This research-to-practice paper investigated the development of Functional Analysis Learning Trajectories (FALT) within the undergraduate courses for engineers including Calculus, Differential Equations, Communication...
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ISBN:
(数字)9798350351507
ISBN:
(纸本)9798350363067
This research-to-practice paper investigated the development of Functional Analysis Learning Trajectories (FALT) within the undergraduate courses for engineers including Calculus, Differential Equations, Communications Theory, Control Systems and Electromagnetism. Focusing on the local instructional practices, manifestations of functional analysis ideas are tracked along these courses. Corresponding concept maps are collaboratively within and across courses to portray the relevant connections for engineeringmathematics education. These trajectories help engineering and mathematics instructors to collectively design, implement, refine task sequences to improve students' preparedness for upper-level math-heavy courses in engineering, connecting mathematics to their disciplines. Here we utilized collaborative concept mapping as a research heuristics to build curricular innovations with functional analysis learning trajectories across courses, hierarchically arranging, integrating, and conceptually connecting instructional tasks. Through sequences of dual stance learning tasks, students are given opportunities to take multiple stances in a learning task from the perspectives of engineers and mathematical scientists. A higher stance on mathematics was supported to be developed by comparing and connecting alternative disciplinary perspectives and practices with the dual modeling tasks and reflecting on the cross-cutting ideas along the learning trajectory. Here we present the collaborative design and analysis of concept maps along functional analysis learning trajectories for undergraduates. This research builds an interdisciplinary scholarship of teaching/learning mathematics across disciplines among engineering and math faculty. This work helps foster reflection and collaboration on their teaching practices to design and implement instructional tasks to build coherent mathematical perspectives across disciplines. It exemplifies how to design a research-based practices
In process monitoring applications, measurements are often taken regularly or randomly from different spatial locations in two or three dimensions. Here, we consider streams of regular, rectangular data sets and use s...
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Spatiotemporal graph neural networks (STGNNs) have shown promising results in many domains, from forecasting to epidemiology. However, understanding the dynamics learned by these models and explaining their behaviour ...
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This paper considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations. A latent group str...
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We analyse the performance of a reconfigurable intelligent surface (RIS) aided system where the RIS is divided into subsurfaces. Each subsurface is designed specifically for one user, who is served on their own freque...
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Geometric control theory, developed by Basile and Marro, and independently, by Wonham and Morse in the 1970s revolves around characterizing the properties of finite dimensional, linear and time-invariant systems using...
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To solve the problem that the maximal feature element in quaternion matrix decomposition for color image watermarking is difficult to be extracted, two new quaternion Givens transformations with realvalued rotation re...
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Academic performance serves as a direct indicator of learning effectiveness. In the paper, a stacking learning method is proposed to predict students’ performance in exam. Firstly, an in-depth correlation analysis is...
Academic performance serves as a direct indicator of learning effectiveness. In the paper, a stacking learning method is proposed to predict students’ performance in exam. Firstly, an in-depth correlation analysis is conducted to examine various factors, including students’ school situation and exam scores in other subjects, which have an impact on the exam scores in a specific subject. It is found that a strong correlation exists between exam scores in different subjects, and factors such as students’ lunch level and parents’ education level significantly impact these scores. Secondly, the stacking integration strategy employs Random Forest (RF) as the base learner, while the random gradient model is selected as the meta learner to construct a predictive model for learning achievement. Finally, a comparative experiment is conducted using multiple models, including Decision Trees (DT) and support vector machines (SVM), to predict students’ exam scores. The results show that the learning achievement prediction model based on the stacking integration strategy outperforms all other tested models, exhibiting an accuracy improvement ranging from 6% to 17%. Moreover, its performance in statistical evaluation indicators and error evaluation indicators are well. The proposed learning method is more efficient to predict students’ performance in exams.
For over a century, extrapolation methods have provided a powerful tool to improve the convergence order of a numerical method. However, these tools are not well-suited to modern computer codes, where multiple continu...
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We show that the Potts model on a graph can be approximated by a sequence of independent and identically distributed spins in terms of Wasserstein distance at high temperatures. We prove a similar result for the Curie...
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