for temperature, 1.72% for relative humidity, and 3.20% for light intensity. Content and media validation scores reached 90% and 86%, respectively. Student responses reached 88% in the small-group evaluation and 89% during field implementation, with real-time monitoring receiving the highest rating of 92%. Among 35 students, the mean learning score increased from 65 to 82, corresponding to a normalized gain of 0.49 in the moderate category. These findings indicate that BIONEST is technically adequate, pedagogically valid, and practical for supervised electromedical laboratory learning. However, because the study used a single-group design without a comparison group, the learning improvement should be interpreted as preliminary within-group evidence rather than a definitive causal effect.

Keywords:

electromedical vocational education; infant-incubator trainer; Internet of Things; system-oriented learning; biomedical instrumentation; laboratory learning