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Universal Design for Learning: Extra Work for Unrealized Returns?

In the fall of 2015, I doffed an old worn-out hat and put two new ones on that I was not certain would fit: I left industry to become a K-8 computer science (CS) instructor at my daughter’s school and a researcher for a STEM+C National Science Foundation grant proposal. It was during this time of transition that I first encountered a handful of papers on the promise of e-textiles in education while searching for curricular resources and conducting a literature review for computational thinking in K-8.

Sewing? No thanks. And so I moved on.

In my first couple years of teaching, I had female students, who had never even had the option of taking a coding class and who furthermore had grown weary of the usual musical, theatrical, or artistic elective offerings, sign up en masse with each other. But the novelty eventually wore off, and fewer and fewer girls signed up for my coding classes. By the spring of 2020, I only had three girls spread out in each of the three classes I was teaching. My classes, viewed over time, with just the right music, could make for a sad movie montage.

This spring, in my master’s program in educational technology, we learned about Universal Design for Learning (UDL): a framework used by educators to ensure learning experiences are planned with multiple means of engagement, representation, and expression to give students with different backgrounds, interests, and skills an equal opportunity to learn (CAST, 2018). I was skeptical and overwhelmed. In theory, UDL was ideal; but in practice, it sounded like a ton of preparation for the occasional student who might never appear in the classroom. What overworked teacher could afford to put so much effort into what might result in so little return?

But, faced with a now severe deficit of girls in my classes, and having attended two recent conferences focused on equity in computer science, and tasked with experimenting with an innovative technology for one of my master’s courses, I had motivation in triplicate to search for those papers on e-textiles I had encountered five years ago.

Qiu, Buechley, Baafi, & Dubow (2013) described in detail a curriculum designed around e-textiles that I could easily adapt: students would learn the basics about circuits and computer science concepts while engaged in making an increasingly complex series of sewing projects. Female students who were not interested in making a video game or robotics would gravitate to the awesomeness of adding LEDs to sewing projects, subsequently giving them incentive to learn code for making more complex projects; sewing and coding would be two separate means of engagement in a single class.

Two of my classmates chose Breakout EDU to explore as their innovative technology and to serve as the basis for their UDL learning experience. In providing feedback on their learning experiences, both from a UDL and from an intersectionality point of view, I realized that the recommendations I gave them to support an imaginary student on the autism-spectrum, recommendations that would inform that student how to handle potentially awkward social situations or be considerate about unequal distribution of problem-solving or speaking time–those would be useful to all of the students in the classroom! I was so wrong about UDL being a waste of time!

The feedback I received from my classmates, especially regarding assessment, reminded me that students have an uncanny ability to complete maker projects without understanding the concepts behind them.

Fig. 1. Peer feedback on adding assessment details to the learning experience.

Constructionsim sometimes just leads to…construction with no understanding; so simply completing an e-textiles project would not be enough evidence of learning. Luckily, I was able to find some resources describing the use of sharing circuit diagrams (Hadad, Thomas, Kachovska, & Yin, 2020), and the use of individual white boards to assist in assessment in case students are uncomfortable showing their understanding–or misunderstanding–to the entire class (Mealy, 2016). Another common piece of helpful advice was to offer student multiple ways for students to present their projects which would adhere to UDL’s principle of multiple means of expression.

Fig. 2. Peer feedback on multiple options for presentation.

Overall, crafting this learning experience based on UDL, intersectionality, research, and peer feedback has been an extremely positive experience. So much so that I intend to use all these ideas in the design of an upcoming summer computational thinking PD for teachers in our local school districts. But most importantly, I expect to see significant increase in female students in my classes as well as better retention.

Fig. 3. The assessment additions made after receiving peer feedback.


CAST (2018). Universal Design for Learning Guidelines version 2.2. Retrieved from

Hadad, R., Thomas, K., Kachovska, M., Yin, Y. (2019). Practicing formative assessment for computational thinking in making environments. Journal of Science Education and Technology, 29, 161-172.

Mealy, N. (2016, November 15). Formative assessment [Blog post]. CS for All Teachers.

Qiu, K., Buechley, L., Baafi, E., Dubow, W. (2013). A curriculum for teaching computer science through computational textiles. In IDC ’13: Proceedings of the 12th International Conference on Interaction Design and Children, 20-27.

Finally! A Visual Worth Sharing

What a difference an app makes!

After struggling with all things visual, I finally tried Canva, and it has made all the difference in the world. I had previously used, and somehow missed their templates. Canva asks you to pick a category of visual first, then directs you to pick a template; it’s opinionated workflow help me make this infographic:

So much better, right? Apologies for the blurriness, Canva requires purchase of Pro account to download higher quality images.

Universal Design for Learning (UDL) is a comprehensive framework to help educators prepare singular learning experiences to meet the needs of a variety of students with different skills and abilities (CAST, n.d.). Its comprehensiveness comes at a cost, one that I have experienced before when encountering massive paradigm shifts in education. After reading about UDL for the first time, I felt overwhelmed, inadequate, and stunned. Would I really have to redesign every single one of my lessons to adhere to the principles of UDL? I haven’t even figured out how to teach one type of student yet!

After struggling for a day with the resources provided for me on UDL, I did some additional searching on the internet about UDL and came to some important understandings:

  • I have made plenty of accommodations over the years for students; I could apply those accommodations to future lessons designed according to UDL.
  • I have been exploring and continue to explore different technologies and methods for engaging students in CS. This fits under UDL’s Affective Networks principle.
  • I am constantly trying different ways to explain and teach CS concepts, from visuals to kinesthetics. This fits under UDL’s Recognition Networks principle.
  • While there are a limited number and types of projects students can create in CS, some of them are very different from others. And I have introduced many of them to my students:, Scratch, python, Twine, Minecraft, and unplugged lessons. This fits under UDL’s Strategic Networks principle.

Thus, the unintended result of all my experimentation in trying to figure out the best single way to teach my students is that I have now employed and gained experience in multiple different ways to teach my students, which, according to UDL, is exactly what I was supposed to have been doing! I just need to figure out how integrate all the different experiences and options together.

Phew! Sanity saved!


Burgstahler, S. (2011). Universal design: implications for computing education. ACM Transactions on Computing Education, 11(3), 1-17.

CAST. (n.d). About UDL.

Currie-Rubin, R. (2015). The Data Inquiry-UDL Cycle: How Data Inquiry and UDL Implementation Work Together to Improve Teaching and Learning. Cast, Inc.

Degner, J. (2016, November 15). How universal design for learning creates accessible classrooms. Education Week Teacher.

Challenging Normative Algorithms

It was the mid 90’s. I remember sitting in the classroom and looking around at the rest of my classmates, all of us stunned and feeling viscerally disturbed. Our Sociology 101 professor had just screened the drag queen documentary Paris is Burning for us, mostly suburban, sheltered, heterosexual kids. My background was especially conservative, coming from a religiously devout immigrant family.

The pedagogy could have come straight out of a Sociology 101 professor’s handbook if there were such a thing, since the lesson was both so obvious and painfully necessary: what we believed to be normal, what we knew to be normal, what we felt to be normal down to our bones was not necessarily normal for other persons. Or even close to normal for other groups of people. Thus normal did not and could not be equated with right.

I would experience that disruptive feeling briefly again, as a young adult who had doubled down on his conservative upbringing, after accepting a ride from the office to the train station from a perfectly friendly coworker, who also happened to be gay.

And now, twenty years later, that visceral feeling again, but this time in reverse, after engaging with content from conservative media, like the National Review, which I have gradually grown opposed to over the years. This was again, at the behest of class, this time for my master’s in educational technology. What is it with liberal institutions of learning and their insistence on challenging students’ normative beliefs? To be fair, the main purpose of this lesson was to intentionally disrupt the algorithmically determined echo chamber in which technology companies have enclosed us. Challenging beliefs was simply a side-product.

So, yes, mission accomplished. I need to confront yet again the disgust I feel for opposing norms–because to be honest, disgust is what that visceral feeling feels like–and to start the mental process of turning that disgust into mere disruption, turning disruption into engagement, and engagement finally into understanding; eventually helping us, as educators, to be better equipped to respond and engage with students from all backgrounds and experiences.

Learning, Making, and Failing. Utterly Failing.

The assignment for my Master’s program in educational technology was simple: create a remix video about copyright practices according to legal copyright practices. Very meta. Since creating using visual tools and medium was a weakness of mine, I was apprehensive about making one. But, I thought my strengths in figuring out technology would balance things out.

I was wrong.

Let’s start with my successes: I fiddled for around for about an hour with the idea of using pop music (the handful of different songs titled “Express Yourself”) and using 30 second snippets of each, but I couldn’t figure out how to download music from Spotify or SoundCloud without breaking their terms of service, which would have been illegal based on copyright law. And by “download” I meant obtaining an mp3 of a song, not merely having the ability to listen offline. I considered purchasing individual tracks from Apple Music, but I decided to look into creative commons music. And, compared to what was available freely around five years ago, the tracks available now are higher quality, easier to find, and easier to download. I found tracks that I wanted to use on ccMixter and the Free Music Archive.

Two hours had gone by.

I had no concept of what videos I would use to stitch together my video on fair use. Do I take four other videos on fair use and simply use parts of each? No, that would be lame. I floundered for another hour looking at fair use videos before I gave up on that idea and finally watched another student’s video for ideas, which inspired me to write a simple outline and describe video scenes that would fit each section of fair use:

  1. Materials must not be used for commercial purposes (education, research, critique, parody, were okay). Here I would use a video snippet of money. Lots of money. Like, printing presses of money.
  2. Materials must not be used in whole, but rather a small part would be okay. Here I would find a video of a competitive eater; and then as a contrasting image, a video of a bird eating.
  3. Meaning must be added to the material. ??? (Never figured this one out.)
  4. The material must be transformed. Transformers!

Another, oh, hour and a half had gone by.

Now it was time to search for my videos. I tried Vimeo, but typing in search terms bounced me to non-creative commons content. I tried advanced Googling, but advance Googling wouldn’t allow me to do both a video search and restrict by creative commons licensing. I made my way over to the Prelinger Archives, and after about an hour of watching old videos, found a video of a U.S. mint making coins. But there were no videos on eating. So, I made my way to Youtube, and finally found a video of a guy explaining how to find creative commons videos on Youtube, and finally found some videos made by competitive eaters. But, then I couldn’t download them without breaking Youtube’s terms of service. Went back to Vimeo…and you get the idea.

Here’s a summary of the video finding issues I encountered:

  1. In order to find specific snippets of video, I had to search for and watch video. Nobody describes what’s in the videos. I had to watch them to find out.
  2. There was no guarantee that I could find a particular snippet I wanted.
  3. When I did find one that was CC licensed, there was no guarantee that the owner of the video actually allowed downloads of it.
  4. Using a 3rd-party video downloader was against terms and service of the video hosting websites in question.

Another two hours gone by. It was well past midnight, and I hadn’t even found videos yet, let alone stitch them together and write a blog post about it. Granted, I had not started until the day the assignment was due; but at least this week, I had finished all the readings beforehand.

I had to cut my losses. There was no way I could finish this assignment in a reasonable amount of time, even if I had started earlier. Maybe if I had found video snippets first, and then figured out how to make meaning out of them, instead of the other way around?

Despite not making anything, I think I have a better understanding of what it takes to remix: it depends on the ability to see and take parts out of their original context and envision them in new contexts. There’s a certain type of visual creativity involved that is similar to DJ’ing or creating music mashups; a level of visual creativity that I apparently possess very very little of! Heck, I had trouble finding a frickin’ gif during a Zoom discussion at the beginning of the semester for another course. And, do not get me started on the infographic I made for the last unit. That, too, was extremely bad!

So, how much of my inability to work with visual media is an individual trait and how much is generational? Hard to say. I’m guessing it’s the combination of both that’s proven especially crippling for me during this assignment.

So, then, as an educator, how do I reflect on this? Is this type of project something I offer to my students? Or, do I avoid it like the plague?

Learning, Failing, and Making

Legos bored me.

Whenever I got a new set for Christmas or for birthdays, I only seemed capable of following the provided instructions for building what was displayed on the box. I couldn’t envision anything else to build. But, my friend Bert would come over and always build the coolest cars and spaceships. “How’d you think of to build that?” I’d ask him. My own attempts at original Lego creations never turned out half as good.

But, my friend Eric had an electronic race track that was bigger and better than mine. I would go over and build the most elaborate tracks I could imagine, replete with curves and multiple loops that would double as tunnels for straight sections of track. “I don’t like them! They’re too complicated!” Eric complained. Yes, the complexity was what made those creations so awesome!

Fig. 1. Does Making always work? No, it doesn’t. This infographic1 is an example!

Two different playsets resulted in two completely different experiences. How would Piaget and Papert have explained the outcomes?

Piaget would have reflected on my hapless Lego skills and concluded that my inner schema for Lego creation was underdeveloped. My lack of understanding of the affordances of Legos in building 3-dimensional structures prevented me from using them successfully. My poor outward Lego creations reflected a failure to understand internally the abstraction of Legos as individual parts of a pre-imagined whole. Perhaps, I was supposed to have started with a mostly fully developed idea in mind and find the Lego pieces that would complete that idea, instead of picking a piece and hoping that attaching it to another would eventually lead to something. Whereas my success with racetrack design revealed a honed schema that understood how single pieces of track could be combined to make exciting and dynamic paths.

Papert would simply have said my failure with Legos should have provided me an opportunity for debugging and sense making. In my use of Legos, I was supposed to have eventually discovered understanding that would have been more difficult to attain without those Legos. Experimenting with Legos should have lead to deeper understanding of 3-dimensional construction, the outward manipulation of a tool influencing inner understanding. Whereas experimenting with race track pieces led to an understanding of how they could best be combined to create an exhilarating and satisfying creation.

Papert’s constructionism seems ultimately an optimistic learning theory–that the right tool will lead to comprehension, and that reflection upon failure should lead to greater understanding. Piaget’s constructivism seems more realistic–not necessarily pessimistic–but allows for the continuation or creation of misconceptions should the marriage of existing schema and new information so align.

As a K-8 computer science instructor, and I suppose, as a child who struggled with Legos, Piaget’s constructivism provides more guidance for classroom instruction. Papert never explained what should happen if the student should lack chemistry with a tool. Move onto another tool to assist in sense making? Persist in using the tool and hope for a breakthrough? But, Piaget would say students who lack understanding of coding concepts need dialectical opportunities to develop their inner understanding. Much of coding is developing internal understandings–mental models–of what the computer is doing. There is limited academic value to letting students flounder around, experimenting and debugging, without guided instruction.

And, perhaps, this is the fear that teachers have with making: sure, failure is good, because it leads to persistence and success. But, failure can also simply lead to more failure and despair. A teacher cannot ensure positive making experiences for every student, because not all students will develop good chemistry with the provided tools. Sure, some of the students will have great making experiences. Bert loved playing with my Legos. But, some are going to flounder as well, as I did. What then? How much money will be thrown at tools until every student finds a tool they can use successfully?

I concede that Making does provide an alternate avenue for learning–one that can be attractive to students who may not succeed in traditional curriculum. And perhaps the experience of abject failure will serve as empathy building for those students who do succeed in more traditional ways!

I didn’t realize I hated Legos so much!


  1. Background Lego image by Rick Mason via Unsplash; image was cropped and darkened. Background wooden train set image by Jason Leung via Unsplash; image was cropped and lightened.

Computational Thinking at Thirteen

Computational Thinking (CT) is in the midst of its awkward teenage years. It is no longer the cute infant idea that brought together leaders in the field of computer science (CS). Instead, it is criticized for not being sure of what it is and maligned by comparisons to its older sibling (CS). At the same time, it has found a sympathetic friend group in educators who are influencing the direction of its development, a direction that its parents (computer scientists) do not appear to understand. Oh, the drama! Thankfully, it has not been involved in an accident that killed off CS. Imagine the guilt.

This Is Us
Computer scientists are right to criticize the lack of accuracy in definitions of CT. The ordered execution of codified language and basic CS design conventions, including models of computation, central to understanding algorithms and abstraction are often left out of CT definitions (Denning, 2017; Guzdial, 2018) but are essential parts of CS as a discipline (Denning, et al., 1989; Aho, 2011); consequently, a poor understanding of algorithms and abstraction would lead to poor CS understanding. From the point of view of computer scientists, any definition of CT that lacks mention of computation is merely describing problem-solving which garners no particular interest in CS.

Different Strokes
Coincidentally, educators love CT as a metacognitive problem-solving framework and as a potential pathway to CS, both widely considered to be classroom needs. Possible reasons that CT has been introduced to some educators with CS-specific content omitted are that CS itself can be inaccessible to both teachers and young students and that CT might be more sensibly applied in non-STEM contexts without CS concepts.

Family Ties
CT critics must realize that if they contend that CT stripped of CS is simply problem-solving, logic then dictates that problem-solving must be a core part of CT. Were it not, then CT stripped of CS would simply be nothing. In other words,

problem-solving + CS = CT

must be true. What happens when problem-solving is removed from CT? You have the ability to do CS things without the good metacognitive skills to do them well, i.e. coding but without computational thinking, a possibility which researchers have recently uncovered (Straw, Bamford, & Styles, 2017).

Growing Pains
If that additive relationship between problem-solving, CS, and CT is indeed true, then there are a handful of helpful implications. Apply to CT what Jeannette Wing stated in 2008 and again in 2017 about the need for progression of computer science concepts for students of all ages, and problem-solving may very well be the result when parts of CT are abstracted away for developmental and contextual reasons. Moreover, if young students can be taught better problem-solving skills as an early part of the progression of CT understanding, then that should increase their ability for CT when they start learning the CS part of the CT equation later.

Educators and their understanding of student development at different grade levels will be essential to determine the progress of CT understanding, from completely non-computational models to the strongly CS version of CT, and all the intermediate steps in between. Educators will then need to be cognizant that some CS education is necessary to understand how to assess whether and when students are ready for CS concepts that enhance CT and how those CS concepts can be used to support cross curricular goals. An estimated guess would peg educators at the fourth(?) grade level as those who will need to prepare themselves to learn basic CS concepts, like conditionals and flow-of-control structures, to support the potential inclusion of CS along with problem-solving as part of the CT progression.

…and Geeks
Computer scientists should be aware that debating the specifics of CT content knowledge, while important, is no longer sufficient for progressing CT understanding. Rather, any CT component under debate must be placed at the correct level of CT progression based on CT pedagogical content knowledge–likely obtained from educators–resulting in varying, but successive definitions of CT. Problem solving, or its associated skills, is one such component that may be central to CT in the early part of the CT progression, but diminish in importance as more complex components are added in the latter parts of the progression. The midpoint of CT progression could be represented by the CT described by Weintrop, et al. (2016), which would include systems thinking and data practices. And at the highest point of the CT progression? That will be a debate for computer scientists.

Happy Days
CT critics can no longer ignore that CT accessibility at different levels of cognitive development may be the reason behind simplistic, vague, or incomplete definitions of CT. Continued progress in CT understanding will have to be a collaborative effort between computer scientists and educators who consider the added dimension of CT progression. These additional complexities are indicative of the level of maturity that CT has reached since 2006, making progress more difficult but achievable.



Aho, A.V. (2012). Computation and computational thinking. Computer Journal, 55, 832–835. Retrieved from
Denning, P.J. (2017). Remaining trouble spots with computational thinking. Communications of ACM, 60(6), 33–39. Retrieved from
Denning, P.J., Comer, D.E., Gries, D., Mulder, M.C., Tucker, A., Turner, A.J., & Young, P.R. (1989). Computing as a discipline. Communications of ACM, 32(1), 9–23. Retrieved from
Guzdial, M. (2018, October 15). Computational mapping: an important set of skills in computational thinking we can define and test [Blog Post]. Retrieved from
Straw, S., Bamford, S. and Styles, B. (2017). Randomised Controlled Trial and Process Evaluation of Code Clubs. Slough: NFER. Retrieved from
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25, 127-147. Retrieved from
Wing, J.M. (2008). Computational thinking and thinking about computation. Philosophical Transactions of the Royal Society, 366, 3717–3725. Retrieved from
Wing, J.M. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7-14. Retrieved from

Computational Thinking

In the spring 1991, my freshman year high school history teacher had me and a couple of other promising students spend the entire semester researching and writing term papers on topics of our own choosing. Fast forward to a week before the due date, and I had read around a dozen thrilling books on the American Mafia1 but had not written a single word. I was simply overwhelmed by the sheer volume of information I had found. In the end, I managed to string together twenty pages worth of facts that were not organized in any coherent way, except perhaps chronologically, and turned in my term paper well aware of its failings but unaware of how to remedy them.

I did not know it at the time, but what I sorely needed was Computational Thinking (CT).

It would not be until fifteen years later in 2006 that Jeannette Wing considered computing’s fast growing influence in the physical sciences and presciently announced a reincarnation2 of CT as a universal problem-solving skill set. A mere one year later in 2007, the first iPhone started the widespread adoption of mobile devices that spurred demand for CS in K-12 education and a decade long growth of CS enrollment in universities (Guzdial, 2017). And in 2010, the National Research Council kickstarted research into CT in K-12 education.

What is Computational Thinking?

Computational Thinking (CT) encompasses a set of thought processes traditionally used in computer science (CS) to solve problems, achieve goals, and complete projects; processes such as decomposition, pattern recognition, abstraction, and algorithms. CT is especially good for clarifying and solving large complex problems or projects in any domain, like term papers or science projects, but is also effective for small problems as well.

Students who learn and practice CT will develop their ability to tackle uncertainty and complexity in problems in school assignments and in their own lives (International Society for Technology in Education & Computer Science Teachers Association, 2011). CT fosters an iterative mindset where students have the confidence to find solutions to difficult problems because they know they can try as many times as needed and make incremental improvements to gradually succeed. In practicing CT, students will learn that mistakes are not fatal, but helpful, and can be used to make the next version better, turning students who feel helpless and act clueless into students who are confident in experimenting with ideas and exploring creative solutions.

For teachers, CT is a way to integrate much needed and hoped for problem-solving instruction into the curriculum without having to make room in an already full curricular schedule. Because of CT’s usability across all domains (Barr & Stephenson, 2011; Yadav, Zhou, Mayfield, Hambrusch, & Korb, 2011), it can be taught in one subject and then used again and reinforced in another. Teachers need not be math and science experts to teach CT since it can be taught in english language arts and social studies, allowing all teachers to teach CT in conjunction with their strengths.

It is important to understand that even though CT is used by computer scientists as a precursor to coding as well as during the coding process, CT is not coding and can be taught without coding and even without computers (Yadav, et al, 2011). But in a world where computers are now everywhere, from satellites to coffee makers, from research labs to our back pockets; and used by everyone from grandmothers to toddlers, researchers hope that integrating CT in K-12 education will lead to broader participation in CS especially among underrepresented minorities and also better prepare students for the problems they will encounter in today’s modern technological world.

After all, if students are taught to think like computer scientists, if students learn to use the mental tools that computer scientists use, then, perhaps, hopefully, students will have an easier time learning to do the things computer scientists do.

That is the promise of CT.

Components of CT

The easiest of the CT skills to explain and use is decomposition, which is the breaking down of a large complex problem into smaller, more easily digestible and solvable parts. Organizing a term paper by creating an outline in English class would be one example. Delegating roles and tasks in a group collaboration is a way to decompose a group project. The separate branches of government, the way large corporations are organized into departments, or the way engineering projects are completed in phases are examples of large scale decomposition at work. Ultimately, decomposition is an organizational skill that allows people to focus on small parts of the whole, and complete large complex problems one small part at a time.

Pattern recognition is the finding and understanding of patterns in data. Analyzing climate data and identifying warming trends, looking at examples of expository writing on animal life cycles and discovering what type of information is repeated across reports and using those patterns to write one’s own report, surveying classmates and gathering data to look for patterns are all examples of pattern recognition. Finding repeated themes in literature and film, in music and art, in scientific phenomena all allow students to develop their observational and analytical skills.

Abstraction is the distillation of information and separation of details to convey key ideas clearly without any unnecessary or complicating specifics. Writing a inside-the-flap book summary without spoiling a book; communicating the essence of a subject, idea, or written material; drawing representations or models of physical objects that include key identifying details, but not every detail, are all abstractions. In engineering and CS, abstraction allows people to use complicated machines like automobiles and computers without having to worry about exactly how they work; the details of engine combustion and transistors are hidden while users need only interface with a steering wheel and keyboard. Social media is an abstraction of a person, showing only the most witty and photogenic parts of person, and obscuring all of one’s awkwardness and brokenness. Practicing abstraction allows students to clearly understand, use, and communicate key ideas without being bogged down with or overwhelmed by the details.

Lastly, algorithms, which is the component most closely aligned with computer science and coding, are step-by-step instructions to complete a task or solve a problem. Choreographing a dance, or making detailed plans for an event; writing detailed instructions for how to do an activity, planning before-game strategy in a team sport are all examples of creating and using algorithms. Closely related to algorithms is the idea of debugging: the step-by-step testing and fixing of algorithms or instructions for the purpose of improving their correctness and efficiency. Students that actively engage in debugging gain perspective on the value of mistakes and can learn to persist‐keep debugging‐until everything works.

Organization, observation, communication, and planning: these all sound suspiciously like management consulting skills! Could CT just be a set of existing skills rebranded for the modern age? Does it matter? Because there were never any management consultants calling for management consulting in K-12 education;3 it is computer scientists and educational researchers who have envisioned the use CT and its components in K-12 education, not to create a future consultants but future problem solvers capable of applying these skills in all domains including CS.4

CT in the Classroom

Because CT is a set of skills rather than a field of knowledge–CT is about the “how to” rather than the “what”–it can be easily integrated into existing lesson plans, where instead of merely having students learn content, the lessons ask students to use CT as a specific method for learning content.

An Example CT Lesson Plan

Take for instance the rules surrounding the use of the indefinite articles “a” and “an”. A typical lesson teaches students that “an” is used to refer to nouns that start with a vowel sound, whereas “a” is used to refer to nouns that start with a consonant sound. A CT-infused lesson on indefinite articles, as described by the Exploring Computational Thinking team at Google (2015), instead present multiple uses of “a” and “an” in existing sentences and explicitly ask students to use pattern recognition to discern the pattern themselves. Students are then instructed to write down an algorithm, a sequence of step-by-step instructions that, based on specific characteristics of a noun, determines whether to use “a” and “an”, and then test their algorithm out on a few sentences or nouns. In this way, instead of simply being told what the rule is, students are also given the opportunity to practice their pattern recognition and algorithm creation skills to determine the rule themselves. With CT, students have the tools to become more active participants in their education.

Integrating CT into existing lesson plans is a convenient and effective way for teachers to teach without having to learn new technology or any CS, lowering the barriers to teaching problem-solving skills. But, it should be noted that including computers in CT instruction has added benefits. Let us reconsider the CT-infused indefinite article lesson plan.

An Example CT Lesson Plan, With Computers

A CT-infused lesson plan with computers on indefinite articles can have students use computers to scrape children’s news sites or ebooks to quickly find a slew of sentences that include the use of an indefinite article. Students could then use the computer to search through those sentences for indefinite articles and noun pairs and quickly create an alphabetized list to assist in discovering patterns. Students could test their own algorithm against a computer generated list of words and compare the two for accuracy. Any discrepancies can be identified and used as data to improve their existing algorithm. This computer-assisted CT-infused lesson gives students added exposure in using computers, building their confidence and developing in them the identity of persons who are capable of using computers to solve problems, a key factor in encouraging underrepresented students to pursue CS.

Advanced CT Components

There are an additional set of CT components identified in literature: data practices (data collection, analysis, and representation), modeling, simulations, and automation, which are all widely used in the math and sciences to further understanding of mathematical concepts and scientific phenomena (Weintrop, et al., 2016). These can be considered advanced CT components since they are built upon the basic CT components. Data practices are an extension of pattern recognition, modeling and simulations are animated or visual abstractions, and simulations and automation are the application of algorithms. The use of these advanced CT concepts allow students to view and edit models to see the immediate effects of changing variables, aiding in the understanding of mathematical formulas or complex physical systems.

Students can use CT to solve problems and further understanding in multiple disciplines. It is a way for teachers to allow students to experiment, fail, retry, and persist in ill-defined and open-ended situations, building confidence and grit in their ability to find solutions. But CT also promises to prepare students for CS and prepare them for a world where a growing number of problems are created by computers and more and more problems are only solvable by computers. Recent events5,6,7,8 highlight the lack of equity and inclusion in CS and the need for engagement by a diverse population of students with different backgrounds, cultures, and values. Perhaps it is only fitting that CT in and of itself could be a solution to these problems.

My high school self would have been psyched to use decomposition to organize my term paper into more sensible chunks. Multiple attempts at abstraction could have helped me realize the most important parts of what I had read instead of thinking everything was important. CT would have turned what for me was an extremely frustrating and stressful writing assignment into the elation that students experience when they accomplish something large. Even now, nearly thirty years later, I smile at the how wicked awesome that term paper would have been if only I had CT skills at my disposal. CT promises to give students a better chance at experiencing those types of successes. It reminds them to take the first step and try, and gives them the tools to take the next step to make things better, and on and on until it all finally gets done.


Barr, V., Stephenson, C. (2011). Bringing computational thinking to K-12: what is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48-54. Retrieved from
Exploring Computational Thinking. (2015). ECT lesson plan: indefinite articles [Lesson Plan]. Retrieved from
Guzdial, M. (2017). ‘Generation CS’ drives growth in enrollments. Communications of the ACM, 60(7), 10-11. Retrieved from
International Society for Technology in Education, & the Computer Science Teachers Association. (2011). Operational definition of computational thinking for K-12 education. Retrieved from
National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking. Washington, DC: The National Academies Press. doi:
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25, 127-147. Retrieved from
Wing, J.M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33-35. Retrieved from
Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., & Korb, J.T. (2011). Introducing computational thinking in education courses. SIGCSE ’11 Proceedings of the 42nd ACM technical symposium on Computer science education (pp. 465-470). New York, NY: ACM. Retrieved from


1 Francis Ford Coppola’s The Godfather III had just been released in the winter of 1990.
2 Seymour Papert first coined the term “computational thinking” in 1980 in his book Mindstorms.
3 This is pure but arguably reasonable conjecture. There is no money in education, so management consultants would never go near it.
4 Perhaps it is the promise of CT that sets it apart from management consulting skills?
5 Gupta, A.H. (2019, November 15). Are algorithms sexist? New York Times. Retrieved from
6 Ajunwa, I. (2019, October 9). Beware of automated hiring. New York Times, p. A27. Retrieved from
7 Goethe, T.S. (2019, March 2). Bigotry encoded: racial bias in technology. Reporter Magazine. Retrieved from
8 Dean, S. (2019, December 2). Riot Games will pay $10 million to settle gender discrimination suit. Los Angeles Times. Retrieved from

NLP: Oil Change Conceptual Change

I successfully changed the oil in my car!

For a networked learning project, changing the oil in my car was a good practical skill to learn. But for exploring how well I could learn something through online sources only, perhaps an endeavor I could practice more than once every 7,500 miles would have been better. Luckily, aside from a small mess near the end, everything went smoothly:

Changing the oil in my car. Footage and narration of my own oil change.

Future oil changes should only cost approximately $33 and fifteen minutes of hands-on time!

At the outset, I found a slew of videos that demonstrated oil changes on my specific car, which collectively contained enough information to give me a decent chance of performing a successful oil change immediately. But, finding more detailed information to deepen my understanding and develop a conceptual framework (Bransford, Brown, Cocking, Donovan, and Pellegrino, 2000) proved harder. For every helpful piece of information I found (Subaru, 2012; Reed, 2013; Cool Stuff Guys Like, 2017; Dear Car Talk, 2013; Reichert, 2015), I also found information that was either redundant (Bruzek, 2016), irrelevant (Bergsma, 2013), or inaccurate (bezly and cardoc, 2014).

Further increasing the amount of time I dedicated to research was my metacognitive process described by Bransford et al. (2000), where I constantly reassessed the state of my learning. Though, truthfully, my metacognitive process felt like more of a neurotic process, and my “constant reassessment of learning” seemed more “a compulsive fear failure.” What if I stripped the oil drain plug? What if I cracked the oil filter? What if I put too much motor oil in? Still in the end, I deepened my understanding, as evidenced by transfer of knowledge (Bransford et al., 2000) of how to pour oil out of a one quart container (Bryant, 2019) to pouring chicken broth out of a one quart box!

Overall, my experience with networked learning was replete with learner-driven, research-based, evaluative learning, all aspects of 21st Century Learning classrooms (21st Century Schools, 2017). I discovered core content knowledge on the properties of synthetic oil (Quirk Subaru of Bangor, n.d.), engaged in problem-solving in trying to determine how to do an oil change without a jack and jack stands, and perhaps even developed emotional awareness of how frustrating networked learning can be, all 21st Century Learning values described by Kereluick, Mishra, Fahnoe, and Terry (2013).

And yet, I kept on wondering, might a 20th Century model of learning have resulted in quicker results with equally deep understanding? Would not taking notes on a lecture by an expert who had already done all this research and learning have been just as effective a learning experience? But, there were no local classes I could have taken to learn basic automotive skills, classes that I vaguely recall existing when I was an adolescent.

Thus, perhaps the strongest argument for adopting 21st Century Learning might not be the emergence of new technology that requires 21st Century skills, but rather the corresponding disappearance of 20th Century learning opportunities. This is a deeply sobering and disconcerting thought. I know now that networked learning is an effective replacement for disappearing 20th Century learning, but what should replace the in-class social interaction that disappeared along with it?


21st Century Schools. (2017, January). Is your school of classroom 21st century? Retrieved from
Bransford, J. D., Brown, A. L., Cocking, R. R., Donovan, M. S., & Pellegrino, J. W. (Eds.). (2000). How People Learn: Brain, Mind, Experience, and School. Expanded Edition. Washington, DC: The National Academies Press. doi:
Bergsma, K. [Mercedessource]. (2013, October 11). Engine oil vacuum extractor shootout part 1 with Kent Bergsma [Video file]. Retrieved from
bezly, & cardoc. (2014, March 16). 2013 Outback oil capacity [Forum thread]. Retrieved from
Bruzek, J. (2016, January 3). Expensive oil changes are here to stay. Retrieved from
Bryant, J. (2019, July 17). Know how notes – 10 oil change tips [Pdf file]. Retrieved from
Cool Stuff Guys Like. (2017, February 13). 2009-2014 Subaru Legacy and Outback oil change [Video file]. Retrieved from
Dear Car Talk. (2013, December 2). Today: Death by Lint? [Blog post]. Retrieved from
Kereluick, K., Mishra, P., Fahnoe, C., & Terry, L. (2013). What knowledge is of most worth: teacher knowledge for 21st century learning. Joural of Digital Learning in Teacher Education, 29(4), 127-140. doi:10.1080/21532974.2013.10784716
Quirk Subaru of Bangor. (n.d.). Subaru synthetic oil – fact, fiction and requirements. Retrieved from—fact–fiction-and-requirements.htm
Reed, P. (2013, April 23). Stop changing your oil! [Blog post]. Retrieved from
Reichert, E. (2015, September 17). Used motor oil disposal and recycling [Blog post]. Retrieved from
Subaru. (2012). 2013 Legacy/Outback owner manual [Pdf file]. Retrieved from

A Lesson Plan for the 21st Century

The premise behind 21st Century Learning is simple: to prepare students for the ever-changing modern technological world, teachers need to adapt (Jerald, 2009).

But the simplest premises can conceal overwhelming complexity.

That so many organizations have struggled to provide satisfying, clear, and complete descriptions of 21st Century learning (Applied Educational Systems, 2019; National Education Association, 2019; Partnership for 21st Century Skills, 2019) reveals the massive and mind-numbing scope of transformation required in education to provide classroom experiences relevant in a world altered by decades of technological change. After all, the march of technological change has been relentless, while the flow of educational change has been glacial in comparison.

Thankfully, not everything in education will need to change. Kereluick, Mishra, Fahnoe, and Terry (2013) observed that the values and learning goals underlying 21st Century Learning are no different than those of the 20th Century. Specifically, foundational, meta, and humanistic knowledge, such as core content knowledge, problem-solving, and life skills, are just as important now as they were in the past and as they will be in the future. It is pedagogy that most needs to change in response to the most recent technological advances (Kereluik et al., 2013).

21st Century Schools (2017) detailed specific, practical, and illuminating differences between pedagogy in a 20th Century classroom and a 21st Century classroom. Some of the differences include a teacher-centric classroom versus a student-centric classroom, working in isolation versus working collaboratively, knowledge, comprehension, and application versus synthesis, analysis, and evaluation (21st Century Schools, 2017).

With the combination of timeless learning values and modern pedagogy in mind, I attempted to adapt an existing lesson plan to engage students in 21st Century Learning. However, since all my existing lesson plans were of the 20th Century variety, most of them teacher-centric show-and-tells followed by work on individual assignments, I ended up mostly redesigning my lesson plan from scratch. To the pair of learning goals that I retained–core content knowledge and problem-solving–I added digital & information literacy, life skills, and communication. Pedagogically, I shifted to student-centered, research-driven, and collaborative learning.

The lesson plan is centered around a collaboration by students to draw a new emoji, create a blueprint of it, identify necessary data, and to research the python commands they will need to use to finally code the emoji. At different parts of the process, each student will have the opportunity to lead or take on supporting roles.

The research-driven part of the lesson plan requires students to apply their basic understanding of how python functions work and discover and experiment with new python functions that they have never used before. Programmers do not have an encyclopedic knowledge of programming languages they know, but rather look up exactly how a programming language represents basic CS concepts as needed. My students will have the opportunity to do the same in this lesson plan to practice information literacy.

Lastly, to practice their communication skills, students will need to present a retrospective of each step in the process, reflecting on what worked and what did not, writing down and video taping their thoughts. This also creates a cross-disciplinary opportunity to employ their English Language Arts writing skills in a coding class.

Designing this lesson plan was challenging. Instead of being able to use the 20th Century pedagogical knowledge and skills I gained through four years of on the job experience, I had to start from scratch and will be learning on the job, yet again, while trying out new and unfamiliar 21st Century pedagogical methods. Just as when I first started teaching, I am sure to discover misconceptions about how I think 21st Century Learning pedagogy should be instantiated! And to think, I only had four years of teaching habits and biases to overcome; how much more difficult is it for a teacher with a long career to adapt new ideas?

As promising as 21st Century Learning may be, what if there are as yet unidentified components to 21st Century Learning that are based on factors other than technological change, such as economic change and the increase in income inequality? Or environmental changes and the increase in climate disruption? What if there needs to be an entire category dedicated to political knowledge to effect societal change? Thus, as with all things new, there is a delicate balance that needs to be struck between considering 21st Century Learning with a critical eye but also with cautious optimism.

Perhaps that is the part of our job as educators that will never change: to carefully research and analyze current trends in education, to be unafraid to experiment with new ideas, and to expect and embrace change and the never-ending onward march of progress.


21st Century Schools. (2017, January). Is your school of classroom 21st century? Retrieved from
Applied Educational Systems. (2019). What are 21st century skills? Retrieved from
Jerald, C. D. (2009). Defining a 21st century education. Center for Public Education. Retreived from
Kereluick, K., Mishra, P., Fahnoe, C., & Terry, L. (2013). What knowledge is of most worth: teacher knowledge for 21st century learning. Joural of Digital Learning in Teacher Education, 29(4), 127-140. doi:10.1080/21532974.2013.10784716
National Education Association. (2019). Partnership for 21st century skills. Retrieved from
Partnership for 21st Century Skills. (2019). Framework for 21st century learning. Retrieved from

I Don’t Need Jack: NLP, Part Two

By far the biggest cost and concern when it comes to changing a car’s oil is having to purchase a jack, jack stands, and wheel chocks. Scouring the internet led me to what seemed like a viable alternative: the oil pump extractor (Bergsma, 2013).

After spending some time researching oil pump extractors and pricing some out, I made sure to check they would work with my make and model. They do not (me73, Plastixx, brucep, 2018)!

I had one last recourse: changing the oil in my car without jacking up the car. Would there be room to squeeze under the car?

Is there room? Finding out if there is enough clearance under the car to do an oil change.

Phew! There is room!

I repriced the cost of changing my own oil:

  • 5.1 quarts of 0w-20 full synthetic oil: $25.49 (Napa, 2019)
  • Oil filter: $8.97 (, 2019)
  • fumoto valve plus plastic clip: $25.95 (, 2019)
  • oil pan: $3.99 (Napa, 2019)
  • funnel (purchased previously)
  • Total: $64.40
  • One-time Costs: $29.94
  • Recurring Costs: $34.46

Based on the cost of my last oil change, which was $75, I should be able to save $1165 over the course of 100,000 miles.

The final piece of research I did was to determine how to dispose of the oil and any oil stained items. The oil I can mostly likely recycle at NAPA (Bryant, 2019). Oil stained materials, also known as absorbents (“The Ultimate Guide,” n.d.), can be disposed of in Oregon in the landfill (Wastes Requiring Special Management, 2019).

I think I am ready!


Bergsma, K. [Mercedessource]. (2013, October 11). Engine oil vacuum extractor shootout part 1 with Kent Bergsma [Video file]. Retrieved from
Bryant, J. (2019, July 17). Know how notes – 10 oil change tips [Blog post]. Retrieved from
me73, Plastixx, brucep. (2018, December 22). Extractor disappointment with the 3.0 [Online Forum Thread]. Messages posted to
The Ultimate Guide: How to Dispose of Used Rags and Oil Absorbents. (n.d.). Retrieved from
Wastes Requiring Special Management. OR DEQ § 340-093-0190 (2019). Retrieved from

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