SIGCSE 2016 Preview: Parsons Problems and Subgoal Labeling, and Improving Female Pass Rates on the AP CS exam
February 29, 2016 at 7:56 am 9 comments
Our research group has two papers at this year’s SIGCSE Technical Symposium.
Subgoals help students solve Parsons Problems by Briana Morrison, Lauren Margulieux, Barbara Ericson, and Mark Guzdial. (Thursday 10:45-12, MCCC: L5-L6)
This is a continuation of our subgoal labeling work, which includes Lauren’s original work showing how subgoal labels improved learning, retention and transfer in learning App Inventor (see summary here), the 2015 ICER Chairs Paper Award-winning paper from Briana and Lauren showing that subgoals work for text languages (see this post for summary), and Briana’s recent dissertation proposal where she explores the cognitive load implications for learning programming (see this post for summary). This latest paper shows that subgoal labels improve success at Parson’s Problems, too. One of the fascinating results in this paper is that Parson’s Problems are more sensitive as a learning assessment than asking students to write programs.
Sisters Rise Up 4 CS: Helping Female Students Pass the Advanced Placement Computer Science A Exam by Barbara Ericson, Miranda Parker, and Shelly Engelman. (Friday 10:45-12, MCCC: L2-L3)
Barb has been developing Project Rise Up 4 CS to support African-American students in succeeding at the AP CS exam (see post here from RESPECT and this post here from last year’s SIGCSE). Sisters Rise Up 4 CS is a similar project targeting female students. These are populations that have lower pass rates than white or Asian males. These are examples of supporting equality and not equity. This paper introduces Sisters Rise Up 4 CS and contrasts it with Project Rise Up 4 CS. Barb has resources to support people who want to try these interventions, including a how-to ebook at http://ice-web.cc.gatech.edu/ce21/SRU4CS/index.html and an ebook for students to support preparation for the AP CS A.
Entry filed under: Uncategorized. Tags: BPC, cognitive load, computing education, computing for all, computing for everyone, ECEP, learning sciences, NCWIT, Parsons Problems.
1.
Optimizing Learning with Subgoal Labeling: Lauren Margulieux Defends her Dissertation | Computing Education Blog | March 29, 2016 at 9:42 pm
[…] SIGCSE 2016 (see post here), Briana presented a paper with Lauren where they showed that subgoal labeling also improved […]
2.
Preview ICER 2016: Ebooks Design-Based Research and Replications in Assessment and Cognitive Load Studies | Computing Education Blog | September 2, 2016 at 7:54 am
[…] claiming them. Readers of this blog may recall Briana and Lauren’s confusing results from SIGCSE 2016 result that suggest that cognitive load in CS textual programming is so high that it blows away our […]
3.
Graduating Dr. Briana Morrison: Posing New Puzzles for Computing Education Research | Computing Education Blog | December 16, 2016 at 7:00 am
[…] really significant result was showing that Parson’s Problems were a more sensitive measure of learning than asking students to write code…, and that subgoal labels make Parson’s Problems better, […]
4.
Embedding and Tailoring Engineering Learning: A Vision for the Future of Engineering Education | Computing Education Blog | March 15, 2017 at 6:01 am
[…] last part is much of what drives my work these days. We’re learning a lot about how great Parsons Problems are for learning CS. Very few CS classes use them. There are reasons why they don’t (e.g., they’re […]
5.
Parsons Problems have same Learning Gains as Writing or Fixing code, in less time: Koli Calling 2017 Preview | Computing Education Blog | November 17, 2017 at 7:00 am
[…] The basic design of her experiment is pretty simple. Everybody gets a pretest where they answer multiple-choiced questions, write some code, fix some code, and solve some Parsons problems. (I’ve written about Parsons Problems here before.) […]
6.
A Generator for Parsons problems on LaTeX exams and quizzes | Computing Education Research Blog | June 8, 2018 at 2:00 am
[…] guide, and on the final exam. It’s a good fit for the problem. We know that Parsons problems are a more sensitive measure of learning than code writing problems, they’re just as effective as code writing or code fixing problems for learning (so good for a […]
7.
What do I mean by Computing Education Research? The Computer Science Perspective | Computing Education Research Blog | November 12, 2018 at 8:01 am
[…] inefficient. Turns out that we can use worked examples with subgoal labeling and techniques like Parson’s problems and peer instruction to dramatically improve learning in less […]
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Proposal #1 to Change CS Education to Reduce Inequity: Teach computer science to advantage the students with less computing background | Computing Education Research Blog | July 20, 2020 at 7:00 am
[…] they are a more careful and finer-grained assessment tool (see this post). If you ask students with less ability to write a piece of code, you might get students who only […]
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Proposal #3 to Change CS Education to Reduce Inequity: Call a truce on academic misconduct cases for programming assignments | Computing Education Research Blog | July 30, 2020 at 7:00 am
[…] which are more sensitive measures of understanding about programming than writing programs (see blog post). We want students to program, and most of our students want to program. Our focus should be on […]