Posts tagged ‘SES’

Adaptive Parsons problems, and the role of SES and Gesture in learning computing: ICER 2018 Preview

 

Next week is the 2018 International Computing Education Research Conference in Espoo, Finland. The proceedings are (as of this writing) available here: https://dl.acm.org/citation.cfm?id=3230977. Our group has three papers in the 28 accepted this year.

“Evaluating the efficiency and effectiveness of adaptive Parsons problems” by Barbara Ericson, Jim Foley, and Jochen (“Jeff”) Rick

These are the final studies from Barb Ericson’s dissertation (I blogged about her defense here). In her experiment, she compared four conditions: Students learning through writing code, through fixing code, through solving Parsons problems, and through solving her new adaptive Parsons problems. She had a control group this time (different from her Koli Calling paper) that did turtle graphics between the pre-test and post-test, so that she could be sure that there wasn’t just a testing effect of pre-test followed by a post-test. The bottom line was basically what she predicted: Learning did occur, with no significant difference between treatment groups, but the Parsons problems groups took less time. Our ebooks now include some of her adaptive Parsons problems, so she can compare performance across many students on adaptive and non-adaptive forms of the same problem. She finds that students solve the problems more and with fewer trials on the adaptive problems. So, adaptive Parsons problems lead to the same amount of learning, in less time, with fewer failures. (Failures matter, since self-efficacy is a big deal in computer science education.)

“Socioeconomic status and Computer science achievement: Spatial ability as a mediating variable in a novel model of understanding” by Miranda Parker, Amber Solomon, Brianna Pritchett, David Illingworth, Lauren Margulieux, and Mark Guzdial

(Link to last version I reviewed.)

This study is a response to the paper Steve Cooper presented at ICER 2015 (see blog post here), where they found that spatial reasoning training erased performance differences between higher and lower socioeconomic status (SES) students, while the comparison class had higher-SES students performing better than lower-SES students. Miranda and Amber wanted to test this relationship at a larger scale.

Why should wealthier students do better in CS? The most common reason I’ve heard is that wealthier students have more opportunities to study CS — they have greater access. Sometimes that’s called preparatory privilege.

Miranda and Amber and their team wanted to test whether access is really the right intermediate variable. They gave students at two different Universities four tests:

  • Part of Miranda’s SCS1 to measure performance in CS.
  • A standardized test of SES.
  • A test of spatial reasoning.
  • A survey about the amount of access they had to CS education, e.g., formal classes, code clubs, summer camps, etc.

David and Lauren did the factor analysis and structural equation modeling to compare two hypotheses: Does higher SES lead to greater access which leads to greater success in CS, or does higher SES lead to higher spatial reasoning which leads to greater success in CS? Neither hypothesis accounted for a significant amount of the differences in CS performance, but the spatial reasoning model did better than the access model.

There are some significant limitations of this study. The biggest is that they gathered data at universities. A lot of SES variance just disappears when you look at college students — they tend to be wealthier than average.

Still, the result is important for challenging the prevailing assumption about why wealthier kids do better in CS. More, spatial reasoning is an interesting variable because it’s rather inexpensively taught. It’s expensive to prepare CS teachers and get them into all schools. Steve showed that we can teach spatial reasoning within an existing CS class and reduce SES differences.

“Applying a Gesture Taxonomy to Introductory Computing Concepts” by Amber Solomon, Betsy DiSalvo, Mark Guzdial, and Ben Shapiro

(Link to last version I saw.)

We were a bit surprised (quite pleasantly!) that this paper got into ICER. I love the paper, but it’s different from most ICER papers.

Amber is interested in the role that gestures play in teaching CS. She started this paper from a taxonomy of gestures seen in other STEM classes. She observed a CS classroom and used her observations to provide concrete examples of the gestures seen in other kinds of classes. This isn’t a report of empirical findings. This is a report of using a lens borrowed from another field to look at CS learning and teaching in a new way.

My favorite part of of this paper is when Amber points out what parts of CS gestures don’t really fit in the taxonomy. It’s one thing to point to lines of code – that’s relatively concrete. It’s another thing to “point” to reference data, e.g., when explaining a sort and you gesture at the two elements you’re comparing or swapping. What exactly/concretely are we pointing at? Arrays are neither horizontal nor vertical — that distinction doesn’t really exist in memory. Arrays have no physical representation, but we act (usually) as if they’re laid out horizontally in front of us. What assumptions are we making in order to use gestures in our teaching? And what if students don’t share in those assumptions?

August 10, 2018 at 7:00 am Leave a comment

MOOCs don’t serve to decrease income inequality

At this year’s NSF Broadening Participation in Computing PI meeting, I heard a great talk by Kevin Robinson that asked the question: Do MOOCs “raise all boats” but maintain or even increase income inequality, or do they help to reduce the economic divide?  It’s not the question whether poor students take MOOCs.  It’s whether it helps the poor more, or the rich more.

Kevin has made his slides available here. The work he described is presented in this article from Science.  I want to share the one slide that really blew me away.

The gray line is the average income for US citizens at various ages.  As you would expect, that number generally increases up until retirement.  The black line is the average income for students in Harvard and MIT’s MOOC participants.  The MOOC participants are not only richer, but as they get older, they diverge more.  These are highly-privileged people, the kind with many advantages.  MOOCs are mostly helping the rich.

May 1, 2017 at 7:00 am 7 comments

Why high-income students do better: It’s not the velocity but the acceleration

Low-income students and schools are getting better, according to this study.  They’re just getting better so much more slowly than the wealthy students and schools.  Both are getting better incrementally (both moving in the right direction), but each increment is bigger for the rich (acceleration favors the rich).

We heard something similar from Michael Lach last week.  The NSF CE21 program organized a workshop for all the CS10K efforts focused on teacher professional development.  It was led by Iris Weiss who runs one of the largest education research evaluation companies.  Michael was one of our invited speakers, on the issue of scaling.  Michael has been involved in Chicago Public Schools for years, and just recently from a stint at the Department of Education.  He told us about his efforts to improve reading, math, and science scores through a focus on teacher professional development.  It really worked, for both the K-8 and high school levels.  Both high-SES (socioeconomic status) and low-SES students improved compared to control groups.  But the gap didn’t get smaller.

Despite public policy and institutional efforts such as need-blind financial aid and no-loan policies designed to attract and enroll more low-income students, such students are still more likely to wind up at a community college or noncompetitive four-year institution than at an elite university, whether a member of the Ivy League or a state flagship.The study, “Running in Place: Low-Income Students and the Dynamics of Higher Education Stratification,” will be published next month in Educational Evaluation and Policy Analysis, but an abstract is already available on the journal’s website.“I think [selective colleges] very much want to bring in students who are low-income, for the most part,” said Michael N. Bastedo, the study’s lead author and an associate professor of higher education at the University of Michigan. “The problem is, over time, the distance between academic credentials for wealthy students and low-income students is getting longer and longer…. They’re no longer seen as competitive, and that’s despite the fact that low-income students are rising in their own academic achievement.”

via News: Running in Place – Inside Higher Ed.

May 28, 2012 at 9:30 am 3 comments


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