Posts tagged ‘PCK’

Why We Need Learning Engineers and Faculty who Know Learning Sciences

I agree strongly with the idea of “learning engineers.” Having learning engineers doesn’t relieve faculty who teach from the responsibility to learn more about learning sciences (see my blog post about testing teachers about PCK). Just teaming up subject-matter experts with learning engineers does not inform a teacher’s day-to-day and in-class decision-making.  The general theme below is one I strongly agree with — we should rely more on evidence-based and research-based teaching.

We are missing a job category: Where are our talented, creative, user-­centric “learning engineers” — professionals who understand the research about learning, test it, and apply it to help more students learn more effectively?

Jobs are becoming more and more cognitively complex, while simpler work is disappearing. (Even that old standby, cab driving, may one day be at risk from driverless cars from Google!) Our learning environments need to do a better job of helping more people of all ages master the complex skills now needed in many occupations.

I am not suggesting that all subject-matter experts (meaning faculty members) need to become learning engineers, although some might. However, students and faculty members alike would benefit from increased collaboration between faculty members and learning experts — specialists who would respect each other’s expertise — rather than relying on a single craftsman in the classroom, which is often the case in higher education today.

via Why We Need Learning Engineers – The Digital Campus – The Chronicle of Higher Education.

May 27, 2015 at 8:46 am Leave a comment

Student Course Evaluations Can’t Measure Teacher’s Knowledge: But We Could

It’s that time of year when Deans and Chairs start prodding students and teachers about course evaluations. What do the students think about their courses? What do the students think about their teachers?

There is a significant body of evidence that suggests that course evaluations are a stable measure about the teachers themselves. For example, the scores for a teacher are consistent across instantiations of the course over time (see Nira Hartiva’s work). However, they still might not be measuring something that we consider significant about teaching.

For example, it’s a mistake to think that student course evaluations tell us what a teacher knows about teaching. The teacher’s pedagogical content knowledge is invisible to the student. The student only sees what the teacher has decided to do to interact with the students. The student can’t see the knowledge that the teacher used in making that choice.

Three kinds of knowledge that are particularly relevant to a CS teacher are:

  • Knowledge about how to teach. A good teacher knows more than one way to teach a particular subject, and knows to change methods for a given student or to change the pace of a class. When I see students driving away in the back of my class, I know that it’s time for a Peer Instruction activity.
  • Knowledge about misconceptions. As was shown in Phil Sadler’s exceptional study (see blog post), a characteristic of teacher expertise is knowledge about what students typically get wrong. Based on that knowledge, teachers can address those misconceptions, and lead students to discover and correct the misconceptions themselves.
  • Knowledge about how to broaden participation in computing, which is particularly relevant to CS teachers. These include how to teach avoiding stereotype threat and triggering the imposter phenomenon, about how to give everyone a voice in the class and not let the loud and boisterous define the teacher’s perceptions of the course. I can offer a negative example, taken from real life but might have been invented after reading the negative examples in Unlocking the Clubhouse.

Teacher: How many of you students had Python in a previous class?
(Most students raise their hands, since it’s the language used in the pre-requisite class.)
Teacher: Well, you learned a terrible language. You’ll have to forget everything you know if you want to pass this class.
(Every student suffering imposter syndrome at this point decides to drop.)

This teacher actually has quite high course evaluation scores — and double the drop rate of every other teacher of that class.

Pedagogical content knowledge (PCK) is the key difference between novice and expert teachers, but is invisible to students. This is another reason why student evaluations of teaching (aka, Student Reviews of Instruction (SRI)) are inadequate as measures of teaching quality. They can’t measure a key indicator of teacher expertise.

I’ve been wondering how post-secondary teaching might change if we were to take a PCK perspective seriously. The knowledge of good teaching is definable and measurable.

  • We might define courses not just in terms of learning objectives but in terms of what knowledge the teacher should have to teach the class effectively.
  • We could evaluate University and College teachers based on their PCK — literally, taking a test on what they know about teaching the class.
  • PCK tests would finally create an impetus for University and College faculty to pursue professional development — that’s where they’d learn the teaching methods, student misconceptions, and methods for encouraging BPC that they need to answer the PCK tests. One might even imagine teachers taking a class on how to teach a new class that they’ll be offering in the future — preparing for a course by developing expertise in teaching that course. What an interesting thought that is, that higher education faculty might study how to teach.

April 20, 2015 at 8:30 am 33 comments

Learning to Teach Computer Science: The Need for a Methods Course : CACM

Pedagogical content knowledge (PCK) is the knowledge that teachers have about teaching specific content. “How People Learn” suggests that it’s much more important for student learning than general teaching knowledge. To create credentialing for CS, we need to offer CS methods courses that teach CS PCK. Aman Yadav and Tim Korb teach one of these courses at Purdue, and have an article in this month’s CACM on how it works.

Learning to teach can be conceptualized around four main ideas—learning to think like a teacher, learning to know like a teacher, learning to feel like a teacher, and learning to act like a teacher.7 These knowledge systems are developed with a comprehensive understanding of the subject matter to be taught as well as ways of teaching that subject matter, that is, pedagogical content knowledge. Teachers with in-depth pedagogical content knowledge understand ways of representing and formulating the subject matter—using powerful analogies, illustrations, examples, explanations, demonstrations, and so forth—to make it understandable to students.13 These teachers also know which topics students find easy or difficult to learn, which ideas (often misconceptions) students bring with them to the classroom, and how to transform those misconceptions. In addition, teachers understand how students develop and learn as well as how to teach diverse learners.

A methods course is typically where prospective teachers are introduced to this skill set and learn about “pedagogical ways of doing, acting, and being a teacher.”1 This knowledge is developed within the context of learning and teaching a particular subject area. Transforming Ball’s statement about mathematics to computer science implies that a computer science methods course is about how computer science is learned and taught, and about how classrooms can provide an environment for learning computer science.

via Learning to Teach Computer Science: The Need for a Methods Course | November 2012 | Communications of the ACM.

November 2, 2012 at 7:11 am Leave a comment

Blog Post #999: Research Questions in Computing Education

The 999th blog post feels like a good point to think about where we’re going.  Here’s how I define the big question of computing education research:

Computing education research is the study of how people come to understand computing, and how we can make that better.

But that’s the big question.  There are lots of research questions inside that.  Here are some of the ones that I’m intrigued by.  This is an overly-long blog post which I’m using as a place marker:  Here’s what I’m thinking about right now at the end of the first 1000 blog posts.  Skip around to the parts that you might find interesting.

What are the cognitive processes of learning to program?

Why is learning to program hard? The empirical evidence of teaching computer science suggests that it is. Failure rates worldwide of 30-50% in the first class have been reported for decades. The misconceptions and challenges that students faced in Scratch in Israel (ITICSE 2011) are quite similar to the same ones documented in Pascal at Yale in the 1980’s (Soloway et al.).

Are there cognitive challenges to learning programming that are unique among other disciplines? Perhaps so. Consider these two possibilities:

  • Agency: Writing a computer program is the task of providing instructions to another agent to execute, but a non-human agent. Miller in 1981 found that humans found it hard to describe task processes to another human, and the produced instructions required human understanding to interpret them. People do not naturally produce instructions at a level detailed enough for a computer to execute.
  • Time: A program uses a variety of notations to compress time, e.g., iteration and recursive constructs. These notations describe a process in brief which will execute repeatedly many times (perhaps millions of times). We know that these notations are among the most challenging for students to grasp.

Both agency and time notations are unique to the challenge of programming. Perhaps these factors (among others) help to explain why programming is so hard, and understanding these challenges will lead to new insight into how humans conceive of agency and time.

Where do problems/difficulties/misconceptions in learning programming come from?

Most students have no experience in programming computers before they enter their first computer science class.  So, no prior conception of assignment, memory allocation, WHILE and FOR loops, linked lists, or recursion — yet these are way up there on the list of things that are hard about learning to program.  They haven’t changed in decades, across multiple languages.  Where did those problems come from?  Do we teach them wrong?  Exactly where so that we can fix it!  Do students have some prior knowledge that is interfering?  What knowledge are students bringing to bear in learning to program?

Can we teach computing without a programming language?
Can someone learn what a computer is, how it works, and what its limitations are simply through non-programming activities?

Mathematicians did. Turing defined what a computer is, without a programming language. Instead, he defined a machine and a language.

I’m increasingly coming to believe that those are outliers — Turing and mathematicians who figure out computing without a computer are unusual, and we can’t do that at-scale.  Learning to understand computing is learning to understand a notional machine (duBoulay), to construct a mental model of how you expect the notional machine to work (Norman), and that mental model consists of decontextualized parts (deKleer and Brown).  It’s very hard to think about those parts without having names or representations of them.  It can happen, but it takes enormous cognitive effort.  It’s not going to be effective and efficient to reach our learning goals without a language.

Challenges for CS10K

The CS10K effort (to have 10,000 high school teachers capable of teaching CS:Principles in 10,000 US high schools) requires answers to some significant research questions. Some of these include:

What kind of pedagogy will fit into the lives of in-service high school teachers and other working professionals?

Computer science pedagogy today is mostly apprenticeship-based: Students get a bit of instruction (perhaps some modeling of good behavior), and then are expected to learn through doing, by programming in front of an IDE. While the apprenticeship-based model is effective, it’s inefficient if the goal is understanding about computer science, as opposed to expertise as a software engineer.

In-service high school teachers are a particularly challenging audience. Most likely, they will never be professional software engineers, and they are full-time (overworked) professions, so they have neither the motivation nor the time to engage in apprenticeship-based learning. How do we teach CS to these teachers in the small bits of time that they have available?

How do we create sufficient, high-quality on-line materials to lead to successful CS learning at a distance?

The best distance learning programs in the world (such as the Open University UK) rely significantly on text-based materials, because we know how to control costs while creating and maintaining high-quality content. CS is not best taught with printed text, since visualizations and simulations play a key role in student learning. How do we create sufficient (e.g., at reasonable cost), high-quality materials to support CS learning at a distance?

What will motivate high school teachers to take classes in computer science, to be engaged with the content, and to sustain their interest?

The existing CS teaching programs in the United States are woefully undersubscribed, e.g., Purdue’s CS methods course has never had more than one student enrolled each term that it is offered. What will drive more teachers into CS education?

What do teachers need in order to develop into successful computer science teachers?

High school teachers will not need to be professional software engineers. They do need to be able to present CS ideas, to assign and assess student work, and to mentor, e.g., to help facilitate student debugging and guide development. What are the learning objectives for CS high school teachers? How do we assess that development?

CS PCK: What is Computer Science Pedagogical Content Knowledge?
In most disciplines, there is a body of knowledge of how to teach that.  How People Learn has a whole chapter on domain-specific teaching practices, and points out that those are much more powerful for effective teaching than domain-general teaching practices.  For example, science educators explain how to support inquiry-based learning, and mathematics educators know how to build on innate understanding of number.  We call that knowledge pedagogical content knowledge.    How do we best teach computer science?  How do we help future educators develop the unique skills to teach computer science?

May 3, 2012 at 6:16 am 18 comments

We used to know how to teach CS in Logo

When I get into conversations about CS pedagogical content knowledge (PCK), I realize that Gary Stager is right (below).  We used to have CS PCK.  I remember “Ask three before me” and Logo songs and all the other terrific techniques that were created for teaching about Logo.  I remember square dancing at the East Coast Logo Conference, as a way of talking about procedures for turtle movements.  What happened to all those techniques?  Why did we lose them?  Gary thinks that we lost the battle when “technology” became a synonym for “computing.”  He may be right.  I also think Seymour got it right when he talked about how schooling carefully removed computing from the curriculum.  In any case, it’s a shame that we are now recreating what we once had.

Although I’m only 48, I have been working in educational computing for thirty years. When I started, we taught children to program. We also taught tens of thousands of teachers to teach computer science to learners of all ages. In many cases, this experience represented the most complex thinking about thinking that teachers ever experienced and their students gained benefit from observing teachers learning to think symbolically, solve problems and debug. There was once a time in the not so distant path when educators were on the frontiers of scientific reasoning and technological progress. Curriculum was transformed by computing. School computers were used less often to “do school” and more often to do the impossible.

Don’t believe me? My mentor, Dan Watt, sold over 100,000 copies of a book entitled, Learning with Logo in the 1980s when much fewer teachers and children had access to a personal computer.

Things sped downhill when we removed “computing” from our lexicon and replaced it with “technology” (like a Pez dispenser or Thermos). We quickly degraded that meaningless term, technology, further by modifying it with IT and ICT. Once computing was officially erased from the education of young people, teachers could focus on keyboarding, chatting, looking stuff up, labeling the parts of the computer and making PowerPoint presentations about topics you don’t care about for an audience you will never meet. The over-reliance on the Internet and the unreliability of school networks ensures that you can spend half of each class period just logging-in.

via Dumbing Down : Stager-to-Go.

April 7, 2012 at 1:37 pm 22 comments


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