Agung Wahyu Setiawan
There are several active learning approaches that can be used not only to increase the students' final mark but also students' engagement. In previous years, the active learning method has been implemented in Biomedical Measurement and Instrumentation (BMI) course using a Project-based Learning (PjBL) scheme. According to the students' comments, active learning needs to be improved in next year's implementation. Therefore, the instructor introduces new learning methods, i.e., hands-on, and industry-based projects in PjBL. Thus, the BMI course is implemented using a hybrid learning approach that combined the traditional learning approaches and new active learning approaches. A new type of project-based learning that focused on the integration of biomedical instrument and IoT, IoT-based project learning (IoTbPL) approach. Through the IoTbPL, the students have several benefits, such as integrating the IoT basic elements that consist of several electrical components or modules; gateway; and server/cloud-based system that is related to digital literacy skills. In this IoTbPL implementation, the students not only have an opportunity to apply their hard skills but also soft skills, such as collaboration and communication to build a strong team; critical thinking and creativity to implement their project idea. It is expected that the students have an industry point of view regarding their project design and implementation. This learning approach and the project results are appreciated by these two experts. One of the experts gives an opportunity to the students to carry on the prototype in an industry internship. The next IoTbPL implementation will focus on strengthening the university-industry partnership. The partner from the industry can offer the project topic. It is expected that the professional not only invited as an assessor to evaluate the project at the end of the semester but also as a mentor during the project implementation.
Penerapan Karya Tulis
According to the students' comments, active learning needs to be improved in next year's implementation.