A parameter estimation method for stiff ordinary differential equations using particle swarm optimisation

We propose a two-step method for fitting stiff ordinary differential equation (ODE) models to experimental data. The first step avoids integrating stiff ODEs during the unbounded search for initial estimates of model parameters. To avoid integration, a polynomial approximation of experimental data is generated, differentiated and compared directly to the ODE model, obtaining crude but physically plausible estimates for model parameters. Particle swarm optimisation (PSO) is used for the parameter search to overlook combinations of model parameters leading to undefined solutions of the stiff ODE. After initial estimates are determined, the second step numerically solves the ODE. This refines model parameter values through a bounded search. We demonstrate this method by fitting the model parameters (activation energies and pre-exponential factors) of the Arrhenius-based temperature-dependent kinetic coefficients in the shrinking core solid-state chemical kinetics model for the reduction of Cobalt (II, III) Oxide (Co3 role= presentation style= display: inline; line-height: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; font-family: Helvetica Neue , Helvetica, Arial, sans-serif; position: relative; \u3e33O4 role= presentation style= display: inline; line-height: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; font-family: Helvetica Neue , Helvetica, Arial, sans-serif; position: relative; \u3e44) particles to Cobalt (II) Oxide (CoO)

A Survey Tool for Assessing Student Expectations Early in a Semester

Quality learning is fostered when faculty members are aware of and address student expectations for course learning activities and assessments. However, faculty often have difficulty identifying and addressing student expectations given variations in students’ backgrounds, experiences, and beliefs about education. Prior research has described significant discrepancies between student and faculty expectations that result from cultural backgrounds (1), technological expertise (2), and ‘teaching dimensions’ as described by Trudeau and Barnes (4). Such studies illustrate the need for tools to identify and index student expectations, which can be used to facilitate a dialogue between instructor and students. Here we present the results of our work to develop, refine, and deploy such a tool.<span style="color: black; font-family: 'Times New Roman'; font-size: x-small;"></span

Expectations of Computing and Other STEM students: A Comparison for Different Class Levels, or (CSE &# x2260; STEM-CSE) &# x007C; course level

Students begin each new course with a set of expectations. These expectations are formed from their experiences in their major, class level, culture, skills, etc. However, faculty and the students are often not on the same page with respect to expectations even though faculty provide students with course syllabi. It is crucial for faculty to understand students\u27 expectations to maximize students\u27 learning, satisfaction, and success. Furthermore, it would promote classroom transparency. There would be no hidden unstated expectations; disappointments during the course can potentially be minimized. We present the results of a survey focused on understanding student expectations. Specifically, we focus on examining the differences in expectations of the students of Computer Science and Engineering (CSE) courses and non-computing STEM courses. We present our analysis and observations of the results using aggregate data for all students at all class levels. We observe various differences and similarities among the STEM fields. Identifying differences is crucial since many non-computing STEM majors are enrolled in computing courses, especially in the lower level courses. We provide a detailed comparison among sophomore and senior level courses in computing, biology and chemistry courses. We also compare sophomore and senior CSE courses. Finally, we discuss the importance of paying attention to all students\u27 needs and expectations. Armed with this knowledge, faculty members can increase transparency in the classroom, student satisfaction, and possibly student retention