Archive for September, 2013

Education Research Questions around Live Coding: Vygotskian and Non-Constructionist

I posted my trip report on the Dagstuhl Seminar on Live Coding on Blog@CACM (see the post here).  If you don’t want to read the post, check out this video as a fun introduction to live coding:

I have a lot more that I want to think through and share about the seminar. I’m doing a series of blog posts this week on live coding to give me an opportunity to think through some of these issues.

IMG_0100

I saw four sets of computing education research questions in live coding. These are unusual research questions for me because they’re Vygotskian and non-Constructionist.

Live coding is about performance. It’s not an easy task. The live coder has to know their programming language (syntax and semantics) and music improvisation (e.g., including listening to your collaborator and composing to match), and use all that knowledge in real-time. It’s not going to be a task that we start students with, but it may be a task that watching inspires students. Some of my research questions are about what it means to watch the performance of someone else, as opposed to being about students constructing. I’ve written before about the value of lectures, and I really do believe that students can learn from lectures. But not all students learn from lectures, and lectures work only if well-structured. Watching a live coding performance is different — it’s about changing the audience’s affect and framing with respect to coding. Can we change attitudes via a performance?

Vygotsky argued that all personal learning is first experienced at a social level. Whatever we learn must first be experienced as an interaction with others. In computing education, we think a lot about students’ first experience programming, but we don’t think much about how a student first sees code and first sees programming. How can you even consider studying a domain whose main activity you have never even seen? What is the role of that coding generating music, with cultural and creative overtones? The social experience introducing computing is important, and that may be something that live code can offer.

IMG_0073

Here are four sets of research questions that I see:

  1. Making visible. In a world with lots of technology, code and programmers are mostly invisible. What does it mean for an audience to see code to generate music and programming as a live coder? It’s interesting to think about this impact for students (does it help students to think seriously about computing as something to explore in school?) and for a more general audience (how does it change adults’ experience with technology?).
  2. Separating program and process. Live coding makes clear the difference between the program and the executing process. On the first day, we saw performances from Alex MacLean and Thor Magnusson, and an amazing duet between Andrew Sorensen at Dagstuhl and Ben Swift at the VL/HCC conference in San Jose using their Extempore system. These performances highlighted the difference between program and process. The live coders start an execution, and music starts playing in a loop. Meanwhile, they change the program, then re-evaluate the function, which changes the process and the music produced. There is a gap between the executing process and the text of the program, which is not something that students often see.
  3. Code for music. How does seeing code for making music change student’s perception of what code is for? We mostly introduce programming as engineering practice in CS class, but live coding is pretty much the opposite of software engineering. Our biggest challenges in CS Ed are about getting students and teachers to even consider computer science. Could live coding get teachers to see computing as something beyond dry and engineering-ish?  Who is attracted by live coding?  Could it attract a different audience than we do now?  Could we design the activity of live coding to be more attractive and accessible?
  4. Collaboration. Live coding is a collaborative practice, but very different from pair programming. Everybody codes, and everybody pays attention to what the others are doing. How does the collaboration in live coding (e.g., writing music based on other live coders’ music) change the perception of the asocial nature of programming?

I’ll end with an image that Sam Aaron showed in his talk at Dagstuhl, a note that he got from a student in his Sonic Pi class: “Thank you for making dull lifeless computers interesting and almost reality.” That captures well the potential of live coding in computing education research — that activity is interesting and the music is real.

IMG_0074

September 30, 2013 at 5:38 am 6 comments

What will programmers have to know by 2040?

Interesting claim below.  Do we believe that being able to build a JIT compiler will be a critical threshold for programming in 2040?  Or will programming become so much a literacy, that there will be people who can just write grocery lists and letters to Grandma and there will be Shakespeares?  I’m predicting a broader spread, not a higher bar.

The FizzBuzz problem described below is pretty interesting, a modern day version of the Rainfall problem.  I will bet that the results claimed for FizzBuzz are true, but I haven’t seen any actual studies of it yet.

While that may be true today, what will matter far more in the future is the quality of programmers, not the quantity. Any programmer who can’t hack together a JIT compiler in 2040 will be as useless as a programmer who can’t solve FizzBuzz today.

via Neil Fraser: News: Programming TNG.

September 27, 2013 at 1:38 am 5 comments

CS loses an advocate for CS Ed: Mary Jean Harrold 1947 — 2013

My colleague Mary Jean Harrold lost her battle with cancer last week.  Mary Jean worked hard for women in computing, and was always a strong supporter of efforts to improve and broaden computing education.  The classes I’m now teaching in TA preparation were originally proposed by Mary Jean and a committee she chaired — she thought it was important that we produce PhD’s who know something about teaching and communicating ideas.  She will be missed.

Harrold also was a fierce advocate for women and minorities in computing fields.  At Georgia Tech, she was the NSF ADVANCE Professor in the School of Computer Science for 10 years, from 2001 to 2011; she also was a member of the Leadership Team and Director of the Georgia Tech Hub for the National Center for Women and Information Technology (NCWIT).  Outside Georgia Tech, Harrold served many years (several as co-chair) on the CRA’s Committee on the Status of Women in Computing Research (CRA-W), whose goal is to increase the number of women in computer science research and education.  She was instrumental in establishing the biennial Software Engineering Educators’ Symposium (SEES), which aims to forge ties between faculty at minority-serving colleges and software engineering researchers.

via In Memoriam: Mary Jean Harrold 1947 — 2013 | News | Communications of the ACM.

September 26, 2013 at 1:36 am Leave a comment

Where are the black students in STEM in Georgia?

Perhaps the saddest table I’ve ever read in my local paper.  “Mathematics II” is Sophomore year (10th grade, 15 years old) in high school mathematics.  APS is Atlantic Public Schools.  I live in Dekalb county.  No wonder we can’t get more Black students into AP CS, if we can’t get past Sophomore year mathematics.

A_Georgia_Tech_researcher_asks__Where_are_the_black_students...___Get_Schooled___www.ajc.com

71 percent of the 2,500 black students in APS who took the Mathematics II exam in 2011, failed and only 1 percent, 25 students, passed with distinction (Pass Plus). By contrast, only 21 percent of white students failed with 79 percent passing and 23 percent of those passing with distinction.

via A Georgia Tech researcher asks: Where are the black students… | Get Schooled | www.ajc.com.

September 25, 2013 at 1:32 am 6 comments

PostDoc Best Practices: For Programs Supporting PostDocs in Computer Science

Post-docs are becoming more common in computer science.  A new effort is aiming to help the community learn how to support these post-docs.

Developing new talent to carry out high impact research is of paramount importance to the Computer Science & Engineering research enterprise.  An appointment as a postdoctoral researcher is an increasingly common starting point for a research career.  The National Science Foundation (NSF) Computer & Information Science and Engineering (CISE) Directorate and the CCC recognize the critical importance in having an excellent postdoc training experience to help junior researchers advance their careers.

With NSF’s backing, the CCC is announcing a program to develop, implement and institutionalize the implementation of best practices for supporting postdocs. This program will award grants to institutions or consortia of institutions to implement best practices for strengthening the postdoc experience in computer science and computing-related fields.  These supporting programs will enable PhD graduates to transition effectively to research roles in a variety of sectors.

via PostDoc Best Practices | The Website for Programs Supporting PostDocs in Computer Science.

September 24, 2013 at 1:52 am Leave a comment

Knowing more doesn’t necessarily lead to correct reasoning: Politics changes problem-solving

Thanks to Elizabeth Patitsas for this piece.  Fascinating experiment — people solve the exact same math problem differently if the context is “whether a skin cream works” or “whether gun control laws work,” depending on their politics.  The statement below is an interesting interpretation of the results and relates to my questions about whether computing education research actually leads to any change.

For study author Kahan, these results are a fairly strong refutation of what is called the “deficit model” in the field of science and technology studies—the idea that if people just had more knowledge, or more reasoning ability, then they would be better able to come to consensus with scientists and experts on issues like climate change, evolution, the safety of vaccines, and pretty much anything else involving science or data (for instance, whether concealed weapons bans work). Kahan’s data suggest the opposite—that political biases skew our reasoning abilities, and this problem seems to be worse for people with advanced capacities like scientific literacy and numeracy. “If the people who have the greatest capacities are the ones most prone to this, that’s reason to believe that the problem isn’t some kind of deficit in comprehension,” Kahan explained in an interview.

via Science Confirms: Politics Wrecks Your Ability to Do Math | Mother Jones.

September 23, 2013 at 1:13 am 2 comments

Lessons Learned From First Year College MOOCs at Georgia Tech (and SJSU)

Karen Head has finished her series on how well the freshman-composition course fared (quoted and linked below), published in The Chronicle. The stats were disappointing — only about 238 of the approximately 15K students who did the first homework finished the course. That’s even less than the ~10% we saw completing other MOOCs.

Georgia Tech also received funding from the Gates Foundation to trial a MOOC approach to a first year of college physics course.  I met with Mike Schatz last Friday to talk about his course.  The results were pretty similar: 20K students signed up, 3K students completed the first assignment, and only 170 finished.  Mike had an advantage that Karen didn’t — there are standardized tests for measuring the physics knowledge he was testing, and he used those tests pre-post.  Mike said the completers fell into three categories: those who came in with a lot of physics knowledge and who ended with relatively little gain, those who came in with very little knowledge and made almost no progress, and a group of students who really did learn alot.  They don’t know why nor the relative percentages yet.

The report from the San Jose State University MOOC experiment with a remedial mathematics course came out with the argument:

The researchers also say, perhaps unsurprisingly, that what mattered most was how hard students worked. “Measures of student effort trump all other variables tested for their relationships to student success,” they write, “including demographic descriptions of the students, course subject matter, and student use of support services.”

It’s not surprising, but it is relevant.  Students need to make effort to learn.  New college students, especially first generation college students (i.e., whose parents have never gone to college), may not know how much effort is needed.  Who will be most effective at communicating that message about effort and motivating that effort — a video of a professor, or an in-person professor who might even learn your name?

As Gary May, our Dean of Engineering, recently wrote in an op-ed essay published in Inside Higher Ed, “The prospect of MOOCs replacing the physical college campus for undergraduates is dubious at best. Other target audiences are likely better-suited for MOOCs.”

On the freshman-composition MOOC, Karen Head writes:

No, the course was not a success. Of course, the data are problematic: Many people have observed that MOOCs often have terrible retention rates, but is retention an accurate measure of success? We had 21,934 students enrolled, 14,771 of whom were active in the course. Our 26 lecture videos were viewed 95,631 times. Students submitted work for evaluation 2,942 times and completed 19,571 peer assessments (the means by which their writing was evaluated). However, only 238 students received a completion certificate—meaning that they completed all assignments and received satisfactory scores.

Our team is now investigating why so few students completed the course, but we have some hypotheses. For one thing, students who did not complete all three major assignments could not pass the course. Many struggled with technology, especially in the final assignment, in which they were asked to create a video presentation based on a personal philosophy or belief. Some students, for privacy and cultural reasons, chose not to complete that assignment, even when we changed the guidelines to require only an audio presentation with visual elements. There were other students who joined the course after the second week; we cautioned them that they would not be able to pass it because there was no mechanism for doing peer review after an assignment’s due date had passed.

via Lessons Learned From a Freshman-Composition MOOC – Wired Campus – The Chronicle of Higher Education.

September 21, 2013 at 1:29 am 14 comments

CS National Curriculum in England Released

The finalized form of the English national curriculum for CS was just released last week.  Worth comparing to CS:Principles, ExploringCS, and the new CS2013 Computer Science curriculum recommendations.

These are the statutory programmes of study and attainment targets for computing at key stages 1 to 4. They should be taught in England from September 2014.

via National curriculum in England: computing programmes of study – Publications – GOV.UK.

September 20, 2013 at 1:26 am Leave a comment

On Computing Education From a 14 year old’s Point of View: A role for livecoding

Articulate and interesting critique of the state of computing education.  This article is describing the UK, but the situations described are actually better than in most of the US (e.g., that everyone gets some computing education, and that everyone gets some Scratch, is light years ahead of the US where 80% have nothing at all).

The particular point quoted below is about the importance of teaching students enough that they can take pride in the result, and that they can see a path to do more.  I’m writing this while immersed in the Livecoding seminar at Dagstuhl, and I realize that this is a role for livecoding — showing students that they can make something realimmediately and quickly change it to make something new.

Again, we have the Windows Movie Maker problem. If a student cannot take pride in the work they produce, how can you expect them to take an interest in the subject?

From a student’s perspective, if it has taken four years to learn how to produce a program to add two numbers together, the gap to becoming a software developer creating useful applications looks enormous.

via On Computing Education – The Windows Movie Maker Problem – Ross Penman – Ross Penman.

September 19, 2013 at 1:44 pm 8 comments

The Brogrammer Effect: Women Are a Small (and Shrinking) Share of Computer Workers – Jordan Weissmann – The Atlantic

Good to see The Atlantic caring about this.  I don’t see much evidence offered that it’s a “Brogrammer” effect, though, other than the title.

So here’s why everybody, whether or not they’ve ever given a hint of thought to brogrammers and the social mores of Silicon Valley or Alley or Beach, should care. A large part of the pay gap between men and women boils down to the different careers they pursue. And STEM jobs, with their generally high salaries, are an especially important factor. Meanwhile, as the Census notes, computer fields make up about a half of STEM employment. So when you talk about women retreating from computer work, you’re talking about a defeat for their financial equality.

via The Brogrammer Effect: Women Are a Small (and Shrinking) Share of Computer Workers – Jordan Weissmann – The Atlantic.

September 19, 2013 at 1:24 am 2 comments

September 2013 Special Issue of IEEE Computer on Computing Education

Betsy DiSalvo and I were guest editors for the September 2013 special issue of IEEE Computer on Computing Education.  (The cover, copied above, is really nice!)  The five articles in the issue did a great job of pushing computing education beyond our traditional image of CS education.  Below I’m pasting our original introduction to the special issue — before copy-editing, but free for me to share, and it’s a reasonable overview of the issue.

Introduction to the Special Issue

Computing education is in the news regularly these days. England has just adopted a new computer science curriculum. Thousands of people are taking on-line courses in computer science. Code.org’s viral video had millions of people thinking about learning to code.

A common thread in all of this new computer science education is that it’s not how we normally think about computing education. Traditional computing education brings to mind undergraduates working late night in labs drinking highly-caffeinated beverages. “CS Class” brings to mind images of students gaining valuable vocational skills in classrooms. The new movement towards computing education is about computing education for everyone, from children to working adults. It’s about people learning about computing in places you wouldn’t expect, from your local elementary school to afterschool clubs. It’s about people making their own computing on things that only a few years ago were not computable at all, like your personal cellphone and even your clothing.

Computing has changed. In the 1950’s and 1960’s, computing moved from the laboratory into the business office. In the PC revolution, it moved into our homes. Now in the early 21st Century, it is ubiquitous. We use dozens of computers in our everyday life, often without even recognizing that the processors are there. Knowing about computing today is necessary for understanding the world we live in. Computer science is as valuable as biology, physics, or chemistry to our students. Consider a computer science concept: that all digitized information is represented in a computer, and the same information could be a picture or text or a virus. That is more relevant to a student today than the difference between meiosis and mitosis, or how to balance an equilibrium equation.

Computing also gives us the most powerful tool for creative expression humans have ever invented. The desktop user interface we use today was created at Xerox PARC in order to make the computer a creative device. Today, we can use computing to communicate, to inform, to delight, and to amaze. That is a powerful set of reasons for learning to control the computer with programming.

The papers in this special issue highlight how computing education has moved beyond the classroom. They highlight computing as porous education that crosses the boundaries of the classroom, and even boundaries of disciplines. These papers help us to understand the implications and the new needs of computing education today.

Maria Knobelsdorf and Jan Vahrenhold write on “Addressing the Full Range of Students: Challenges in K-12 Computer Science Education”. The issues change as computer science education moves down from higher education into primary and secondary education. What curricula should we use in schools? How do prepare enough teachers? Maria and Jan lay out the challenges, and use examples from Germany on how these challenges might be addressed.

“STEAM-Powered Computing Education using E-Textiles: Impacting Learning and Broadening Participation” by Kylie Peppler talks about integrating art into traditional STEM (Science, Technology, Engineering, and Mathematics) classrooms through use of new kinds of media. Kylie has students sewing computers into fabrics. Her students combine roles of engineers, designers, scientists and artists as they explore issues of fashion and design with electronic circuits and computer programming.

In “The Porous Classroom: Professional practices in the computing curriculum”, Sally Fincher and Daniel Knox consider how computer science students learn beyond the classroom. Learning in the classroom is typically scripted with careful attention to students activities that lead to learning outcomes. The wild and unconstrained world outside the classroom offers many more opportunities to learn, and Sally and Daniel look at how the opportunities outside the school walls influence students as they move between the classroom and the world beyond.

Karen Brennan’s paper “Learning Computing through Creating and Connecting” starts from the programming language, Scratch, which was created to introduce computing into afterschool computer clubhouses. Students using Scratch learned through creating wonderful digital stories and animations, then sharing them with others, and further learning by mixing and re-mixing what was shared. Karen then considers the porous education from the opposite direction — what does it take to take an informal learning tool, such as Scratch, into the traditional classroom?

The paper by Allison Elliott Tew and Brian Dorn, “The Case for Validated Tools in Computing Education Research”, describes how to measure the impacts of computing education, in terms of learning and attitudes. This work ties these themes together and back to the traditional classroom. Wherever the learning is occurring, we want to know that there is learning happening.  We need good measurement tools to help us know what’s working and what’s not, and how to compare different kinds of contexts for different students. Allison and Brian tell us that “initial research and development investment can pay dividends for the community because validated instruments enable and enhance a host of activities in terms of both teaching and research that would not otherwise be feasible.”   Tools such as these validated instruments may allow us to measure the impact of informal, maker-based, or practice-based approaches.  Work in basic tools for measurement help us to ground and connect the work that goes on beyond our single classroom through the porous boundary to other disciplines and other contexts.

The story that this special issue tells is about computer science moving from subject to literacy. Students sometimes learn computer science because they are interested in computers. More often today, students learn computer science because of what they can do with computers. Computing is a form of expression and a tool for thinking. It is becoming a basic literacy, like reading, writing, and arithmetic. We use reading and writing in all subject areas. We see that students are increasingly using programming in the same way. The papers in this special issue offer a view into that new era of computing education.

September 18, 2013 at 1:54 pm Leave a comment

Eye Movements in Programming Education: Interesting new workshop

Eye Movements in Programming Education: Analyzing the expert’s gaze

Workshop at the 13th KOLI CALLING INTERNATIONAL CONFERENCE ON COMPUTING EDUCATION RESEARCH

Joensuu, Finland, November 13th – November 14th, 2013

Computer Science Education Research and Teaching mainly focus on writing code, while the reading skills are often taken for granted. Reading occurs in debugging, maintenance and the learning of programming languages. It provides the essential basis for comprehension. By analyzing behavioral data such as gaze during code reading processes, we explore this essential part of programming.

This first workshop gives participants an opportunity to get insights into code reading with eye movement data. However, as this data only reflects the low level behavioral processes, the challenge to tackle is how to make use of this data to infer higher order comprehension processes. We will take on this challenge by working on a coding scheme to analyze eye movement data of code reading. The links between low and high level behaviors will help computing science educators to design, realize and reflect on the teaching of code reading skills.

Furthermore, we aim to open discussion about the ways of explicit teaching of readership skills in computing education. Therefore we will discuss the role of reading skills in teaching programming, facilitated by position papers of each participant.

To participate send a mail to teresa.busjahn@fu-berlin.de. It is possible to participate independent of attending Koli Calling. Participants will get eye movement data of reading and comprehension processes of expert programmers, and a coding scheme for annotating the process. You will annotate the video, and reflect on the (perceived) intentions behind the visible pattern. Applying and refining the coding scheme on the data gives insight into the higher order comprehension strategies of the reader.

A short individual reflection and position paper of the results and perspectives for teaching programming is required by the participants [max. 2-3 pages]. As a result, participants will jointly prepare a paper with the data and the refined coding scheme.

via Eye Movements in Programming Education • Computer Science Education • Department of Mathematics and Computer Science.

September 18, 2013 at 1:58 am Leave a comment

Computer Science Education Week is Dec 8–14, 2013

The dates for CSEdWeek are good to know, but the “Hour of Code” from Code.org is an interesting new initiative.

What is Computer Science Education Week?

Computer Science Education Week (CSEdWeek) is the annual awareness program for computer science education. It is organized each year by the Computing in the Core coalition and Code.org. It is a call to action to raise awareness (particularly in the K-12 environment) about the importance of computer science education and its connection to careers in computing and other fields. CSEdWeek is held in recognition of the birthday of computing pioneer Admiral Grace Murray Hopper (December 9, 1906).

What is an Hour of Code?

It’s a 1 hour intro to computer science and programming, to give beginners a taste and to demystify “code”. For existing CS teachers, it can be anything you want – get creative. For everybody else, we’ll provide self-guided tutorials anybody can do, with just a web-browser or smartphone, or even unplugged, no experience needed. Note:  HTML does not count as an Hour of Code.

via Computer Science Education Week is Dec 8–14, 2013 | CSEd Week 2013.

September 17, 2013 at 1:56 am 5 comments

Doctoral Consortium at the Australasian Computing Education Conference

Great to see happening!  The SIGCSE Doctoral Consortium is associated with ICER, which was just in San Diego, and then will be in Glasgow, and then will be in Omaha, and then will be in Melbourne.  It’s good to have a DC for Australasian doctoral students before then.
This is a call for participation in the Doctoral Consortium (DC) (http://elena.aut.ac.nz/homepages/ace2014/doctoral-consortium.html)  for the 16th Australasian Computing Education Conference (ACE 2014), a conference on research and innovation in computing education in its various aspects, at all levels and in all contexts (http://elena.ait.ac.nz/homepages/ace2014/). The conference will be held in Auckland, New Zealand at the Auckland University of Technology in conjunction with Australian Computer Science Week (ACSW) (http://www.aut.ac.nz/study-at-aut/study-areas/computing–mathematical-sciences/beyond-the-classroom/acsw-2014). The Doctoral Consortium with be held on Monday January 20th 2014 (prior to ACE 2014).
The DC will provide an opportunity for a group of PhD students to discuss and explore their research interests and career objectives with a panel of established researchers in computing education research. The DC is sponsored by the Software Engineering Laboratory of Auckland University of Technology and the School of Computing and Mathematical Sciences.  The sponsorship covers full registration for the DC for up to 10 attendees, and for those wishing to stay on to attend the full set of ACSW conferences the $NZD165.00 sponsorship can be applied as partial payment of the full $NZD300.00 student registration fee.
The DC is open to students who are currently enrolled in any stage of doctoral studies with a focus on computing education research. The number of participants is limited to 10. Senior researchers in the field will provide feedback and suggestions for improvement of the students research. Each applicant should submit an application that includes the following information in one PDF file:
– Curriculum Vita
– Research summary, including motivation, background and literature to contextualize the research, research questions, methodologies used or planned, and any results obtained to date.
– Questions related to the research that the applicant would like to discuss and get feedback on at the doctoral consortium
The research summary should be 1-3 pages long, depending on the stage of the research. This summary will be made available to other participants of the doctoral consortium to allow them to provide feedback and prepare questions on the research.
Important dates and submission process
Applications due: 8th November 2013
Notification date: 25th November 2013
For further information contact Associate Professor Katrina Falkner (katrina.falkner@adelaide.edu.au)
Jacqueline Whalley and Daryl D’Souza
ACE 2014 Conference Co-chairs

September 16, 2013 at 1:52 am 1 comment

How much is too much time spent on testing in schools?

How_much_time_do_school_districts_spend_on_standardized_testing__This_much.

Exactly how much standardized testing are school districts subjecting students to these days? A nearly staggering amount, according to a new analysis.

“Testing More, Teaching Less: What America’s Obsession with Student Testing Costs in Money and Lost Instructional Time,” released by the American Federation of Teachers, looks closely at two unnamed medium-sized school districts — one in the Midwest and one in the East — through the prism of their standardized testing calendars.

via How much time do school districts spend on standardized testing? This much..

This article is worth blogging on for two reasons:

First, my colleagues in the UK were stunned when I told them that most tests that students take in US schools are locally invented.  “Doesn’t that lead to alot of wasted effort?”  Perhaps so — this report seems to support my claim.

Second, I don’t find that much testing either staggering nor undesirable.  Consider the results on the Testing Effect — students learn from testing.  20 hours in an academic year is not too much, if we think about testing as driving learning.  We don’t know if these are good or useful tests, or if they are being used in a way that might motivate more learning, so 20 hours isn’t obviously a good thing.  But it’s also not obviously a bad thing.

Consider the results of the paper presented by Michael Lee at ICER 2013 this year (and which won the “John Henry Award,” the people’s choice best paper award).  They took a video game that required programming (Gidget) and added to it explicit assessments — quizzes that popped up at the end of each level, to ask you questions about what you did.  They found that such assessments actually increased engagement and time-on-task.  Their participants (both control and experimental) were recruited from Amazon’s Mechanical Turk, so they were paid to complete more levels.  Adding assessments led to more levels completed and less time per level — that’s pretty remarkable.

Lee-Gidget-engagement-speed

Maybe what we need is not fewer tests, but better and more engaging tests.

 

September 13, 2013 at 1:57 am 4 comments

Older Posts


Enter your email address to follow this blog and receive notifications of new posts by email.

Join 9,005 other followers

Feeds

Recent Posts

Blog Stats

  • 1,880,400 hits
September 2013
M T W T F S S
 1
2345678
9101112131415
16171819202122
23242526272829
30  

CS Teaching Tips