scholarly journals The Use of Algorithmic Models to Develop Secondary Teachers’ Understanding of the Statistical Modeling Process

Author(s):  
Andrew Zieffler ◽  
Nicola Justice ◽  
Robert delMas ◽  
Michael D. Huberty
Actuators ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 68 ◽  
Author(s):  
Takuya Taniguchi ◽  
Loïc Blanc ◽  
Toru Asahi ◽  
Hideko Koshima ◽  
Pierre Lambert

Mechanically responsive materials are promising as next-generation actuators for soft robotics, but have scarce reports on the statistical modeling of the actuation behavior. This research reports on the development and modeling of the photomechanical bending behavior of hybrid silicones mixed with azobenzene powder. The photo-responsive hybrid silicone bends away from the light source upon light irradiation when a thin paper is attached on the hybrid silicone. The time courses of bending behaviors were fitted well with exponential models with a time variable, affording fitting constants at each experimental condition. These fitted parameters were further modeled using the analysis of variance (ANOVA). Cubic models were proposed for both the photo-bending and unbending processes, which were parameterized by the powder ratio and the light intensity. This modeling process allows such photo-responsive materials to be controlled as actuators, and will possibly be effective for engineering mechanically responsive materials.


2020 ◽  
Author(s):  
◽  
Wenmin Zhao

Research on mathematical modeling is growing rapidly in the field of mathematics education, and there are numerous benefits of engaging students in modeling activities. However, mathematical modeling is still marginalized in mathematics instruction. In order to understand the challenges and obstacles secondary teachers face to incorporate modeling into their classrooms, this study drew upon the theoretical perspective of practical rationality. It used a breaching experiment survey of 176 secondary teachers in Missouri and follow-up interviews with six purposefully selected survey respondents to examine secondary teachers' norms and professional obligations related to mathematical modeling. After data collection, I used descriptive statistical analyses to examine the norms and applied the professional obligation framework to analyze teacher obligations. My findings confirmed four of the six hypothesized norms: Teachers tend to precisely identify which factors should be included in students' solutions, expect students to find a symbolic representation as their model, expect students to primarily work on politically neutral tasks, and are open to students doing model revisions. However, this study did not have robust enough evidence to confirm or disconfirm whether teachers tend to give students unambiguous directions about mathematical operations or components. Nor could it uncover whether students are expected to primarily engage in mathematical thinking rather than nonmathematical thinking during the modeling process. The findings also revealed that teachers' preferred actions among the scenarios presented were influenced by their perceived professional obligations, including commitments to individual students, the classroom's interpersonal dynamics, the practice of the mathematics discipline, and the institutions they work for. More specifically, disciplinary obligations featured most often in the survey responses of those teachers who complied with canonical actions. In contrast, those who selected noncanonical options (such as opening up the beginning phases of the modeling process) most often cited their obligations to individual student thinking to justify their survey responses. The findings suggested that the mathematics education community needs to increase awareness about complex and often conflicting teacher obligations related to mathematical modeling. To support teachers in enacting modeling, the field might consider qualified professional development, viable modeling tasks and tools, and a flexible schedule as well as opportunities to build shared understandings of disciplinary practices and obligations to students' individual needs.


Author(s):  
Susanna Makela ◽  
Yajuan Si ◽  
Andrew Gelman

This chapter argues that it is wasteful to do a large, expensive poll and then just report a few percentages. Statistical modeling allows researchers to make the most effective use of available data, and graphs make it possible to convey more information more directly, both to general audiences and to specialists. Graphs are an invaluable tool at each step of the modeling process: exploring raw data, building and refining the model, and understanding and communicating the results are all made easier with graphs. In addition, graphical methods can be useful to survey researchers to understand weighting and other aspects of survey construction and analysis. The chapter includes several examples.


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