Understanding best practices in control engineering education using the concept of TPACK

This study aimed to design an integrated pedagogical approach to advance introductory Process Control Engineering Education through the application of the Technological Pedagogical Content Knowledge (TPACK) framework, and evaluating its impact on student learning. The research is initially being undertaken at Nottingham Trent University, UK but we will next adapt it to a case study in Libya. This paper aims to strengthen the teaching of introductory Process Control by using appropriate approach es in universities to improve the learning outcomes for students. From this work a new schematic for teaching Process Control ha s be en developed and, moreover, a thoughtful best practice in introducing Process Control in engineering education can be developed

An investigation and development of high level control engineering training packages for higher education and industry

This study investigates using the best technological pedagogical approaches for teaching in Higher Education (HE) in Science, Technology, Engineering and Mathematics (STEM), using Control Engineering as a case study. Five objectives directed the study: first, it examined tutors' understanding of integrated technology to pedagogy and content; second, it developed a self-assessment instrument of understanding integrated technology, content and pedagogy for tutors in HE; third, it examined approaches to selecting the content and developing the curriculum; fourth, it developed a teaching and learning framework for HE to meet the needs of students and the industrial sector; finally, it implemented and assessed this framework in real modules at Nottingham Trent University at undergraduate and postgraduate levels. The Technological Pedagogical and Content Knowledge framework (TPACK) guided this study and the instrument was developed to assess the tutors' understanding of the TPACK framework in HE. The study used qualitative and quantitative approaches (mixed methods) under the post-positivist and constructivist paradigms (worldview). Through the use of purposive sampling, a total of 111 tutors and 120 students responded to the study. The questionnaires were used as a quantitative method, and semi-structured interviews, open-ended questions, observations and the literature review were used as qualitative methods. Quantitative data was analysed using the Statistical Package for the Social Sciences (SPSS). Principal Component Analysis (PCA) was used to check the validity of the instrument; Cronbach’s alpha was used as a reliability measure; t-test, correlation and regression were performed to examine the effectiveness of implementing a new pedagogical HE framework which was developed based on TPACK. The findings disclosed the validity of the TPACK framework in HE for control engineering teaching and indicated the likely benefits for HE STEM education in general; and they enabled the development of a self-assessment instrument for tutors in HE. The validity and reliability have been demonstrated in English; and the initial work on translation to Arabic is positive (originally, a case study was planned in Libya). The instrument helps to assess tutors in-service and pre-service training for Continuing Professional Development (CPD). This research proposes a training model within TPACK for tutors in HE, based on factor analysis (PCA) results, which clarify the most appropriate path to follow in particular training courses based on the real needs of the participant tutors. Finally, the research developed and investigated a new pedagogical framework (the AJ Framework) for teaching and learning in HE STEM and confirmed the effectiveness at BSc and MSc levels in control engineering. This study recommends that training in TPACK and the AJ Framework would provide HE tutors with wider understanding of technology-enhanced teaching and learning. Also, that there is a need to integrate the student feedback system (student evaluation surveys for modules and courses) with the rest of the NOW system (Nottingham Trent Online teaching and learning Workspace). Potential areas of other future work are discussed

Power curve modelling for wind turbines

The wind turbine power curve WTPC describes the relationship between wind speed and turbine power output. Power curve, provided by the manufacturer is one of the most important tools used to estimate turbine power output and capacity factor. Hence, an accurate WTPC model is essential for predicting wind energy potential. This paper presents a comparative study of various models for mathematical modelling of WTPC based on manufacturer power curve data gathered from 32 wind turbines ranging from 330 to 7580 kW. The selected models are validated by comparing the capacity factor obtained using the models based on Gamma probability density function with the capacity factor estimated using manufacturer power curves based on measured wind speed data. The selected models are also validated by comparing the instantaneous power obtained using the models with manufacturer power curve data. The accuracy of the models is evaluated using statistical criteria such as Normalized Root Mean Square Error (NRMSE), relative error (RE), and correlation coefficient (R). The adopted model allows predicting the behavior of wind turbine generated under different wind speeds. Results of the analysis presented in this paper show that the power-coefficient based model presents favorable efficiency followed by general model, since they have lower values of RE in estimation of capacity factor, whereas the polynomial model showed the least accurate model