The study I report on today was one for which I had high hopes.  The topic, digital equity, is near and dear to me as it was the focus of my dissertation and one of my first major academic publications.  However, after reading the article, Digital Equity: New Findings from the Early Childhood Longitudinal Study, by Sharon Judge, Kathleen Puckett and Burcu Cabuk, I had many critiques.  Mostly, I felt like the authors had certain expectations and wanted to write about a digital divide; however, the data did not ultimately support their hypotheses.  Yet, they highlighted very small conclusions and “buried the lead.”  So, with all due respect to the authors, I’m going to use this article to point out some of my critiques and to surface the lead story, which is that, when properly “measured,”  and despite what most people think, students in high poverty schools have equal or greater access to computers in schools than their wealthier counterparts; schools are leveling the playing field that is unbalanced only beyond the bricks-and-mortar school buildings.

My first critique of the article is that the authors make statements of the following sort:

The problem here is that the last sentence invalidates the first two.  That is, if there are no statistically significant differences, it is simply wrong to suggest that one group is higher or lower than the other.  This is a basic rule of inferential statistics.  Where one group appears higher or lower, without statistical significance, those perceived differences may very well be due to chance, especially where the sample size is as large as the one used in this study.

My second major critique is that the authors highlight their conclusion that “…children attending higher poverty schools had significantly fewer computers and software programs available.” They even include this in their abstract. While that conclusion may be accurate based on some dichotomous dependent variable (e.g. are there any computers in your school?), it is not true when considering other better measures. For instance, how could the authors bury these findings?:

• Students in high poverty schools were significantly more likely (23% vs. 18%) to attend a school with a student:computer ratio of 4:1 or better.
• First-grade children attending higher poverty schools had more adequate computer labs than children attending lower poverty schools (t = 1.97, p < .05)…In addition, higher poverty schools employed significantly more full-time computer specialists compared to lower poverty schools (t = 5.76, p < .001).

Those seem like pretty important conclusions, no?

Or, consider this statement, which resonates most with me and, concerns me most:  “In first grade, higher poverty schools used computers for instructional purpose significantly more for read/write/spell, whereas lower poverty schools used computers significantly more for fun.” In first grade? Really?

You see, these authors highlighted one very minimal finding and failed to emphasize that what they found was that schools have done a good job of leveling the digital playing field that is only (and substantially) unbalanced beyond the bricks-and-mortar school buildings. In fact, in many cases, lower income students have greater access to computers in schools than their wealthier counterparts. The research and policy questions that must be asked now relate more to the last finding I highlighted. That is, the REAL digital divide in education may be the one that exists with respect to use; not amounts of use, but types of use. Do low-income students and/or students of color have equal opportunities to learn with more current, more relevant digital applications? That’s the research and policy question I want explored.

Tags: digital equity, education, equity, research, technology

I’m newly come to the Amy Winehouse bandwagon and I’ve been searching for a Wii to purchase for a while now.  Given those interests, this video absolutely cracked me up.  Enjoy.

[youtube=http://www.youtube.com/watch?v=DxyFKJnjJA0&rel=0&color1=0x006699&color2=0x54abd6&border=0]

My brother-in-law works for a PR firm that does a lot of work with and for Microsoft.  As part of that work, he is coordinating a trial program for Microsoft Student with Encarta Premium 2008.  Here’s part of the letter he sent me…

Normally I wouldn’t pass along these sorts of offers, but this is my brother-in-law; this isn’t just something I stumbled upon.  Also, I know how complicated it is to “try” something new at this point in the school year.  But, if you’re willing and able, please do participate!    Thanks!

 In the evaluation work I do, I’m occasionally asked to study/assess the cost effectiveness of a “product.”  This makes some sense where the product is hardware or some kind of proprietary digital object.  And, there, COSN’s work on Total Cost of Ownership is exemplary.  But, when it comes to many software programs, even comprehensive programs, things have changed.  In the day and age of Moodle and other free, open-source programs, cost effectiveness is moot.  I’m not tapped in to the open source in education community, but I’d love to know what prevents open source from making a bigger impact in education.  At a time when the economy is slow and budgets are tight, how do schools/districts continue to justify ed. tech. expenditures where equally good and free alternatives exist?

Tags: budgets, cost effectiveness, education, evaluation

[THIS IS THE SECOND ENTRY IN MY WEEKLY SERIES]

I’m not alone in my concerns over the achievement gap and educational equity more generally.  I have, however, felt for a while now that among the many articles and reports I’ve read on these matters, the most convincing is this one.  Schools, Achievement, and Inequality: A Seasonal Perspective, written by Alexander, Entwistle & Olsen in 2001, tells a compelling empirical tale of the cumulative effects of two phenomena:  differences in school readiness and summer learning loss.  As the simple chart below shows (the data are made up), by the time low income students reach school age, they trail their higher income counterparts with respect to student achievement.  Over the course of subsequent school years, the schools serve all students equally well (i.e. the slopes of the achievement lines are equivalent from Fall to Spring).  Then, over the summer, higher income students demonstrate slight achievement gains (not nearly as much as during the school year) while low income students make no gains (nor do they necessarily suffer learning loss).  The result, over time, is that the achievement gap between low- and high-income students expands over time, though that widening can be attributed mostly to “out-of-school” factors.

So, the policy implications are fairly clear: if we are serious about narrowing the achievement gap, we need to fund universal pre-K programs, and seriously consider either funding summer programs for low-income students or, more radically, think about year-round schooling.  Schooling according to the agrarian calendar has run its course?

 [click for larger image]

Tags: achievement, education, equity, policy