In need of a Base Case
Comments about coding, research and life

In preparation for writing the introduction/motivation section of a research proposal I’m reflecting on some previous work in assessing naive understanding of computing concepts. One of the articles recommended to me by Sally Fincher at ICER was the Commonsense Computing series. The first paper in that series looked at students naive concepts of sorting.

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

This morning I read an article about Sarah Fine and the reaction to her decision to leave an inner city DC school. No one questions that Sarah is an exemplary teacher, and her decision prompted both outrage that she should “abandon” the class as well as understanding.

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.

Earlier this morning Marcia Linn gave the Keynote at ICER entitled “Learning to Teach Computer Programming”. The work that she talked about, while containing some historical perspective about teaching computer science, was mostly about a new report “Fostering Learning in the Networked World: The Cyberlearning Opportunity and Challenge” and two initiatives: Computational Thinking and 21st Century Skills Movement.

I have not read the Cyberlearning report, so I do not have a lot to comment about it.

As far as the Computational Thinking and 21st Century Skills movement - first I was very happy to hear the “21st Century Skills” agenda introduced at a computer science. She even gave a link to the 21stcenturyskills.org website and showed their “rainbow” curriculum model.

Marcia showed us a simulation from the WISE collection of Science simulations and tried to model how this was a computational thinking/21st Century Skill activity. (It was about global temperature and you could control the amount of C02 that was added to the environment) I was not convinced that it was truly a computational thinking activity. One of the features of computational thinking that I was struck by the first time I heard Jeanette Wing speak about it was the idea that Automation was one of the three key aspects of computational thinking. Its not just about looking a representation of information, but it is about somehow automating some process. The WISE collection of activities is great, but I’m not sure its really computational thinking.

Marcia also talked about a cycle of knowledge building that can be used through a tutor or electronic environment where students go through a 4 stage process of generating ideas, adding ideas, evaluating those ideas and finally sorting the ideas based on the evaluation. This reminded me a little of the misconception research that says you need to expose student’s misconceptions in order to move past them, however it was unclear how incorrect ideas in this process would be “weeded out”.

Still processing what my take away from that talk will be.