Factors influencing student success on open-ended design problems

Open-ended design problems have become an important component in our educational landscape (Grubbs and Strimel in J STEM Teach Educ 50(1):77–90, 2015; Jonassen et al. in J Eng Educ 95:139–151, 2006; National Research Council in Education for life and work: developing transferable knowledge and skills in the 21st Century, National Academies Press, Washington, 2012; Strimel in Technol Eng Teach 73(7):8–18, 2014a). The ability of students to confront open-ended problem scenarios, think creatively, and produce novel designs have all been lauded as necessary skills for today’s twenty first century learners (Partnership for 21st Century Skills in P21 framework definitions, Author, Washington, 2016). This emphasis on open-ended design problems in problem-based learning scenarios has been tied to workforce and higher education preparation for students (National Academy of Engineering and National Research Council in STEM integration in K–12 education: status, prospects, and an agenda for research, National Academies Press, Washington, 2014; National Research Council in Engineering in K–12 education: understanding the status and improving the prospects, National Academies Press, Washington, 2009; Strimel in Technol Eng Teach 73(5):16–24, 2014b). However, little research has been conducted to identify the impact of potentially-influential factors on student success in such open-ended design scenarios. Therefore, the researchers examined data from 706 middle school students, working in small groups, as they completed an open-ended design challenge to determine the relationships between a variety of potentially-influential factors and student performance, as measured through adaptive comparative judgment. The analysis of the data revealed several relationships, significant and not significant, between identified variables and student success on open-ended design challenges.     

Fast Kernel Smoothing of Point Patterns on a Large Network using Two‐dimensional Convolution

We propose a computationally efficient and statistically principled method for kernel smoothing of point pattern data on a linear network. The point locations, and the network itself, are convolved with a two‐dimensional kernel and then combined into an intensity function on the network. This can be computed rapidly using the fast Fourier transform, even on large networks and for large bandwidths, and is robust against errors in network geometry. The estimator is consistent, and its statistical efficiency is only slightly suboptimal. We discuss bias, variance, asymptotics, bandwidth selection, variance estimation, relative risk estimation and adaptive smoothing. The methods are used to analyse spatially varying frequency of traffic accidents in Western Australia and the relative risk of different types of traffic accidents in Medellín, Colombia.     

Is there an association between Vice‐Chancellors’ compensation and external performance measures?

We provide evidence on the pay for performance relation between Australian university Vice Chancellors’ compensation and independent measures of university teaching, research and other performance indicators provided by external ranking bodies. Our results show limited association between university rankings and Vice Chancellors’ compensation, but confirm that Vice Chancellors’ compensation is predominantly driven by size measures based on the different components of revenue. Further, we find that few universities offer performance‐based bonus payments. Our results are robust with respect to a number of sensitivity tests.     

Examining design cognition coding schemes for P-12 engineering/technology education

Cultivating students’ design abilities can be highly beneficial for the learning of science, technology, engineering, and mathematics (STEM) concepts, and development of higher-order thinking capabilities (National Academy of Engineering and National Research Council in STEM integration in k-12 education: status, prospects, and an agenda for research, The National Academies Press, Washington, 2014). Therefore, examining students’ strategies, how they distribute their cognitive effort, and confront STEM concepts during design experiences, can help educators identify effective and developmentally appropriate methods for teaching and scaffolding design activities for students (National Research Council in standards for k-12 engineering education? The National Academies Press, Washington, 2010). Yet, educational researchers have only recently begun examining students’ engineering design cognition at the P-12 level, despite reports such as Standards for K-12 Engineering Education? (National Research Council 2010) designating this area of research as lackluster. Of the recent studies that have investigated engineering design cognition at the P-12 level, the primary method of investigation has been verbal protocol analysis using a think-aloud method (Grubbs in further characterization of high school pre- and non-engineering students’ cognitive activity during engineering design, 2016). This methodology captures participants’ verbalization of their thought process as they solve a design challenge. Analysis is typically conducted by applying a pre-determined coding scheme, or one that emerges, to determine the distribution of a group’s or an individual’s cognition. Consequently, researchers have employed a variety of coding schemes to examine and describe students’ design cognition. Given the steady increase of explorations into connections between P-12 engineering design cognition and development of student cognitive competencies, it becomes increasingly important to understand and choose the most appropriate coding schemes available, as each has its own intent and characteristics. Therefore, this article presents an examination of recent P-12 design cognition coding schemes with the purpose of providing a background for selecting and applying a scheme for a specific outcome, which can better enable the synthesis and comparison of findings across studies. Ultimately, the aim is to aid others in choosing an appropriate coding scheme, with cognizance of research analysis intent and characteristics of research design, while improving the intentional scaffolding and support of design challenges.     

Convenient specification tests for logit and probit models

We propose several Lagrange multiplier tests of logit and probit models, which may be inexpen- sively computed by means of artificial linear regressions. These maybe used to test for various forms of model inadequacy, including the omission of specified variables and heteroskedasticity of known form. We perform a number of sampling experiments, in which we compare the small-sam- ple properties of these tests and of likelihood ratio tests. One of the LM tests turns out to have better small-sample properties than any of the others. We then investigate the power of the tests against local alternatives, and conduct a further series of sampling experiments to compare the power of various tests.     

Monetary anticipations and the demand for money

It has recently been argued that unanticipated changes in nominal money supply should affect the real demand for money, and empirical results which appear to support this hypothesis have been presented. Unfortunately, the econometric techniques used by previous authors yield severel biased estimates. In this paper we show how valid estimates may easily be obtained, and present some illustrative empirical results using U.S. data. These results do not support the conclusions of earlier authors. We find that unanticipated money supply changes do indeed affect the real demand for money, but with the wrong sign, and that anticipated changes also have a significant effect.A consistency test for all models employing the anticipated-unanticipated distinction is also proposed.