A Framework for Integrating Intelligent Sensor Measurement Data into Engineering Education

In the recent literature, researchers have investigated the mismatch between teaching and learning styles with different research objectives. This paper presents a framework for integrating intelligent sensor real-time measurement data into engineering education for innovative practice-oriented learning environment. In this integration framework, intelligent sensors are deployed on Local Area Networks (LAN) in engineering laboratories to measure physical quantities that may be used for classroom training. Engineering laboratories may involve measurement of dynamic processes since physical quantities measured by sensors such as temperature, humidity, pressure, displacement, voltage, current, etc., are continuous in nature. To improve the quality of measurement data there is a need to remove unwanted signals associated with the measured input signal; hence self-compensation algorithms are formulated for implementation in the sensor nodes. It is noted that presentations that use both visual and auditory styles reinforce learning for most students. Indeed, students’ learning may be motivated and students’ engagement and comprehension of fundamental engineering principles (or concepts) may be increased by a teaching style that balances concrete information with theoretical concepts. In the integration framework presented in this paper, professors/instructors may activate and take real-time reading from sensors located in the laboratories for purpose of illustrating or emphasizing engineering concepts (or principles). Moreover, in order to assess the acceptability of this framework in a learning environment, Questionnaires are administered to gather pertinent information from engineering professors and students. It is shown by the results obtained that this framework has high level of acceptability, and will create better learning experience for engineering students.