College Match and Undermatch: Assessing Student Preferences,College Proximity,and Inequality in Post-College Outcomes

Recently, multiple studies have focused on the phenomenon of “undermatching”—when students attend a college for which they are overqualified, as measured by test scores and grades. The extant literature suggests that students who undermatch fail to maximize their potential. However, gaps remain in our knowledge about how student preferences—such as a desire to attend college close to home—influence differential rates of undermatching. Moreover, previous research has not directly tested whether and to what extent students who undermatch experience more negative post-college outcomes than otherwise similar students who attend “match” colleges. Using ELS:2002, we find that student preferences for low-cost, nearby colleges, particularly among low-income students, are associated with higher rates of undermatching even among students who are qualified to attend a “very selective” institution. However, this relationship is weakened when students live within 50 miles of a match college, demonstrating that proximity matters. Our results show that attending a selective postsecondary institution does influence post-college employment and earnings, with less positive results for students who undermatch as compared with peers who do not. Our findings demonstrate the importance of non-academic factors in shaping college decisions and post-college outcomes, particularly for low-income students.     

Innovation in regions: What does really matter?

The literature on systems of innovation conceptualises innovation as an evolutionary and social process of collective learning. But three main questions remain open: is this learning process rather internal or external? What are the boundaries of this process? Is this social process voluntary or rather unintended? This paper strives to tackle those questions by analysing the determinants of regional innovative levels in French manufacturing industries. By estimating a knowledge production function we find that building external interactions is of greater importance than developing internal innovative competences. Second, unintended knowledge flows have less influence on regional innovative performance than deliberate ones. Lastly, the French innovation system looks regional rather than sectoral.     

Old Enemies？

本专栏由英国大使馆文化处供稿,旨在让读者从中学习地道英文,体悟英文百味。本期介绍了英国的四个组成部分——英格兰、苏格兰、威尔士和北爱尔兰之间的“恩怨”与“宿仇”。由于英格兰地域广,政治、经济实力强,不了解英国的人通常误把英格兰当英国。但是,在体育上,其他几个“兄弟”可是要扳回一城的……     

FORECASTING CREDIT HOURS

This paper employs Tobit to estimate retentionprobabilities and credit hours at two universities. Theinnovation is that this technique examines credit-hourchoice with the decision to depart the university treated as the choice of zero credit hours.Tobit is appropriate for this problem because itrecognizes the lower bound of zero on credit hours andincorporates this bound into the parameter estimates and forecasts. Models are estimated for credithours in a single year and cumulative hours over asix-year horizon.     

Not Your Usual Maths Course: critical mathematics education for adults

This article describes an action research project that investigated which features of critical theory were useful for teaching everyday mathematics in an evening course for adults. Paulo Freire's philosophy of education and Jürgen Habermas’ theory of knowledge interests are among the main influences on exponents of critical mathematics education such as Ole Skovsmose and Marilyn Frankenstein. Through the use of dialogical processes and critical reflection, students engage in social and political issues in their lives or communities and, as a result of their increased consciousness and mathematical learning, take “transforming action”. This study highlights the value of learning in a positive environment, through dialogical processes and critical questioning. Participants of the study were able to overcome barriers to learning mathematics; engage in everyday issues involving mathematics; and make changes to the way they dealt with mathematics, as a result of increased critical consciousness.     

Improving document clustering in a learned concept space

Most document clustering algorithms operate in a high dimensional bag-of-words space. The inherent presence of noise in such representation obviously degrades the performance of most of these approaches. In this paper we investigate an unsupervised dimensionality reduction technique for document clustering. This technique is based upon the assumption that terms co-occurring in the same context with the same frequencies are semantically related. On the basis of this assumption we first find term clusters using a classification version of the EM algorithm. Documents are then represented in the space of these term clusters and a multinomial mixture model (MM) is used to build document clusters. We empirically show on four document collections, Reuters-21578, Reuters RCV2-French, 20Newsgroups and WebKB, that this new text representation noticeably increases the performance of the MM model. By relating the proposed approach to the Probabilistic Latent Semantic Analysis (PLSA) model we further propose an extension of the latter in which an extra latent variable allows the model to co-cluster documents and terms simultaneously. We show on these four datasets that the proposed extended version of the PLSA model produces statistically significant improvements with respect to two clustering measures over all variants of the original PLSA and the MM models.