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Google study on the challenges for rural communities in teaching CS

ComputingEd - Mon, 09/04/2017 - 07:00

Google continues their series of reports on the challenges of teaching CS, with a new report on rural and small-town communities in the US.  This is an important part of CS for All, and is a problem internationally.  The Roehampton Report found that rural English schools were less likely to have computing education than urban schools.  How do we avoid creating a computing education divide between urban and rural schools?

This special brief from our Google-Gallup study dives into the opportunities and challenges for rural and small-town communities. Based on nationally representative surveys from 2015-16, we found:

  • Students from rural/small-town schools are just as likely as other students to see CS as important for their future careers, including 86% who believe they may have a job needing computer science.

  • Rural/small-town parents and principals also highly value CS, with 83% of parents and 64% of principals saying that offering CS is just as or more important than required courses.

  • Rural/small-town students are less likely to have access to CS classes and clubs at school compared to suburban students, and their parents are less likely to know of CS opportunities outside of school.

  • Rural/small-town principals are less likely to prioritize CS, compared to large-city or suburban principals.

Source: Google for Education: Computer Science Research


Tagged: #CS4All, #CSforAll, computing for all, computing for everyone, Google

The Role of Emotion in Computing Education, and Computing Education in Primary School: ICER 2017 Recap

ComputingEd - Fri, 09/01/2017 - 07:00

I wrote my Blog@CACM post in August about the two ICER 2017 paper awards:

  • Danielsiek et al’s development of a new test of student self-efficacy in algorithms classes;
  • Rich et al.’s trajectories of K-5 CS learning, which constitute an important new set of theories about how young students learn computing.

Rich et al.’s paper is particularly significant to me because it has me re-thinking my beliefs about elementary school computer science. I have expressed significant doubt about teaching computer science in early primary grades — it’s expensive, there are even more teachers to prepare than in secondary schools, and it’s not clear that it does any longterm good. If a third grader learns something about Scratch, will they have learned something that they can use later in high school? Katie Rich presented not just trajectories but Big Ideas. Like Big Ideas for sequential programming include precision and ordering. It’s certainly plausible that a third grader who learns that precision and ordering in programs matters, might still remember that years later. I can believe that Big Ideas might transfer (at least, within computing) over years.

I was struck by a recurring theme of emotion in the papers at ICER 2017. We have certainly had years where cognition has been a critical discussion, or objects, or programming languages, or student’s process. This year, I noticed that many of these papers were thinking about beliefs and feelings.

I find this set of papers interesting for highlight an important research question: What’s the most significant issue influencing student success or withdrawal from computer science? Is it the programming language they use (blocks vs text, anyone?), the kind of error messages they see, the context in which the instruction is situated, or whether they use pair programming? Or is the most significant issue what the students believe about what they’re doing? And maybe all of those other issues (from blocks to pairs) are really just inputs to the function of student belief?

(Be sure to check out Andy Ko’s summary of ICER 2017.)


Tagged: affect, beliefs, computing education research, K12

New view of dispersants used after Deepwater Horizon oil spill

News From NSF - Mon, 08/28/2017 - 15:00

New research has uncovered an added dimension to the decision to inject large amounts of chemical dispersants above the crippled seafloor oil well during the Deepwater Horizon disaster in 2010.

The dispersants, scientists have found, may have significantly reduced the amount of harmful gases in the air at the sea surface -- diminishing health risks for emergency responders and enabling them to keep working to stop the spill and clean it up sooner.

The results were published ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=242909&WT.mc_id=USNSF_51&WT.mc_ev=click


This is an NSF News item.

The Problems with Coding Bootcamps: Allure with little Payoff

ComputingEd - Mon, 08/28/2017 - 07:00

Audrey Watters weighs in below on why Coding Bootcamps are failing. She argues that bootcamps aren’t filling a real need, that there really isn’t a huge untapped need for coding skills.

Kyle Thayler and Andy Ko just published an article at ICER 2017 about their analyses of bootcamps.  Kyle has a nice summary as a Medium post (see link here), but I recommend reading the actual ICER paper, too.  Kyle’s summary is balanced about the strengths and weaknesses of coding bootcamps, while I think the results in the ICER paper are much more critical.  This one quote, about the nine months (!) following graduation, was particularly compelling for me, “I preŠtty much devoted my time to [my bootcamp’s] prescribed job hunting methods, which means €financially, I have no money. [. . . ] And that [sacrifice] reflects on my family because now we’re low on funds [. . . ] and now instead of selling our house and buying a house, we’re selling our house to pay the debt that we’re in and then go rent until I can €find a job.”

Kyle’s visualization of the paths of his 26 interviewees is rich with detail, but can be confusing.  Here’s a slice of three of them.

What I didn’t get at first is that the gray area to the right is planned (or even imagined).  So P18, above, has already had one partial bootcamp (half-moon), one complete bootcamp, and still doesn’t have the desired job (the star in the upper right hand corner).  Of his 26 interviewees, only three have their desired job in the software industry.  Several have less than desirable jobs (including one that has an unrelated job and gave up). Nine of the 26 had already dropped out of a bootcamp.

When I read Kyle and Andy’s study about the struggle and pain that the bootcamp attendees go through, including difficulties finding jobs beyond what was expected, and then read Audrey’s piece suggesting that there might not be as many jobs available as people think, I wonder what is the allure of bootcamps.  Why go through all of that when there isn’t a guaranteed (or even likely?) payoff?

Within the past week, two well-known and well-established coding bootcamps have announced they’ll be closing their doors: Dev Bootcamp, owned by Kaplan Inc., and The Iron Yard, owned by the Apollo Education Group (parent company of the University of Phoenix). Two closures might not make a trend… yet. But some industry observers have suggested we might see more “consolidation” in the coming months.

It appears that there are simply more coding bootcamps – almost 100 across the US and Canada – than there are students looking to learn to code. (That is to say, there are more coding bootcamps than there are people looking to pay, on average, $11,000 for 12 weeks of intensive training in a programming language or framework).

All this runs counter, of course, to the pervasive belief in a “skills gap” – that there aren’t enough qualified programmers to fill all the programming jobs out there, and that as such, folks looking for work should jump at the chance to pay for tuition at a bootcamp. Code.org and other industry groups have suggested that there are currently some 500,000 unfilled computing jobs, for example. But that number is more invention than reality, a statistic used to further a particular narrative about the failure of schools to offer adequate technical training. That 500,000 figure, incidentally, comes from a Bureau of Labor Statistics projection about the number of computing and IT jobs that will added to the US economy by 2024, not the number of jobs that are available – filled or unfilled – today.

Perhaps instead of “everyone should learn to code,” we should push for everyone to learn how to read the BLS jobs report.

There isn’t really much evidence of a “skills gap” – there’s been no substantive growth in wages, for example, that one would expect if there was a shortage in the supply of qualified workers.

Source: Why Are Coding Bootcamps Going Out of Business?


Tagged: bootcamps, coding for all, coding for everyone, jobs

Unlocking the Potential of Learning Analytics in Computing Education

ACM TOCE and InRoads - Sun, 08/27/2017 - 20:00
Shuchi Grover, Ari Korhonen

Categories: Education

Blending Measures of Programming and Social Behavior into Predictive Models of Student Achievement in Early Computing Courses

ACM TOCE and InRoads - Sun, 08/27/2017 - 20:00
Adam S. Carter, Christopher D. Hundhausen, Olusola Adesope

Analyzing the process data of students as they complete programming assignments has the potential to provide computing educators with insights into both their students and the processes by which they learn to program. In prior research, we explored the relationship between (a) students’ programming behaviors and course outcomes, and (b) students’ participation within an online social learning environment and course outcomes. In both studies, we developed statistical measures derived from our data that significantly correlate with students’ course grades. Encouraged both by social theories of learning and a desire to improve the accuracy of our statistical models, we explore here the impact of incorporating our predictive measure derived from social behavior into three separate predictive measures derived from programming behaviors.
Categories: Education

A Contingency Table Derived Method for Analyzing Course Data

ACM TOCE and InRoads - Sun, 08/27/2017 - 20:00
Alireza Ahadi, Arto Hellas, Raymond Lister

We describe a method for analyzing student data from online programming exercises. Our approach uses contingency tables that combine whether or not a student answered an online exercise correctly with the number of attempts that the student made on that exercise. We use this method to explore the relationship between student performance on online exercises done during semester with subsequent performance on questions in a paper-based exam at the end of semester. We found that it is useful to include data about the number of attempts a student makes on an online exercise.
Categories: Education

A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments

ACM TOCE and InRoads - Sun, 08/27/2017 - 20:00
Shuchi Grover, Satabdi Basu, Marie Bienkowski, Michael Eagle, Nicholas Diana, John Stamper

Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills.
Categories: Education

Youth Computational Participation in the Wild: Understanding Experience and Equity in Participating and Programming in the Online Scratch Community

ACM TOCE and InRoads - Sun, 08/27/2017 - 20:00
Deborah A. Fields, Yasmin B. Kafai, Michael T. Giang

Most research in primary and secondary computing education has focused on understanding learners within formal classroom communities, leaving aside the growing number of promising informal online programming communities where young users contribute, comment, and collaborate on programs to facilitate learning. In this article, we examined trends in computational participation in Scratch, an online community with over 1 million registered youth designers. Drawing on a random sample of 5,004 youth programmers and their activities over 3 months in early 2012, we examined programming concepts used in projects in relation to level of participation, gender, and length of membership of Scratch programmers. Latent class analysis results identified the same four groups of programmers in each month based on the usage of different programming concepts and showed how membership in these groups shifted in different ways across time.
Categories: Education

Blending Measures of Programming and Social Behavior into Predictive Models of Student Achievement in Early Computing Courses

ACM Transactions on Computing Education - Sun, 08/27/2017 - 20:00
Adam S. Carter, Christopher D. Hundhausen, Olusola Adesope

Analyzing the process data of students as they complete programming assignments has the potential to provide computing educators with insights into both their students and the processes by which they learn to program. In prior research, we explored the relationship between (a) students’ programming behaviors and course outcomes, and (b) students’ participation within an online social learning environment and course outcomes. In both studies, we developed statistical measures derived from our data that significantly correlate with students’ course grades. Encouraged both by social theories of learning and a desire to improve the accuracy of our statistical models, we explore here the impact of incorporating our predictive measure derived from social behavior into three separate predictive measures derived from programming behaviors.

A Contingency Table Derived Method for Analyzing Course Data

ACM Transactions on Computing Education - Sun, 08/27/2017 - 20:00
Alireza Ahadi, Arto Hellas, Raymond Lister

We describe a method for analyzing student data from online programming exercises. Our approach uses contingency tables that combine whether or not a student answered an online exercise correctly with the number of attempts that the student made on that exercise. We use this method to explore the relationship between student performance on online exercises done during semester with subsequent performance on questions in a paper-based exam at the end of semester. We found that it is useful to include data about the number of attempts a student makes on an online exercise.

A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments

ACM Transactions on Computing Education - Sun, 08/27/2017 - 20:00
Shuchi Grover, Satabdi Basu, Marie Bienkowski, Michael Eagle, Nicholas Diana, John Stamper

Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills.

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