Posts tagged ‘computing education research’

Scientists Looking at Programmers’ Brains see more Language than Mathematics: The Neuroscience of Programming

I’m not convinced that our ability to image brains is actually telling us much about cognition yet.  I did find this result surprising, that our understanding of programming languages seems more linguistic than mathematical

Scientists are finding that there may be a deeper connection between programming languages and other languages then previously thought. Brain-imaging techniques, such as fMRI allow scientists to compare and contrast different cognitive tasks by analyzing differences in brain locations that are activated by the tasks. For people that are fluent in a second language, studies have shown distinct developmental differences in language processing regions of the brain. A new study provides new evidence that programmers are using language regions of the brain when understanding code and found little activation in other regions of the brain devoted to mathematical thinking.

Source: Scientists Begin Looking at Programmers’ Brains: The Neuroscience of Programming | Huffington Post

January 23, 2017 at 7:00 am 2 comments

Power law of practice in software implementation: Does this explain the “W” going away?

I wonder if this result explains why the second semester students in Briana’s studies (see previous blog post) didn’t have the “W” effect.  If you do enough code, you move down the power law of practice, and now you can attend to things like context and generating subgoal labels.

Different subjects start the experiment with different amounts of ability and past experience. Before starting, subjects took a multiple choice test of their knowledge. If we take the results of this test as a proxy for the ability/knowledge at the start of the experiment, then the power law equation becomes (a similar modification can be made to the exponential equation):

eqn

That is, the test score is treated as equivalent to performing some number of rounds of implementation). A power law is a better fit than exponential to this data (code+data); the fit captures the general shape, but misses lots of what look like important details.

Source: The Shape of Code » Power law of practice in software implementation

January 18, 2017 at 7:26 am 2 comments

Computer Science added to US Dept of Ed Civil Rights Data Collection

From Ruthe Farmer in White House OSTP.  It’s great that we’re going to get more data about CS Education in the United States.  Should it be at the federal level, when decisions about K-12 in the US are at the state level?  I’d like to get data collected at a level that impacts decision-making. How do we get states to track CS education?  Will the federal government’s effort be a prompt to get the states to track who takes CS classes, where they’re offered, and where they’re not?

Computer science has been added to the proposed 2017-18 Dept of Ed Civil Rights Data Collection.  The proposed new collection instruments are open for public comment through 2/28/17.

You can view the documents here:
https://www.regulations.gov/document?D=ED-2016-ICCD-0147-0003
(you will find the proposed data collection instruments on pages 29-31 of the doc titled A-2_CRDC_Data_Groups_12_23_16)

You can add comments here:
https://www.regulations.gov/docket?D=ED-2016-ICCD-0147

Comments from the public are critical to inclusion of this new data request, as the overall push is to lessen the reporting load for schools.  However, we felt it was necessary to add computer science as a separately tracked subject to obtain a better picture of total enrollment nationally.

Please share this opportunity to comment with your networks.

Regards,

Ruthe A. Farmer  |  Senior Policy Advisor for Tech Inclusion
Office of Science & Technology Policy
Executive Office of the President

 

January 9, 2017 at 7:08 am Leave a comment

Balancing cognition and motivation in computing education: Herbert Simon and evidence-based education

Education is a balancing act between optimally efficient instruction and motivating students. It’s not the same thing to meet the needs of the head and of the heart.

Shuchi Grover tweeted this interesting piece (quoted below) that reviews an article by Herb Simon (and John Anderson and Lynne Reder) which I hadn’t previously heard of.  The reviewer sees Herb Simon as taking a stand against discovery-based, situated, and constructivist learning, and in favor of direct instruction. When I read the article, I saw a more subtle message.  I do recommend reading the review piece linked below.

He [Herbert Simon] rejects discovery learning, and praises teacher instruction

When, for whatever reason, students cannot construct the knowledge for themselves, they need some instruction. The argument that knowledge must be constructed is very similar to the earlier arguments that discovery learning is superior to direct instruction. In point of fact, there is very little positive evidence for discovery learning and it is often inferior (e.g., Charney, Reder & Kusbit, 1990). Discovery learning, even when successful in acquiring the desired construct, may take a great deal of valuable time that could have been spent practicing this construct if it had been instructed. Because most of the learning in discovery learning only takes place after the construct has been found, when the search is lengthy or unsuccessful, motivation commonly flags.

Source: Herbert Simon and evidence-based education | The Wing to Heaven

Some cognitive scientists have been railing against the constructivist and situated approaches to learning for years. Probably the most important paper representing the cognitivist perspective is the Kirschner, Sweller, and Clark paper, “Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching.”  I talked about the Kirschner, Sweller, and Clark paper in this blog post with its implication for how we teach computer science.

The conclusion is pretty straightforward: Direct instruction is far more efficient than making the students work it out for themselves. Students struggling to figure something out for themselves does not lead to deeper learning or more transfer than simply telling students what they ought to do. Drill and practice is important. Learning in authentic, complex situations is unnecessary and often undesirable because failure increases with complexity.

The Anderson, Reder, and Simon article does something important that the famous Kirschner, Sweller, and Clark paper doesn’t — it talks about motivation. The words “motivation” and “interests” don’t appear anywhere in the Kirschner, Sweller, and Clark paper. Important attitudes about learning (like Carol Dweck’s fixed and growth mindsets, or Angela Duckworth’s grit) are not even considered.

In contrast, Anderson, Reder, and Simon understand that motivation is a critical part of learning.

Motivational questions lie outside our present discussion, but are at least as complex as the cognitive issues. In particular, there is no simple relation between level of motivation, on the one hand, and the complexity or realism of the context in which the learning takes place, on the other. To cite a simple example, learning by doing in the real-life domain of application is sometimes claimed to be the optimum procedure. Certainly, this is not true, when the tasks are life-threatening for novices (e.g., firefighting), when relevant learning opportunities are infrequent and unpredictable (e.g., learning to fly a plane in bad weather), or when the novice suffers social embarrassment from using inadequate skills in a real-life context (e.g., using a foreign language at a low level of skill). The interaction of motivation with cognition has been described in information-processing terms by Simon (1967, 1994). But an adequate discussion of these issues would call for a separate paper as long as this one.

There are, of course, reasons sometimes to practice skills in their complex setting. Some of the reasons are motivational and some reflect the special skills that are unique to the complex situation. The student who wishes to play violin in an orchestra would have a hard time making progress if all practice were attempted in the orchestra context. On the other hand, if the student never practiced as a member of an orchestra, critical skills unique to the orchestra would not be acquired. The same arguments can be made in the sports context, and motivational arguments can also be made for complex practice in both contexts. A child may not see the point of isolated exercises, but will when they are embedded in the real-world task. Children are motivated to practice sports skills because of the prospect of playing in full-scale games. However, they often spend much more time practicing component skills than full-scale games. It seems important both to motivation and to learning to practice one’s skills from time to time in full context, but this is not a reason to make this the principal mechanism of learning.

As a constructionist-oriented learning scientist, I’d go further with the benefits of a motivating context (which is a subset of what they’re calling a “complex setting”). When you “figure it out for yourself,” you have a different relationship to the domain. You learn about process, as well as content, as in learning what it means to be a scientist or how a programmer thinks. When you are engaged in the context, practice is no longer onerous but an important part of developing expertise — still arduous, but with meaning. Yasmin Kafai and Quinn Burke talk about changing students’ relationship with technology. Computer science shouldn’t just be about learning knowledge, but developing a new sense of empowerment with technology.

I’ve been wondering about what (I think) is an open research question about cognitivist vs. situationist approaches on lifelong learning. I bet you’re more likely to continue learning in a domain when you are a motivated and engaged learner. An efficiently taught but unmotivated learner is less likely to continue learning in the discipline, I conjecture.

While they underestimate the motivational aspect of learning, Anderson, Reder, and Simon are right about the weaknesses of an authentic context. We can’t just throw students into complex situations. Many students will fail, and those that succeed won’t be learning any better. They will learn slower.

Anderson, Reder, and Simon spend much of their paper critiquing Lave & Wenger’s Situated Learning. I draw on situated learning in my work (e.g., see post here) and reference it frequently in my book on Learner-Centered Computing Education, but I agree with their critique. Lave & Wenger are insightful about the motivation part, but miss on the cognitive part. Situated learning, in particular, provides insight into how learning is a process of developing identity. Lave & Wenger value apprenticeship as an educational method too highly. Apprenticeship has lots of weaknesses: inefficient, inequitable, and difficulty to scale.

The motivational component of learning is particularly critical in computing education. Most of our hot issues are issues of motivation:

The challenge to being an effective computing educator is to be authentic and complex enough to maintain motivation, and to use scaffolding to support student success and make learning more efficient. That’s the point of Phyllis Blumenfeld et al.’s “Motivating Project-Based Learning: Sustaining the Doing, Supporting the Learning.” (I’m in the “et al,” and it’s the most cited paper I’ve ever been part of.) Project-based learning is complex and authentic, but has the weaknesses that the cognitivists describe. Blumenfeld et al. suggest using technology to help students sustain their motivation and support their learning.

Good teaching is not just a matter of choosing the most efficient forms of learning. It’s also about motivating students to persevere, to tell them the benefits that make the efforts worthwhile. It’s about feeding the heart in order to feed the head.

January 6, 2017 at 7:00 am 6 comments

LaTiCE 2017 in Hong Kong

LaTiCE was announced to be in Saudi Arabia (see previous blog post), but it didn’t work out.  I don’t know why. It will now be held in Hong Kong.
FIRST ANNOUNCEMENT AND CALL FOR PAPERS

Learning and Teaching in Computing and Engineering (LaTiCE 2017)
April 20-23, 2017
Hong Kong
http://www.latice-conference.org/

The Fifth International Conference on Learning and Teaching in Computing and Engineering (LaTiCE 2017) aims to create a platform towards sharing rigorous research and current practices being conducted in computing and engineering education. The previous four LaTiCE conferences have been successfully held in Macau (2013), Malaysia (2014), Taiwan (2015) and Mumbai (2016). The fifth LaTiCE conference will be held at the University of Hong Kong, from April 20th to 23rd, 2017.

LaTiCE 2017 is jointly organized by the University of Hong Kong, Hong Kong, and the Uppsala Computing Education Research Group (UpCERG), Uppsala University, Sweden. It is technically co-sponsored by the Special Technical Community for Education Research (STC Education), which is an IEEE Computer Society initiative to connect those interested in all forms of educational research and pedagogy in the field of computing and engineering.

The conference is preceded by a doctoral consortium on April 20th. The conference is a gathering for presentations of research papers, practice sharing papers, work-in-process papers, and display of posters and demos.

MAIN CONFERENCE THEMES
– Computer Science and Engineering Education research
– Secondary School Computer Science
– ICT in Education

CONFERENCE SUB-THEMES
– Computing and engineering education research, theories, and methodologies
– Cross-cultural aspects of computing and engineering education
– Educational technology, software, and tools
– Teaching innovations, best practices, experience sharing in computing and engineering education
– Course module design, proficiency assessment, and module cross-accreditation
– Improving student engagement in computing and engineering
– Collaborative learning in computing and engineering- team and project skills
– “Flipped” classrooms and active learning
– Work-integrated learning and project-based learning

PAPER SUBMISSION
Research Papers
Research papers (6-8 pages) present original, unpublished work relevant to the conference themes. Papers may be theoretical or based on empirical investigations. Papers are evaluated with respect to their theoretical contribution and the quality and relevance of the research.

Practice / Work-in-progress Papers
Practice / work-in-progress papers (3-5 pages) present original, unpublished practice sharing or work-in-progress work with a focus on innovative and valued practices within specific institutions. They can present preliminary results or raise issues of significance to the discipline.

Poster/Demo
Poster/demo (2 pages abstract) should present innovative ideas for work in the early stages related to research, teaching practice, or tools. Demonstration of tools should stress the methodology and can include some hands-on work for participants.

Papers should be submitted to the EasyChair review management system. All papers will undergo double-blind peer review.
Conference content will be submitted for inclusion into IEEE Xplore as well as other Abstracting and Indexing (A&I) databases. All papers should follow the IEEE Xplore Conference Publishing formatting guidelines.

IMPORTANT DATES
Paper submission: January 15th, 2017
Notification of acceptance: February 15th, 2017
Camera-ready deadline: March 1st
Author registration deadline: March 1st
Doctoral consortium: April 20th
LaTiCE conference: April 21st-23rd

PROGRAMME COMMITTEE CO-CHAIRS
Roger Hadgraft, University of Technology, Sydney, Australia, Roger.Hadgraft@uts.edu.au
James Harland, RMIT University, Australia, james.harland@rmit.edu.au

January 4, 2017 at 7:21 am Leave a comment

Why the Software Industry Needs Computing Education Research

Interesting argument from Andy Ko and Susanne Hambrusch about why we need more computing education research.

To fill the available jobs with skilled software developers, learners need to actually be learning. Unfortunately, recent research shows that many students simply aren’t. For example, a 2004 study conducted across seven countries and 12 universities found that even after passing college-level introductory programming courses, the majority of students could not predict the output of even basic computer programs. In some of our research on coding bootcamps, we are seeing similar trends, with students failing to learn and failing to get jobs.

If learning outcomes are as bad as these studies show, we need to be deeply concerned. Existing and new programs may be training tens of thousands of new software developers who aren’t quite good enough to get even an entry level position. This leaves the status quo of top companies fighting over top coders, leaving many jobs unfilled while they wait for more skilled developers. Worse yet, the demand for developers may be so high that they do get jobs, but write poor-quality code, putting at risk the software-based infrastructure that society increasingly needs to be robust, secure, and usable.

Source: Why the Software Industry Needs Computing Education Research | The Huffington Post

January 2, 2017 at 7:26 am 9 comments

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