First of all the standards are not billed as STEM, but “Science Education”. There has been a lot of buzz in the past year of how we have aligned ourselves with the mathematics community - to the point that the new national standards in Mathematics list CS as a potential 4th year course in its sequence. Can we offer outrage that “computer science” concepts are not included in the K12 science standards if we are also making the argument that we belong with the math folks?

We need to decide what we are. Are we math? do we belong in math? are we science? are we our own discipline? Because if we argue for belonging to everyone we end up looking like a 10 year old who argues that they should get chocolate sauce on EVERYTHING! Because chocolate sauce is important. The adults know that they can eat a lot of things without chocolate, and have done just fine without chocolate, so even though the 10 year old says they will DIE without chocolate, our own experience teaches us otherwise.

Now in a health model, where the conditions for what makes us healthy do not change, and reflecting on our own experience and things that we were taught years ago is viable, however our society and economy is a moving target. Yet, even then the 10 year old’s argument would still appear to be self-serving and frivolous. Now imagine the 10 year old wanted something less popular. Something like coconut-pomegranate sauce. You and I may not even believe that anybody would like the sauce in addition to not believing its necessary. And this is where we are.

Regardless of my own personal opinion of how we should move forward - this is the circumstances we find ourselves.

What is my opinion? Stop beating up the math and science educators. They have enough content to try and deliver to students as it is. They work hard and retraining ALL of them to correctly integrate appropriate and accurate computer science into their classes would be costly and meet with lots of resistance (both from the educators themselves and from a good portion of the CS community who believe that in order to teach any CS you need a bachelors degree in cs).

Let’s make use of another opportunity. Lets push for the literacy course in computing. Let’s shift the computer applications course to computing. Yes that means that at the K8 level they will learn some computer applications - but they need that! and use of tools is the first step to understanding the tools themselves. Spreadsheets can give us some early models of computation - especially if they are used smartly and the projects are designed with care - does anyone debate that we could teach prediction models and simple machine learning fundamentals in excel? And guess what - that aligns with the modeling and simulation strands that are showing up in the new math and science standards - look! the course may even get support from other teachers for such a curriculum if students could then use those skills in their other classes.

We are our own discipline. We should not have to piggy back onto another in order to achieve our own goals. We also need to pay attention to the two economic models of schools - student time (seat time/learning time) and money (teacher salaries and training). Ideally would I wave my hand and have CS fully integrated throughout the curriculum? sure. Do I still think we would need our own class to teach certain skills explicitly? as a cognitive scientist - absolutely (they will never learn it otherwise). Have I found the correct incantation and hand waving pattern yet? no. And so I style myself a realist, and try to use my experience in addition to my overall desires to evaluate the movement to get more students exposed to rigorous computer science and give all students an idea of what CS is.

Stella’s work on conceptual change is built on the foundational idea that by the time kids start primary school they have naive constructions of knowledge. These constructions are built from students’ interactions with everyday life and can be found echoed from their home environment and culture. The models that children and novices build of the world are based in ontological and epistemological presuppositions.

The naive models that students/children hold can be described as observation and cultural information about the phenomena, beliefs held by the child, and mental models of the interactions of the artifact with the world.

Stella works within a framework theory[1] which says that knowledge is built into frameworks or models, and that significant conceptual change occurs when the framework itself is restructured. Stella has done a lot of work with children’s understanding of the day/night cycle and also of the seasons. She has worked to categorize the types of misconceptions that students have about day/night based on several different models of the earth and sun.

When we run into trouble as educators is when we assume that by simply presenting students with new information that we can break apart a strongly held framework and restructure their model of interaction. Often students see things learned in school as inert knowledge - information that does not have any application to everyday life (when am I going to use this?)

Recently, in a study of software engineering misconceptions of students here at CMU I encountered situations where students believed both a misconception and its inverse to be true.
This implies to me that they are in the fragmentation stage of conceptual change - they are beginning to see that restructuring their framework is necessary and are either constructing a secondary framework, which will overtake the first (incorrect) framework, or replace it completely. (We are not sure if the original framework is restructured to the new, or if a completely new one is built and then becomes the dominant structure called upon when reasoning.)

How does this fit into my other work? I am looking at naive understanding/mental models of data and how that impacts students’ ability to reason about and write code for problems. I believe that our students have particular naive models and those models are the framework which they use to reason in our introductory courses until we provide them with new reasoning points (think bottle cap arrays or analogies[2]).

More to come…

[1] Vosniadou, S., “Capturing and Modelling the Process of Conceptual Change”, Learning and Instruction, Vol. 4, pp. 45-69, 1994

[2] Meyer, R., “Models for Understanding”, Review of Educational Research, Vol. 59, No. 1, pp. 43-64, 1989

This past year the blogging has significantly declined because I have shifted my focus from reading about others work to conducting some of my own. Here are some of the projects I’m working on right now:

* Naive models of Looping and Instructional Benefit: I am currently getting my feet wet in exploring conceptual change in a fairly constrained domain looking at for and while structures and mapping people’s naive predilection for one type of loop syntax over another yields benefits in learning gains. I’ve constructed a small cognitive tutor and using a within subjects crossed design to explore the question. Data still incoming..

* Misconceptions in Software Engineering: I’ve completed a project with a SE PhD student looking at the misconceptions students at CMU have in regards to software engineering and offering a novel assessment methodology that measures not only the misconceptions but their strength as well.

* Animation vs. Static Instruction in young children with the day/night cycle: This is an interdisciplinary project I am completing with a psychology student where we are looking at the effect of static (still images) vs. animated instruction in 2nd and 3rd graders with regards to conceptual change and misconception dispersal.

I’m now starting to think about the direction for my thesis research and to that effect am starting to read again. Expect a number of posts about some talks from last week, as well as conversations I have had and readings I am doing.

Well, heres to a good start to what I hope will be a productive summer!

“How we need to learn includes using the technology that professionals in various disciplines use. Professionals routinely use the web and tools such as wikis, blogs, and digital content for the research, collaboration, and communication demanded in their jobs. They gather data and analyze it using inquiry and visualization tools. They use graphical and 3D modeling tools for design. For students, using these real-world tools creates learning opportunities that allow them to grapple with real-world problems – opportunities that prepare them to be more productive members of a globally competitive workforce.”

There are some good beginnings to computational thinking here - the use of modeling tools (btw - 3D modeling tools - that would be great for the Alice team..) and inquiry and visualization.

Now to put my head back down into the research work. Sorry for the lapse in posting, but trying to move forward. I’ll try to do better.

I dont think anyone would argue that there are wonderful programs out there, mostly because of the FANTASTIC teachers who run them. But we have yet to figure out how to bottle that to grow that model into new schools, or schools where the program is waning.

Within the data from the NEAP report you can see that the computer-related course taking has actually increased. I’m not sure how they judged computer-related however. There are two coding areas from the CSSC course codes that were used that apply to computer science - Both 07-03 (07 is the Business heading and 03 is the particular part which includes CS) and the entire section of 11 (although this also includes courses like “computer appreciation”, “computer math”, Introduction to the Internet”, as well as APCS.

Unfortunately neither of those subjects are available using the online data tools. I should make a request to get at the data and look at what happens when you remove the “applications” course codes from the data and re-calculate the graph on the bottom of page 9 of the publication.

More about this, I promise..

This course raised the bar for computer science education, both in expectations on the students and in the teaching of the course itself. Before entering this class, I thought education was synonymous with communication, and that to be a good educator it sufficed to refine clarity in communication. After taking this course, I am amazed at my naivete. I now have an appreciation for assessment, metacognition, motivation, engagement, cooperative learning, cognitive architecture and many other concepts that contribute to good teaching.

This was written in one of the online course evaluations that are done at the university level and I think it sums up a resolution of my goals for the course.

Why do we applaud things? We applaud them because they are unusual, because they are exceptional, or in appreciation. As things become common place we are less likely to applaud what would have caused us to applaud early on. Have we stopped applauding computer science?

New developments, new applications, new inventions - while applauded within our own community do not often make the larger news in current times. I wonder what affects that has?

Just some quick notes - more about Koli perhaps on the way home.

So to start, here is my comment on Alfred’s blog, with more, new comments below:

::sigh::

Forgive me for being an education purist, but I think this is part of the reason why we are in the pickle that we are in as a discipline. Relying on tools or the industry to define the introductory experience, and even more so the novice experience is .. well.. crazy? ineffective? ridiculous? not sure what the right word is.

When the biology department tries to decide what biology knowledge makes someone a literate citizen they don’t look at what professional biologists are doing. They don’t look at what nifty tools are available for teaching biology. They decide what it should mean for your average American to have an understanding of biology. They define a set of broad standards that would allow a knowledgeable teacher to implement those standards in a classroom in one of many different ways, potentially using one or more of the tools available to them. Then they train the teachers. To BOTH have an understanding of biology AND an understanding of what it means to teach biology - both to be familiar with the standard curriculum and the pedagogical nature of the course.

How is this different from what we do? We argue about what the right “foundation” is. College CS people argue about why AP is not good, but the AP program is one of the few programs with a solid curriculum, that does TEACHER TRAINING. While there are great things being done in a few places, its often hard to find something (outside of AP workshops) that a new/novice teacher can hold on to, and take back to their administrator as “standard”.

We need to stop arguing about language, tools, etc. and decide what it means for the AVERAGE American to be literate in computing. (notice the computing - not the computer science, its a bigger question than just CS) Then we need to write a set of standards, or agree that the ACM’s standards are good. There needs to be consensus in the message coming out from our community. Then there are the economics to consider. How are schools going to pay for this?

and now for the new part

It really does boil down to economics in the long run. We can train 10,000 new teachers, and ACM can publish excellent standards that are used by every CS teacher in schools, but unless we somehow make it cost beneficial or cost neutral to schools - those 10,000 teachers (for the most part) will not find a job unless they are filling a retiring teachers spot. There are two types of economics going on here. First, the money issue, how in the world are schools going to afford to pay for a new teacher for a course that is not required, and currently doesn’t exist in the school so there is no demand for it?

Secondly, the economics of the student’s schedule. Students’ days are currently full. Any high school teacher can tell you that. The days we remember of study halls in high school or “free periods” are a thing of the past. In the competitive college market students have taken to PACKING their schedule with as much as the possibly can, with the largest percentage of AP classes they can to try and get into the best schools with the best scholarships. As of right now, college admissions officers give no more weight to AP CS than any other ELECTIVE AP. So why take CS, when you can take a language, art, music or one of any other AP courses? And if it is not an AP course why take it when an AP course has more weight on your transcript?

So, there is no money to hire teachers for this elective course. And there is no student demand for the course. How does picking a better tool fix the economic problem we are facing? How does writing a better textbook fix this? Yes, it may change what is happening in colleges, or in a few established HS programs - but there are bigger issues here.

I have some ideas about this, but to save time I’m going to leave this with the questions. (Machine Learning - homework calls) But as we consider whats going to make the LARGE changes, in the location that we all agree is where it needs to be (K12), the economics are undeniable and unavoidable. Lets keep those in mind too.

First let me say I approve of the work and most of the methodology that was applied. I’m in the middle of my own process for writing research proposals which are a combination of drawing on similar work and also exposing weaknesses in that work with motivate your own work. This blog is not meant to be critical, just observant of some of the things in the paper I feel were important for me to think about.

First of all, their selection of students (first day of a computer programming class) may already introduce a bias into the student’s answers. Because students have already enrolled and showed up to a class which is generally understood to exist in a computational setting, that may have shifted the student’s perceptions in that direction. Is that necessarily a bad thing? I dont know, after all we teach them these concepts in a computational setting. One of the things I’m hoping to explore in my own work to help provide a measurement of how much the questions get answered by “what they think is the right answer, or what the teacher wants to hear” is the idea of attachment strength. If anyone knows of an article that talks about attachment strength to a model I am looking for a good citation for the paper. (as well as a little help in designing that part of the assessment)

The second to last paragraph in the introduction states “The results suggest that beginners can describe algorithms, but their models of the machine and instructions differ from those of many instructors. In particular, the results suggest that instructors should guide students to understand a virtual machine in which numbers are primitive objects …” This lead to a note in the margin that reads: Are the students’ models “wrong” or just abstracted at a different level? Why should we guide them towards a NEW model rather than providing insight into their own?

The paper states in a couple of places that one of the goals of this work is to inform better instruction based upon the findings, but the largest instructional change recommended was to shift from while loops to do-until loop structures. Any intuitions expressed by the student that appear to represent a different level of abstraction were labeled as a misconception and it was recommended that the instructor work to move the student to the instructors way of thinking. While this is probably the correct response in most cases, I’m wondering if it is right in all. Are there cases when a misconception is based upon a different level of abstraction where we should simply introduce students to the concept at their abstraction level and then progress to a deeper one over time? I guess this is one of the broader questions I hope to address in my work. hm.. Any comments would be much appreciated. Even if they are based on naive models

It reminds me a little of some of my own challenges - when I first decided I wanted to teach, that it was important to me I had to justify this decision with several people - including my then significant other who thought that was a waste and that I should go into the computer industry. The number of times I heard the phrase “those who cant, teach” made me question my decision to the point where I actually interviewed with several companies for systems administrator positions.

I started teaching in an inner city school in Ft. Lauderdale FL. 2000+ students and I was a minority. I had a student threaten to turn his gang on me for trying to enforce school policy. (that was on the second day) I did eventually leave that place due to the communities reaction to a tragedy that occurred. I did keep teaching, but I moved to a suburban school district with different problems.

I was burned out after two years, and if I hadnt had the option of moving to a better district I’m not sure how much more I would have lasted through. Now, as I hope to make a change, to contribute to a community of educators in a different way I again face questions. Only this time its along the lines of “why are you leaving? your students need you”.

It feels like a double standard. First teaching isnt good enough for intelligent, hard working people (clearly they should do something with better options for success) and yet, when they can take no more of the lack of support and difficulties that Sarah describes they are berated for leaving. We need to let our teachers know we value them - right from the time they decide to be a teacher. Maybe then more smart, excited people will pursue and stay with teaching.

If you have a teacher in your life (either a colleague or one of your childrens’ teachers) who is doing a good job, let them know you are glad they made the decision long ago to be a teacher. One comment like that used to get me through a whole semester.