How to Teach Computational Literacy/Thinking: Wolfram’s Language and Code.org’s Response

Stephen Wolfram has published an essay arguing for a programming language as key to teaching computational literacy. He says computational thinking — I think he means the same thing as I do with CL instead of CT. I agree with him, and made a similar argument in my book. He goes on to argue that Wolfram Language (and the Mathematica infrastructure behind it) is particularly good for this.

But how does one “tell a computer” anything? One has to have a language. And the great thing is that today with the Wolfram Language we’re in a position to communicate very directly with computers about things we think about. The Wolfram Language is knowledge based: it knows about things in the world—like cities, or species, or songs, or photos we take—and it knows how to compute with them. And as soon as we have an idea that we can formulate computationally, the point is that the language lets us express it, and then—thanks to 30 years of technology development—lets us as automatically as possible actually execute the idea. The Wolfram Language is a programming language. So when you write in it, you’re doing programming. But it’s a new kind of programming. It’s programming in which one’s as directly as possible expressing computational thinking—rather than just telling the computer step-by-step what low-level operations it should do. It’s programming where humans—including kids—provide the ideas, then it’s up to the computer and the Wolfram Language to handle the details of how they get executed. Programming—and programming education—have traditionally been about telling a computer at a low level what to do. But thanks to all the technology we’ve built in the Wolfram Language, one doesn’t have to do that any more. One can express things at a much higher level—so one can concentrate on computational thinking, not mere programming. Yes, there’s certainly a need for some number of software engineers in the world who can write low-level programs in languages like C++ or Java or JavaScript—and can handle the details of loops and declarations. But that number is tiny compared to the number of people who need to be able to think computationally.

Source: How to Teach Computational Thinking—Stephen Wolfram Blog

He may be right. I don’t know of any studies of the Wolfram Language in any setting. The idea of providing a programming language with such rich knowledge behind it is intriguing and promising — so much there for just about any kind of inquiry, for any kind of context.

Hadi Partovi, CEO of Code.org, wrote an essay in response, where he similarly agreed with Wolfram on the issues of what we’re trying to teach and the importance of a programming language to teach those concepts. I disagree with Hadi on his critique of Wolfram, which is that the Wolfram Language is functional and lacks loops and declarations, and is inappropriate for use with learners. It’s totally true that most professional software engineers use procedural programming. But that doesn’t mean we have to. 

If we’re teaching computational literacy or computational thinking, it’s not clear why the practices of professional software engineers should influence what we teach or how we teach it. That’s not what we are teaching. I argue that we need to take a learner-centered approach, where we recognize that learners are not professionals or experts, and particularly in computing, may not want to become professional software engineers.
What gets used in daily practice by professionals is the result of historical and cultural factors that in no way imply that we made choices optimized what is best for learners. Fortran won over Lisp because (in part) we didn’t know how to compile Lisp efficiently, but we do now and we know how to teach Lisp well. C++ and then Java won over Pascal because of perceptions of what industry wanted, not because of data and not because Pascal was shown to be ineffective for learners. What we know about what is “natural” for learners when they are first thinking about programming strongly implies that Wolfram’s functional structures are easier for learners than loops and declarations. We should strive to make decisions for what we use in classrooms based on evidence, not on what is professional practice, nor what we decide based on social defense mechanisms.

More importantly, there is no “best” platform for teaching computer science. As a functional programming language, the Wolfram Language is fantastic for data analysis and exploration, but it can’t be used to create a traditional “app.” Most professional software engineers use procedural programming, using exactly the same concepts that Wolfram criticizes: loops, conditionals, event-handlers, and such. Without these concepts, none of today’s software would function. The debate about which is better—functional vs procedural programming—has raged for decades without an answer.

Source: The Keys to a Well Rounded Computer Science Education | EdSurge News

ECEP and White House Symposium on State Implementation of CS for All

I was thrilled when I got this message two weeks ago:

We have been working for months now on a big meeting organized by ECEP with the Research+Practice Collaboratory and Ruthe Farmer of the White House Office of Science and Technology Policy (OSTP). The goal is to organize state and federal leaders in growing CS for All in the states.  Here’s my written-for-ECEP description of the agenda (not official, not vetted by OSTP, etc.):

CS for All: State-Level Research and Action Summit

Friday

The first part of the Friday sessions at the White House Office of Science and Technology Policy (OSTP) is aimed at strengthening connections between research and practice. The NSF’s CS10K efforts and the President’s CS for All Initiative have created an unprecedented rise in the implementation of CS education efforts across the United States. Making education reform systematic and sustainable requires cross-sector efforts with shared goals and meaningful data collection that can inform practice. We need to make sure that we are building and using evidence-based knowledge about what’s happening in our CS for All efforts.

CS for all is a rare education research opportunity. The American education canon does not change often. We need to create research-practice partnerships to improve our understanding of what works and why.  The Research+Practice Collaboratory (Bronwyn Bevan, Phil Bell, Bill Penuel) will be bringing in a group of learning sciences researchers (including Shuchi Grover, Nichole Pinkard, and Kylie Peppler) and practitioners to work with the ECEP state teams. The goal is to learn how research-practice partnerships can help the field identify key questions and areas for building and sustaining evidence-based practice.

The afternoon session is focused on understanding where the state’s are today. ECEP Evaluators, Sagefox, will share with state groups benchmark data. We will review data on the evaluation of the efforts to make Exploring CS, CS Principles, Bootstrap, and Code.org curricula and professional development available across the country. As a group, we will review state efforts in computer science education implementation and reform. States identify their greatest successes and identify their most pressing needs.

The evening session at OSTP is focused on making the President’s CS for All initiative work at the state level. In the United States, K-12 curriculum and policies are decided at the state-level.  Obama Administration officials will help the state teams to understand the goals of the CS for All initiative. Four state teams will share their successes and efforts, which differ considerably from one another as they meet the unique challenges and objectives of their state’s education system.

Saturday

The CS for All initiative means that we all students in all schools in all districts get access to CS education. Each of our 16 states and Puerto Rico will summarize their successes and lessons learned in 3 minute madness talks. We’ll have two panels — one on negotiating state structures and processes when implementing CS for All, and one on how to make sure that we broaden participation while we aim for CS for All (to avoid being CS Just For Rich Kids). We will have a luncheon keynote from Cameron Wilson of Code.org on how they are aiming to create CS education that reaches all students.

The CS for All initiative requires us to reach all students in a system and sustainable way.

1. Reaching Broader: We can see from the benchmark data where CS initiatives are focused and where there are gaps. Not all districts are implementing CS education yet. We need to develop strategies for filling in the gaps.
2. Reaching Deeper: The data also show us where CS initiatives are starting but shallow. In most districts, a handful of teachers are getting short professional learning opportunities with little follow-up. Teachers need effective learning opportunities that give them the knowledge and self-confidence to make CS a sustainable topic. We need to develop strategies to make CS change deep, systemic, and sustainable.

State teams develop and share their strategies to reach broader and deeper.

Google-Gallup Reports on Race and Gender Gaps in CS: Guest Blog Post from Miranda Parker

Google’s latest reports from their collaboration with Gallup lines up with Miranda Parker’s research interests in privilege in CS education (see preview of her RESPECT 2015 paper here). I invited her to write a guest blog post introducing the new reports. I’m grateful that she agreed.

Google, in collaboration with Gallup, has recently released new research about racial and gender gaps in computer science K-12 classrooms. A lot of the report confirms what we already knew: there are structural and social barriers that limit access to CS for black, Hispanic, and female students. I don’t mind the repeated results though–it helps form an even stronger argument that there is a dearth of diversity in computing classrooms across the country.

The report does highlight interesting tidbits that may not have been as obvious before. For example, black and Hispanic students are 1.5 and 1.7 times more likely than white students to be “very interested” in learning computer science. This knowledge, combined with the data that black and Hispanic students are less likely to have access to learning CS, creates a compelling argument for growing programs focused at these groups.

Research like this continues to push the envelope of what is known about racial and gender gaps in computer science. However, it may be time to dig deeper than visible identities and explore if there are other variables that, independently or together with the other traits, create a stronger argument for why the diversity gap exists. Does socioeconomic status better explain racial gaps? What about spatial ability? These are variables that we at Georgia Tech are looking at, as we hypothesize about what can be done to level the playing field in computing.

 

Today, we’re releasing new research from our partnership with Gallup that investigates the demographic inequities in K-12 computer science (CS) education in two reports, Diversity Gaps in Computer Science: Exploring the Underrepresentation of Girls, Blacks and Hispanics and Trends in the State of Computer Science in U.S. K-12 Schools. We surveyed 16,000 nationally representative groups of students, parents, teachers, principals, and superintendents in the U.S.  Our findings explore the CS learning gap between white students and their Black and Hispanic peers as well as between boys and girls and confirm just how much demographic differences matter.  We’re excited to share this data to bring awareness to issues on the ground in order to help expand CS education in meaningful ways.

Source: Racial and gender gaps in computer science learning: New Google-Gallup research

We have to teach where the students are: Response to “How We Teach Should Be Independent Of Who We Are Teaching”

Valerie Barr has great insights into computing education, especially with regards to diversity (e.g., see the blog post last CS Ed Week about alternative ways to view data about diversity in computing).  I like what she has to say in her most recent Blog@CACM blog post, but I think the title is somewhat misleading.

“How we teach should be independent of who we are teaching” is clearly not true.  No one would argue for teaching Linux kernel developing via all day long bootcamps in C to middle school students.  Few people use CS Unplugged with machine learning graduate students. What Valerie is explicitly addressing in her blog post is an issue called essentialism.

As we continue efforts to diversify computing, we cannot afford to paint any group in a monochromatic way.  We have to embrace the richness of today’s student population by making what we teach meaningful and relevant to them.  There are women who want to geek out about hard-core tech, and there are men who care deeply about computing for the social good.  There are students of all genders and ethnic and racial backgrounds who will be happy with an old-fashioned lecture, and those who will thrive on active learning with examples drawn from a range of cultures and application areas. Many students will be motivated by knowing how the techniques and subject matter they’re learning fit into their future workplace or life goals.

Source: How We Teach Should Be Independent Of Who We Are Teaching | blog@CACM | Communications of the ACM

Here’s a definition of essentialism (from the Geek Feminism Wiki):

The concept of Essentialism states that there are innate, essential differences between men and women. That is, we are born with certain traits. This is often used as an explanation for why there are so few women in science and technology.

In contrast, the critical issue is who is in your classroom, what do they know, and what are their motivations. As How People Learn describes it:

There is a good deal of evidence that learning is enhanced when teachers pay attention to the knowledge and beliefs that learners bring to a learning task, use this knowledge as a starting point for new instruction, and monitor students’ changing conceptions as instruction proceeds.

This is hard to do. We can’t redesign every class for each new student population. What I think Valerie is admonishing us to do is to actually check and not assume certain interests and motivations because of the demographics of the students. When we were developing Media Computation, we did focus groups with students to get their feedback on our developing designs. We surveyed the students to get a sense of what they were interested in and what motivated them. Great work like Unlocking the Clubhouse suggested our starting point, but we did not assume that the majority-female class would have stereotypical responses. We checked with our student population, and we provided different kinds of media interactions to attract different kinds of students within our population.

It would be best if we could provide educational opportunities that meet each student’s needs individually. Short of that, we can design for the students who enter our classrooms, not for the stereotypes that we might expect.

How can teachers help struggling computationalists

My Blog@CACM post for this month is about imagining the remedial teaching techniques of a school-based “Computing Lab” in the near future.

It’s becoming obvious that computing is a necessary skill for 21st Century professionals. Expressing ideas in program code, and being able to read others’ program code, is a kind of literacy. Even if not all universities are including programming as part of their general education requirements yet, our burgeoning enrollments suggest that the students see the value of computational literacy.

We also know that some students will struggle with computing classes. We do not yet have evidence of challenges in learning computation akin to dyslexia. Our research evidence so far suggests that all students are capable of learning computing, but differences in background and preparation will lead to different learning challenges.

One day, we may have “Computing Labs” where students will receive extra help on learning critical computational literacy skills. What would happen in a remedial “Computing Lab”? It’s an interesting thought experiment.

Source: Designing the Activities for a “Computing Lab” to Support Computational Literacy | blog@CACM | Communications of the ACM

I list several techniques in the article, and I’m sure that we can come up with many more.  Here’s one more each DO and DON’T for “Computer Lab” for struggling computationalists.

• DO use languages other than industry standard languages.  As I’ve mentioned before in this blog, CS educators are far too swayed by industry fads.  I’m a big fan of Livecode, a cross-platform modern form of HyperCard. An ICER 2016 paper by Raina Mason, Simon et al. estimated Livecode to have the lowest cognitive load of several IDE’s in use by students.  If we want to help students struggling to learn computing, we have to be willing to change our tools.
• DON’T rely on program visualizations.  The evidence that I’ve seen suggests that program visualizations can help high-ability students, and well-designed program visualizations can even help average students.  I don’t see evidence that program visualizations can help the remedial student.  Sketching and gesture are more effective for teaching and learning in STEM than diagrams and visualizations.  Sketching and gesture encourage students to develop improved spatial thinking.  Diagrams and visualizations are likely to lead remedial students into more misconceptions.

 

K-12 CS Framework: Best we can expect. Maybe as good as we need.

The CS K-12 Framework was released Monday.  This has been an 11 month long process — see first blog post about the framework, first blog post on the process, and the post after my last meeting with the writers as an advisor.  The whole framework can be found here and a video about the framework can be found here:

A webinar about the Framework will be held on Wednesday, October 19, at 12 PM Pacific / 3 PM Eastern. Visit https://www.youtube.com/watch?v=wmxyZ1DFBwk for more details and to watch the webinar on the 19th.

I believe that this framework is about as good as we can expect right now.  Pat Yongpradit did an amazing job engaging a broad range of voices in a short time.  The short time frame was forced on the process by the state policymakers who wanted a framework, something on which they could hang their state standards and curricula.  The NGSS veterans did warn us what could happen if we got it wrong, if we went too fast.  Maybe the framework process didn’t go too fast.

The framework document is impressive — comprehensive, carefully constructed, with a rich set of citations.  It’s teacher-centric, which may not be the best for a document to inform state standards, but that’s the constituency with the strongest voice in CS Ed today.  There are too few CS Ed informed policymakers or district administrators to push back on things that might not work work. The CS Ed researchers are too few and too uncertain to have a strong voice in the process.  Computer scientists (both professional and academic) generally ignored the process. The CS teachers had the greatest political influence.

I predicted in January that this would be a “safe list,” a “subset of CS that most people can agree to.”  I was wrong. There’s a lot in there that I don’t see as being about computation.  Like “Create team norms, expectations, and equitable workloads to increase efficiency and effectiveness” — that’s a high school computing recommendation?  Like “Include the unique perspectives of others and reflect on one’s own perspectives when designing and developing computational products” — you can achieve that in high school?

Those “aspirational” statements (Pat’s word) mean that the writers went beyond defining a consensus document.  They tried to push future CS education in the ways that they felt were important.  Time will tell if they got it right.  The framework fails if schools (especially under-resourced schools) decide that it’s too hard and give up, meaning that underprivileged kids will continue to get no CS education.  If teachers and administrators work harder to provide more and better CS education because of this document, then the framework writers win.

This is an important document that will have a large influence.  Literally, millions of schoolchildren in several states are going to have their CS education defined by this document.

Typing that statement gives me such a sinking feeling because we just don’t have the research evidence to support what’s in the framework.

When I went to meetings, I too often heard, “Of course, teachers and students can do this, because it works in my program.”  So few computing education programs (e.g., packages of curriculum, professional development, assessment, and all the things teachers need like pacing guides and standards crosswalks) have scaled yet in diverse populations.  Maybe it works in your program.  But will it work when it’s not your program anymore?  When it’s a national program? When states and districts take it over and make it their own?  Will it still work?

And we want schools and districts to make things their own.  That’s at the heart of the American educational system — we’re distributed and diverse, with thousands of experiments going on at once.  I worry about how little knowledge about computing and computing education is out there, as guidance when schools and districts make it their own.

So, yeah, I’m one of those uncertain researchers, mumbling in the corner of this process, worrying, “This could go so wrong.”  Maybe it won’t.  Maybe this will be the first step towards providing a computing education for everyone.

The die is cast. Let’s see what happens.

 

Underrepresentation is more dangerous to US than to CS: Interview with Richard Tapia

Insightful interview with Richard Tapia.  He understands issues about broadening participation in computing and talks about them frankly.

You have said, “Underrepresentation is a much greater danger to the health of the nation than to the health of the discipline.” Can you explain what you meant by that?

The disciplines of math and science are in good shape and will continue to flourish without the involvement of women and underrepresented minorities. Of course many of the applications will be impacted by the presence of women and minorities in these application areas. But the theory will continue to be healthy without the involvement of these groups. However, the backbone of America has been mathematics, science, and engineering. We have historically led the world in these areas.  Our changing demographics show that the country is becoming not only more Hispanic but significantly more. If we do not involve women and minority groups in our backbone activity, we will have no one to do the work and the nation will most certainly suffer and lose global competitiveness.

Source: People of ACM – Richard Tapia

What Science Literacy Really Means: Concepts, Contexts, and Consequences

I’ve only just started reading this new report from National Academies Press, but am finding it useful and interesting.  What do we mean when we say that we want people to be scientifically literate?  It’s an important question to ask when considering the goal of computational literacy.

Science is a way of knowing about the world. At once a process, a product, and an institution, science enables people to both engage in the construction of new knowledge as well as use information to achieve desired ends. Access to science—whether using knowledge or creating it—necessitates some level of familiarity with the enterprise and practice of science: we refer to this as science literacy.

Science literacy is desirable not only for individuals, but also for the health and well-being of communities and society. More than just basic knowledge of science facts, contemporary definitions of science literacy have expanded to include understandings of scientific processes and practices, familiarity with how science and scientists work, a capacity to weigh and evaluate the products of science, and an ability to engage in civic decisions about the value of science. Although science literacy has traditionally been seen as the responsibility of individuals, individuals are nested within communities that are nested within societies—and, as a result, individual science literacy is limited or enhanced by the circumstances of that nesting.

Science Literacy studies the role of science literacy in public support of science. This report synthesizes the available research literature on science literacy, makes recommendations on the need to improve the understanding of science and scientific research in the United States, and considers the relationship between scientific literacy and support for and use of science and research.

Source: Science Literacy: Concepts, Contexts, and Consequences | The National Academies Press

Research results: Where does Coding to Learn Belong in the K-12 Curriculum?

I’m not a big fan of the method in this paper — too little was controlled (e.g., what was being taught? how?). But I applaud the question.  Where are things working and where are they not working when using coding to help students learn something beyond coding? We need more work that looks critically at the role of introducing computing in schools.

Nevertheless, there is a lack of empirical studies that investigate how learning to program at an early age affects other school subjects. In this regard, this paper compares three quasi-experimental research designs conducted in three different schools (n=129 students from 2nd and 6th grade), in order to assess the impact of introducing programming with Scratch at different stages and in several subjects. While both 6th grade experimental groups working with coding activities showed a statistically significant improvement in terms of academic performance, this was not the case in the 2nd grade classroom.

Source: Informing Science Institute – Code to Learn: Where Does It Belong in the K-12 Curriculum?

Maryland school district showcases computer science education at all levels: ECEP’s role in Infrastructure

The Expanding Computing Education Pathways (ECEP) Alliance, funded by NSF to support broadening participation in computing through state-level efforts, is one of the more odd projects I’ve been part of.  I don’t know how to frame the research aspect of what we’re doing.  We’re not learning about learning or teaching, nor about computer science.  We’re learning a lot about how policy makers think about CS, how education is structured in different states (and how CS is placed within that structure), and how decision-making happens around STEM education.

It’s not the kind of story that the press loves.  We’re not building curriculum. We don’t work directly with students or teachers. We fund others to do summer camps and provide professional development. We help states figure out how to measure what’s going on in their state with computing education. We help organize (and sometimes fund) meetings, and we get states sharing with each other how to talk to policy makers and industry leaders.

So it’s nice when we get a blurb like the below, in a story about the terrific efforts to grow CS for All in Charles County, MD.  It’s amazing how much Charles County has accomplished in providing computing education in every school.  I’m pleased that ECEP’s role got recognized in what’s going on there.

Expanding Computer Education Pathways (ECEP) provided grant funding for summer camp computer programs. CCPS’s facilitators participate in their Train-the-Trainer webinars to design and plan an effective workshop, build an educator community, increase diversity in Computer Science and teach Computer Science content knowledge. ECEP also funded the Maryland Computer Science Summit in a joint effort with Maryland State Department of Education to bring over 200 attendees from every county in Maryland to share and set priorities for Computer Science education.

Source: Maryland school district showcases computer science education at all levels | NSF – National Science Foundation

The big reason why women drop out of engineering and computing: It isn’t in the classroom

Yep. Though I’ve seen a lot of in-classroom culture that drives out women, the bigger driver is that computing culture drives out many people, like the Stack Overflow results recently mentioned here.

Engineering classes and assignments do not “weed out” women; indeed, data show that women students do as well or better than male students in their course work. Instead, women students often point to the culture of engineering itself as a reason for leaving engineering.

This starts with activities that are designed to show novices how the profession actually does its work, how to interact with clients and other professionals, and how to exercise discretionary judgment in situations of uncertainty. Many discover that the engineering profession is not as open to being socially responsible as they hoped.

And, during the more informal, out-of-classroom training and socialization, women experience conventional gender discrimination that leaves them marginalized. These factors appear to be the main reasons these accomplished women leave their chosen profession.

Source: The big reason women drop out of engineering isn’t in the classroom – MarketWatch

Call for AP CS Principles Readers

Guest blog post from Barbara Ericson, copying a message from Paul Tyman:

Please consider signing up to an an AP CS Level A or CS Principles reader. We will need lots of new readers for the CSP exam. I did the pilot reading last year and it was interesting to see what the students submitted for their paper about a computing innovation and their code for the create task. The readings are really a great professional development opportunity for you. There is always an invited speaker and demos in the evenings. You will meet lots of great people who care about computer science education, both in high school and higher education teaching. We have a social space in the evenings which is quite busy with lots of card games, board games, and music. There are also groups who walk, do yoga, run, etc. They pay for your travel, hotel, meals, and pay you a stipend as well.

Barb Ericson
Georgia Tech

---

From: Paul Tymann <pttics@rit.edu>

Sent: Saturday, October 1, 2016 9:54 AM
Subject: CSP Readers Needed!!

All,

Current estimates indicate that we will need more than 200 readers to score the AP CS Principles exam that will be administered in May 2017. I need your help recruiting new readers. Could you reach out to a couple of your colleagues and encourage them the apply to be readers? As former readers you are in an unique position to explain the reading process and the benefits of participating.

Potential readers can find out more information about becoming an AP CSP reader, and more importantly can sign up to become a reader, by pointing a browser to:

http://etscrs.submit4jobs.com/index.cfm?fuseaction=85332.viewjobdetail&CID=85332&JID=300364&notes_id=2

Please contact me if you have any questions. I hope to, no will, see you in Kansas City!!

—
Paul.

How to Write a Guzdial Chart: Defining a Proposal in One Table

In my School, we use a technique for representing an entire research proposal in a single table. I started asking students to build these logic models when I got to Georgia Tech in the 1990’s. In Georgia Tech’s Human-Centered Computing PhD program, they have become pretty common. People talk about building “Guzdial Charts.” I thought that was cute — a local cultural practice that got my name on it.

Then someone pointed out to me that our HCC graduates have been carrying the practice with them. Amy Voida (now at U. Colorado-Boulder) has been requiring them in her research classes (see syllabus here and here). Sarita Yardi (U. Michigan) has written up a guide for her students on how to summarize a proposal in a single table. Guzdial Charts are a kind of “thing” now, at least in some human-centered computing schools.

Here, I explain what a Guzdial Chart is, where it came from, and why it should really be a Blumenfeld Chart [*].

Phyllis Teaches Elliot Logic Models

In 1990, I was in Elliot Soloway’s office at the University of Michigan as he was trying to explain an NSF proposal he was planning with School of Education professor, Phyllis Blumenfeld. (When I mention Phyllis’s name to CS folks, they usually ask “who?” When I mention her name to Education folks, they almost always know her — maybe for her work in defining project-based learning or maybe her work in instructional planning or maybe her work in engagement. She’s retired now, but is still a Big Name in Education.) Phyllis kept asking questions. “How many students in that study?” and “How are you going to measure that?” She finally got exasperated.

She went to the whiteboard and said, “Draw me a table like this.” Each row of the table is one study in the overall project.

• Leftmost column: What are you trying to achieve? What’s the research question?
• Next column: What data are you going to collect? What measures are you going to use (e.g., survey, log file, GPS location)?
• Next column: How much data are you going to collect? How many participants? How often are you going to use these measures with these participants (e.g., pre/post? Midterm? After a week delay?)?
• Next column: How are you going to analyze these data?
• Rightmost column: What do you expect to find? What’s your hypothesis for what’s going to happen?

This is a kind of a logic model, and you can find guides on how to build logic models. Logic models are used by program evaluators to describe how resources and activities will lead to desired impacts. This is a variation that Phyllis made us use in all of our proposals at UMich. (Maybe she invented it?) This version focused on the research being proposed. Each study reads on a row from left-to-right,

• from why you were doing it,
• to what you were doing,
• to what you expected to find.

When I got to Georgia Tech, I made one for every proposal I wrote. I made my students do them for their proposals, too. Somewhere along the way, lots of people started doing them. I think Beth Mynatt first called them “Guzdial Charts,” and despite my story about Phyllis Blumenfeld’s invention, the name stuck. People at Georgia Tech don’t know Phyllis, but they did know Guzdial.

Variations on a Guzdial Chart Theme

The critical part of a Guzdial Chart is that each study is one row, and includes purpose, methods, and expected outcome. There are lots of variations. Here’s an example of one that Jason Freeman (in our School of Music) wrote up for a proposal he was doing on EarSketch. He doesn’t list hypotheses, but it still describes purpose and methods, one row per study.

In Sarita’s variation, she has the students put the Expected Publication in the rightmost column. I like that — very concrete. If you’re in a discipline with some clearly defined publication targets, with a clear distinction between them (e.g. , the HCI community where Designing Interactive Systems (DIS) is often about process, and User Interface Software and Technology (UIST) is about UI technologies), then the publication targets are concrete and definable.

My former student, Mike Hewner, did one of the most qualitative dissertations of any of my students. He used a Guzdial Chart, but modified it for his study. Still one row per study, still including research question, hypothesis, analysis, and sampling.

I still use Guzdial Charts, and so do my students. For example, we used one to work through the story for a paper. Here’s one that we started on a whiteboard outside of my office, and we left it there for several weeks, filling in the cells as they made sense to us.

A Guzdial Chart is a handy way of summarizing a research project and making sure that it makes sense (or to use when making sense), row-by-row, left-to-right.

 

___________

[*] Because Ulysses now makes it super-easy to post to blogs, and I do most of my writing in Ulysses, I accidentally posted this post to Medium — my first ever Medium post.  I wanted this to appear in my WordPress blog, also, so I decided to two blog posts: The Medium one on Blumenfeld Charts, and this one on Guzdial Charts.