| Recent reports proclaim that more students should engage in enrolling in STEM programs at an early stage of schooling. Due to insufficient number of students pursuing in STEM programs, lot of them missing out too early in their educational career. Nine out of every ten adults had at least a high school or general education diploma, but when it comes to a higher degree only one out of every three adults held a higher degree. To overcome this problem, we need to attract more students by implementing scientific STEM projects where they can actively work on and give them a hand on learning experience how STEM will be helpful in upgrading their career.;In this thesis, we have gone through several technological methodologies which were used as a case study in education and have chosen active or hands on learning methods which are suitable to design an engineering STEM training toolkit. In this training toolkit, we have used different sensors which are capable of reading real-time data. These sensors are connected to the Raspberry Pi and programmed with Wolfram Mathematica to design an interface which simplifies the learning curve and educate students with buttons or on click interface events on Wolfram Mathematica to read the sensor data in real-time. While developing this training toolkings on Raspberry Pi, we desired to comprehend the limitations of it in terms of execution and response time. It also to measure quantifying sensors data it can handle when connected parallel with I2C and to know if there is any memory, power or processing issue of Raspberry Pi while connecting these sensors. After determining the results of the experimental setup, we determined that the execution and response time increases whenever we add additional sensors to the Raspberry Pi. It is found that the 5 Voltage powers of Raspberry Pi is not distributing equal across all the sensors. |
