The Integration of Data Analytics to Assess Multi-Complex Environments of Research to Practices in Engineering Education

The integration of data analytics in engineering education to address technical requirements from a multi-complex environment perspective concept will explore areas of research to practices category in regards to the current work in progress using data analytics tools (e.g., IBM Watson Analytics). The results obtained from a multi-complex environment have aided students and improved their decision approach to quantify data accuracy and project requirements in education practices for predictive learning. In using the data sets developed from Watson Analytics, this assembly of display in multi-complex environments provided students with the ability to assess and understand the visual presentation to determine predictive models in data exploration. Data exploration was used to identify a research approach in the education assessment of the multi-complex environments of engineering students’ projects. The multi-complex environments and the variables assessment also provided insight with an understanding of project requirements and objectives using data visualization techniques and decision relationships gained from data exploration. This approach investigated the learning methods and decision practices through pattern recognition, educational objectives and course outcomes in specific multi-complex environments with efforts supporting research to practices. The integration of analytics tools with regard to decision-based learning allowed the engineering students the ability to forecast requirements and create new methods critical to their engineering design. This was significant due to the students’ ability to model decisions in a manner that experts had challenged engineering education using research to practices to address aspects of the multi-complex environments based on industry standards. This technique had also improved the practical implication for student learning and the decision methods to support research in engineering education with regard to predictive learning and modeling design methods.