The Interplay of Psychology and Mathematics Education: From the Attraction of Psychology to the Discovery of the Social

It is a rather safe statement to claim that the social dimensions of the scientific process are accepted in a fair share of studies in the philosophy of science. It is a somewhat safe statement to claim that the social dimensions are now seen as an essential element in the understanding of what human cognition is and how it functions. But it would be a rather unsafe statement to claim that the social is fully accepted in the philosophy of mathematics. And we are not quite sure what kind of statement it is to claim that the social dimensions in theories of mathematics education are becoming more prominent, compared to the psychological dimensions. In our contribution we will focus, after a brief presentation of the above claims, on this particular domain to understand the successes and failures of the development of theories of mathematics education that focus on the social and not primarily on the psychological.     

Within‐High‐School Versus Across‐High‐School Scaling of Admissions Assessments: Implications for Validity and Diversity Effects

Previous research has established that SAT scores and high school grade point average (HSGPA) differ in their predictive power and in the size of mean differences across racial/ethnic groups. However, the SAT is scaled nationally across all test takers while HSGPA is scaled locally within a school. In this study, the researchers propose that this difference in how SAT scores and HSGPA are scaled partially explains differences in validity and subgroup differences. Using a large data set consisting of 170,390 students each of whom matriculated at one of 114 separate colleges, the researchers find that awarding SAT scores by ranking SAT within a high school generally results in substantial reduction in the size of subgroup mean differences for this predictor. However, validity for predicting first‐year GPA is also reduced by a small amount. Conversely, placing HSGPA onto a nationally normed metric through the use of multiple regression procedures results in a moderate increase in the size of subgroup mean differences, while also producing a small increase in validity. Taken together, these findings suggest that differences in predictor scaling can partially explain differences in the size of subgroup mean differences between HSGPA and SAT scores and have implications for predictive power.     

Public purpose and the preparation of teachers for rural schools

Abstract

Rural communities need rural teachers who can “ground” traditional school subjects in local realities and dilemmas and at the same time fashion instructional approaches consonant with larger intellectual, ethical, and social purposes. Such teachers are assets rather than burdens to their communities. Only such teachers can cultivate an ethic of responsibility for the health and vitality of the communities of which they and their students are critical components.

This essay examines the role of the university in professionalizing rural teachers. It contends that narrowly utilitarian ends prevail in teacher education programs, and that, in the absence of a substantive definition of educations public purpose, this instrumentalism creates teachers who are ill‐prepared to contribute in meaningful ways to the intellectual life of the communities in which they work. Teacher preparation programs should abandon the instrumental focus. Programs that prepare rural teachers, in fact, confront unique challenges and opportunities to include three themes at their centers: Sustainability, Social Justice, and Democracy.     

SPREAD: A Distributed Simulation Toolkit

This article describes “SPREAD”, a simulation toolkit, and its use in building “Virtual Robots”, a simulation of multiple mobile robot vehicles used in the teaching of computer science at university level. A novel aspect of the simulator is the use of PVM (Parallel Virtual Machine) to achieve high performance at low cost by using spare CPU cycles on large numbers of networked workstations.     

Application of the Levenshtein Distance Metric for the Construction of Longitudinal Data Files

The analysis of longitudinal data in education is becoming more prevalent given the nature of testing systems constructed for No Child Left Behind Act (NCLB). However, constructing the longitudinal data files remains a significant challenge. Students move into new schools, but in many cases the unique identifiers (ID) that should remain constant for each student change. As a result, different students frequently share the same ID, and merging records for an ID that is erroneously assigned to different students clearly becomes problematic. In small data sets, quality assurance of the merge can proceed through human reviews of the data to ensure all merged records are properly joined. However, in data sets with hundreds of thousands of cases, quality assurance via human review is impossible. While the record linkage literature has many applications in other disciplines, the educational measurement literature lacks details of formal protocols that can be used for quality assurance procedures for longitudinal data files. This article presents an empirical quality assurance procedure that may be used to verify the integrity of the merges performed for longitudinal analysis. We also discuss possible extensions that would permit merges to occur even when unique identifiers are not available.