Consensus-based course design and implementation of constructive alignment theory in a power system analysis course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractThis article presents the implementation of the constructive alignment theory (CAT) in a power system analysis course through a consensus-based course design process. The consensus-based design process involves both the instructor and graduate-level students and it aims to develop the CAT framework in a holistic manner with the goal of including different perceptions. The considerations required to implement this approach are described in detail. To examine the effect of this approach, three different course evaluations were conducted by querying the students during different stages of the course. These evaluations show that most of the students find a benefit for their learning in the implementation of CAT within the new course design. These observations are supported by a comparison of the students' performance in the new course and the previous one. Finally, the revised two-factor study process questionnaire (R-SPQ-2F) is utilised to identify the students' learning approach towards the course. The aim is to correlate the students' approach with their final grade to assess if students adopting a deep learning approach are rewarded with higher marks and vice versa, that is, to check if the CAT implementation was successful. Meanwhile, some of the R-SPQ-2F limitations, which affect the quality of the results, are identified and discussed. Additionally, to facilitate the practical usage of R-SPQ-2F, an algorithm was developed by the authors to rank the students' approach towards the course. The results of the new ranking algorithm demonstrate positive correlation with the students' final grade, which is an indication of the effective CAT implementation.Keywords: constructive-alignment theorytwo-factor study process questionnairepower system analysis AcknowledgementsThe economical support of the institutions and funding bodies listed below is sincerely acknowledged: Statnett SF, the Norwegian Transmission System Operator; andThe STandUP for Energy collaboration initiative.About the authorsLuigi Vanfretti received the Electrical Engineering degree from Universidad de San Carlos de Guatemala, Guatemala City, Guatemala, in 2005, and the MSc and Ph.D. degrees in electric power engineering from Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.He was a Visiting Researcher with The University of Glasgow, Glasgow, Scotland, in 2005. He became an Assistant Professor with the Electric Power Systems Department, KTH Royal Institute of Technology, Stockholm, Sweden, in 2010 and was conferred the Swedish title of 'Docent' in 2012. He is currently a tenured Associate Professor with the same department.He is Special Advisor in Strategy and Public Affairs for the Research and Development Division of Statnett SF, the Norwegian transmission system operator, where he previously served as Scientific Advisor from 2011 to 2013. His duties include architectural analysis for synchrophasor data transfer, communications, and application systems to be utilised in Smart Transmission Grid applications; as well as providing inputs into R&D strategy development and aiding in the execution of collaborative projects with universities, TSOs, and R&D providers.Dr Vanfretti has served, since 2009, in the IEEE PES PSDP Working Group on Power System Dynamic Measurements, where he is now Vice-Chair. In addition, since 2009, he has served as Vice-Chair of the IEEE PES CAMS Task Force on Open Source Software. For his research and teaching work towards his Ph.D. degree, he was awarded the Charles M. Close Award from Rensselaer Polytechnic Institute.He is a lecturer in power system analysis and carries out research to enhance student learning through the implementation of constructive alignment theory and the use of Free and Open Source Software in his teaching. His research interests are in the general area of power system dynamics,;hile his main focus is on the development of applications of PMU data.Mostafa Farrokhabadi obtained the BSc in Electrical Engineering from Tehran Polytechnic University in 2010. He recently obtained the MSc in Electrical Power Engineering degree from KTH Royal Institute of Technology, Stockholm, Sweden, in April 2012. Currently, he is a Ph.D. student at Electrical and Computer Engineering Department, University of Waterloo, Canada.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it