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.     

The challenge to make schools safe: Preparing education personnel to curb student aggression and violence

Abstract

The incidence of aggression and violence in schools is approaching epidemic proportions. Unfortunately, few teacher education programs offer coursework designed to prepare school personnel to face the mounting challenge of student aggression. Drawing upon the accumulated literature, changes are discussed in the content of teacher education programs that would prepare teachers to effectively address student violence. Assertions are made that colleges of education must assume leadership roles in redefining professional boundaries to prepare all school personnel to combat student aggression. Lastly, arguments are presented for strengthening university and public school collaboration to promote programs designed to curb the rising tide of student aggression and violence.     

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.