If you teach basic statistics in any form, you have probably dealt with the sharp contrast between ‘statistics’ and ‘statistical study’; in other words, there is a large difference between statistical data and the practice of statistics. Having data does not mean there has been a statistical study. In a similar way, having data does not mean that there has been research.<\/p>\n
Research is an abstraction of this ‘data => statistical study’ to a higher level; research involves a prolonged effort to answer meaningful questions in a field of study, usually involving multiple researchers. Research, in this meaning, is rare in developmental mathematics — we have lots of data, quite a few studies, but not that much research. Research strives to provide richer and more subtle answers, and deals with a common core of issues.<\/p>\n
One of my friends (thanks, Laura!) recently passed along a link to an article on research in developmental mathematics; this article is by Peter Bahr, whom I had read a few years ago (he’s been busy!). The current article is called A Case for Deconstructive Research on Community College Students and Their Outcomes<\/em><\/strong>, and is available online at http:\/\/cepa.stanford.edu\/sites\/default\/files\/Bahr%203_26_12.pdf<\/a> <\/p>\n This article places research on developmental math within a larger framework of research in community colleges, focusing on student progression. Which factors in a progression make a difference in the eventual outcome? One of the conclusions Dr. Bahr reaches is that beginning algebra is a critical course; not passing this course on the first attempt raises the risk that a student will not complete — even if they persist to try the course again. The article has several other points with practical implications for us, and for policy makers.<\/p>\n Instead of saying that remedial math is part of a ‘bridge to nowhere’ (the mistaken message of Complete College America), research into developmental mathematics takes a more intelligent (and difficult) approach of identifying specific features that have positive or negative impacts on student outcomes. This research is too specialized for policy makers to understand, even if they understand research as opposed to statistics; part of our responsibility is to articulate what this research means in a manner that policy makers can understand.<\/p>\n I hope that you will use research like the Bahr article to suggest basic changes in your developmental math program.<\/p>\n Join Dev Math Revival on Facebook:
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