Posts tagged ‘educational psychology’
Source of the “Geek Gene”? Teacher beliefs: Reading on Lijun Ni, Learning from Helenrose Fives on teacher self-efficacy
I discovered the below quoted post when I was looking up a paper by my former student, Lijun Ni. It’s nice to see her work getting recognized and reviewed! I talked a lot about her work when I was talking to PhD students at the University of Oldenburg program — Lijun has studied the beliefs of CS teachers, and that’s super important.
One of the other international guests at the Oldenburg program I attended last month (see post here) was Helenrose Fives who has literally written the book on teacher beliefs (see Amazon reference). Several of the PhD students who presented their research talked about student teachers having lower self-efficacy after actually being in the classroom, less commitment to ideals like inquiry learning, and less belief that students can learn. Helenrose said that that’s really quite common. Teachers have a high level of self-efficacy (“I can teach using novel approaches that will really help students learn!”) before they enter the classroom, and that sense of self-efficacy falls off a cliff once they face the reality of the classroom. The self-efficacy rises over time (up and down, but mostly up) but never reaches the optimism of before teachers enter the classroom.
I talked to Helenrose about what her work means for University CS teachers. In general, the work she describes is about school teachers, not faculty. She agreed that it’s possible for University CS teachers to have high self-efficacy even if they are not successful teachers, because University teachers define self-efficacy differently than school teachers. School teachers are responsible for student learning. They know individual students. They actually know if they are successful in their teaching or not (in terms of student learning and engagement). University teachers tend to have larger classes, and they tend to teach via lecture. They usually have little knowledge of individual student learning and engagement. Their sense of self-efficacy may arise from their ability to succeed at their task, “I can give great lectures. (Almost nobody falls asleep.) I can manage huge classes.” Where they do have knowledge of learning and evidence of ineffective teaching, they may simply decide that it’s the student’s fault. Perhaps this is where the Geek Gene is born.
Here’s a hypothesis: If a University teacher has high self-efficacy (great confidence in his or her teaching ability) and sees evidence of students not learning, it’s rational for that teacher to believe that the problem lies with the students and that the problem is innate — beyond the ability of the teacher to improve it.
In the first study, Ni interviewed teachers about their identity in order to establish what strengths and weaknesses are common in high school computer science teachers. She found that the teaching identity of computer science teachers is largely underdeveloped compared to teachers in other fields, and that often computer science teachers prefer to identify as a math teacher or a business teacher, rather than a computer science teacher.
Further, she found that high school computer science teachers generally do not have any sort of teaching support community to turn to, because they are often the only computer science teacher at their school.
All of these problems combine to keep computer science teachers from developing a strong teaching identity centered in the computer science field. Instead, we have teachers with low commitment levels to the field training our next generation of programmers in basic computing skills that are generally unrelated to the field of computer science itself.
Annie Murphy Paul has a nice article about autodidacts — yes, there are some, but most of us aren’t. MOOCs are mostly for autodidacts. The paper from Educational Psychologist is excellent, and I reading the original as well as Paul’s review.
In a paper published in Educational Psychologist last year, Jeroen J.G. van Merriënboer of Maastricht University and Paul A. Kirschner of the Open University of the Netherlands challenge the popular assumption “that it is the learner who knows best and that she or he should be the controlling force in her or his learning.”
There are three problems with this premise, van Merriënboer and Kirschner write. The first is that novices, by definition, don’t yet know much about the subject they’re learning, and so are ill equipped to make effective choices about what and how to learn next. The second problem is that learners “often choose what they prefer, but what they prefer is not always what is best for them;” that is, they practice tasks that they enjoy or are already proficient at, instead of tackling the more difficult tasks that would actually enhance their expertise. And third, although learners like having some options, unlimited choices quickly become frustrating—as well as mentally taxing, constraining the very learning such freedom was supposed to liberate.
A recent article in Slate (see here) suggests that practice may not lead to expertise, that the “10,000 hour rule” is wrong. The “10,000 hour rule” was popularized by Malcolm Gladwell in his book Outliers (see excerpt here), but really comes from an important paper by K. Anders Ericsson and colleagues, “The Role of Deliberate Practice in the Acquisition of Expert Performance.” Ericsson claimed that 10,000 hours of deliberate practice results in expert-level performance.
The Slate article is based mostly on a new meta-analysis (see here) by Macnamara, Hambrick (also a co-author on the Slate article), and Oswald which reviewed and combined studies on expertise. They found that practice always was positively correlated with better performance, but did not explain all of (or even most of) the difference in expertise between study participants. The Slate article authors suggest, then, that deliberate practice is not as important as genetics or innate talent.
Deliberate practice left more of the variation in skill unexplained than it explained…There is now compelling evidence that genes matter for success, too…What all of this evidence indicates is that we are not created equal where our abilities are concerned.
The paper and article make two big mistakes that leave the “10,000 hour rule” as valid and valuable. The first is that practice is not the same as deliberate practice, and the second is that the fallback position can’t be genetics/innate talent. In general, their argument hinges on practice hours all being of equal value, which shows a lack of appreciation for the role of teaching.
Practice is not the same as deliberate practice
Ericsson was pretty clear in his paper that all practice is not created equal. Deliberate practice is challenging, focused on the skills that most need to be developed, with rapid feedback. (Here’s a nice blog post explaining deliberate practice.) Simply putting in 10,000 hours of practice in an activity does not guarantee expertise. Ericsson and the Slate authors would be in agreement on this point.
I’m sure that we’ve all seen musicians or athletes (and if we’re honest, we’ve probably all been like those musicians or athletes) who sometimes just “phone it in” during practice, or even during a game. I used to coach my daughters’ soccer teams, and I can absolutely assure you that there were hours in games and rehearsals where some of my players really didn’t make any progress. They found ways of getting through practice or games without really trying.
In the Macnamara paper, whether practice was “deliberate” or not was determined by asking people. They collected practice logs, surveys, and interviews. The participants in the studies self-reported whether the practice was deliberate. Imagine someone telling the interviewer or writing in their log, “Yeah, well, about 5,000 of those 10,000 hours, I was really lazy and not trying very hard.” It’s impossible to really distinguish practice from deliberate practice in this data set.
The bottom-line is that the Macnamara study did not test Ericsson’s question. They tested a weak form of the “10,000 hour rule” (that it’s just “practice,” not “deliberate practice”) and found it wanting. But their explanation, that it’s genetics, is not supported by their evidence.
Genetics/Innate starts at birth, no later
The Slate authors argue that, if practice doesn’t explain expertise, then it must be genetics. They cite two studies that show that identical twins seem to have similar music and drawing talent compared to fraternal twins. But that’s correlation and doesn’t prove causation — there may be any number of things on which the identical twins aren’t similar. (See this great Radiolab podcast exploring these kinds of miraculous misconceptions.)
If you’re going to make the genetics/innate argument, you have to start tracking participants at birth. Otherwise, there’s an awful lot that might add to expertise that’s not going to get counted in any practice logs.
I took classes on how to coach soccer. One of the lessons in those classes was, “It’s a poor coach who makes all practices into scrimmage.” Rather, we were taught to have students do particular drills to develop particular skills. (Sound like deliberate practice?) For example, if my players were having trouble dribbling, I might have them dribble a ball in a line around cones, across distances, through obstacles.
Can you imagine a child who one day might play in a soccer team with official practices — but before those practices and perhaps even before joining a team might dribble a ball around the neighborhood? Wouldn’t that be developing expertise? And yet, it wouldn’t be counted in player logs or practice hours. A kid who did lots of dribbling might come into a team and seem like a superstar with all kinds of innate talent. One might think that the kid had the “Soccer gene.”
To start counting hours-towards-expertise anything later than birth is discounting the impact of learning in the pre-school years on up. We know that pre-school years make a difference (see this website that Diana Franklin sent me, and the argument for pre-school in this recent Freakonomics podcast). A wide variety of activities can develop skills that can be influence expertise. If you don’t start tracking students from birth, then it’s hard to claim that you’ve counted in the practice log everything that’s relevant for expertise.
The claim that expertise is determined at birth is a common claim among CS educators. Most CS teachers to whom I’ve asked the question are convinced some people “can’t” learn to code, that it’s genetic or innate to learn programming. That’s where the myth of the “Geek Gene” came from (Raymond Lister has written several times on that). Couldn’t it be that there are dribbling-around-the-neighborhood activities that lead toward CS expertise? Consider the famous pre-programming activity of writing the instructions out for making a peanut-butter-and-jelly sandwich (like here). If we believe that that kind of practice helps to develop CS expertise, then other “writing instructions out” activities might lead towards CS expertise. Maybe people who seem to have genetic/innate ability in CS just did a lot of those kinds of activities before they got to our classes.
The clock on developing expertise doesn’t start when students walk through our door.
Bigger than P=NP: Is teaching > genetics?
In the end, it’s very difficult to prove or disprove that genetics accounts for expertise in cognitive skill. I don’t think Macnamara et al. settled the score. But my point about deliberate practice actually points to a much bigger issue.
Teachers Matter is the two word title of a 2012 OECD report (available here). There is a difference between great teachers and poor teachers, and the difference can be seen in terms of student performance. If you believe that (and there’s gobs of evidence that says you should), then it seems obvious that all practice is not created equal. Hours spent in practice with a good teacher are going to contribute more to expertise than hours spent without a teacher. Look back at that definition of “deliberate practice” — who’s going to pick the activities that most address your needs or provide the immediate feedback? The definition of deliberate practice almost assumes that there’s going to be teacher in the loop.
An open question is just how far we can get with excellent teaching. How much can we use teaching to get beyond genetic disparities? Is teaching more powerful than genetics? That’s an important question, and far more important than the classic CS question whether P=NP. I believe that there are limits. There are genetic problems that teaching alone can’t address. But we don’t know what those limits are.
We certainly have evidence that we can use teaching to get past some differences that have been chalked up to genetics or being innate. Consider the fact that men have better spatial skills than women. Is it innate, or is it learned? It’s not clear (see discussion on that here). But the important point is: it doesn’t matter. Terlecki, Newcombe, and Little have found that they can teach women to perform as well as men on visual skills and that the improvements in spatial ability both transfers and persists (see the journal article version here). The point is that spatial skills are malleable, they can be developed. Why should we think that other cognitive skills aren’t? The claims of the Slate authors and Macnamara et al ignore the power of a great teacher to go beyond simple rote practice to create deliberate opportunities to learn. The words teach, teacher, and teaching don’t appear in either article.
Here’s my argument summarized. The Slate authors and Macnamara et al. dismiss the 10K hour rule too lightly, and their explanation of genetic/innate basis for expertise is too simple. Practice is not the same as deliberate practice, or practice with a teacher. Expertise is learned, and we start learning at birth with expertise developing sometimes in ways not directly connected to the later activity. The important part is that we are able to learn to overcome some genetic/innate disparities with good teaching. We shouldn’t be giving up on developing expertise because we don’t have the genes. We should be thinking about how we can teach in order to develop expertise.
This is part of Briana Morrison’s dissertation work. She’s asking the question about the role of explaining programs in different modalities (e.g., visual vs. oral text) have on understanding. If you know potential applicants (e.g., maybe advertise it to your whole class?), please forward this to them. We’d appreciate it!
Do you like to watch videos on the internet?
Want to help with a research study?
We need volunteers, age 18 and older, with no computer programming experience to help us determine the best way to explain code using videos.
No more than 2 hours of your time!
Completing a portion of the study allows you to enter a raffle for one of four
$50 Amazon Gift Cards
Completion of entire study allows you to enter a raffle for one
$100 Amazon Gift Card
Interested? Go to the following website:
The International Computing Education Research (ICER) conference 2014 is August 11-13 in Glasgow (see program here). My involvement starts Saturday August 9 when we have the welcome dinner for the doctoral consortium, which will be run all day on Sunday August 10 (Sally Fincher and I are chairing). The main conference presentations continue through noon on Wednesday August 13. The rest of August 13 and into Thursday August 14 will be a new kind of ICER session: Critical Research Review for work-in-progress. I’m presenting on some new work that I’m getting feedback on related to constructionism for adults. I’ll blog about that later.
Briana Morrison is presenting her paper on developing an instrument to measure cognitive load (early version of paper available here), with co-authors Brian Dorn (my former student, now a chaired assistant professor at U. Nebraska-Omaha) and me. Briana’s research is looking at the impacts of modality on program understanding for students. Does audio vs. video vs. both have an impact on student understanding? She’s controlling for time in all her presentations, and plans to measure performance…and cognitive load. Is it harder for students to understand audio descriptions of program code, or to try to read text descriptions while trying to read text programs?
There wasn’t a validated instrument for her to use to measure the components of cognitive load — so she created one. She took an existing instrument, and adapted it to computer science. She and Brian did the hard work of crunching all the correlations and load factors to make sure that the instrument is still valid after her adaptation. It’s an important contribution in terms of giving computing education researchers another validated tool for measuring something important about learning.
Since states are making computing courses count as foreign language courses (even if that’s a bad idea), it’s worthwhile to consider what the value is of learning a foreign language. A recent Freakonomics podcast (linked below) considers the return on investment of learning a foreign language. Most intriguing is that people problem-solve differently in their non-native languages. I wonder what the implications are for programming languages? We know that people have negative transfer when their native language abilities conflict with their programming language problem-solving. Are there ways we could make the programming language better for problem-solving?
Learning a language is of course not just about making money — and you’ll hear about the other benefits. Research shows that being bilingual improves executive function and memory in kids, and may stall the onset of Alzheimer’s disease.
And as we learn from Boaz Keysar, a professor of psychology at the University of Chicago, thinking in a foreign language can affect decision-making, too — for better or worse.
Gas station without pump’s post on Garth’s complaint “Teaching programming is not getting easier” intrigued me. Garth does a good job of pulling together a lot of the themes of what makes teaching CS hard today. I think that we can improve the situation. I’m particularly interested in learning how to scaffold the development of programming knowledge, and we have to find ways to create professional communities of CS teachers. There are techniques to share (worked examples, peer instruction, pair programming, Parson’s problems, audio tours), and we’re clearly not doing a good job of it yet.
In programming there are 4 homework problems over the period of a week, none of which are “easy”, and all require some problem solving and thinking. There is somewhat of an incremental progression to the problems but that step from written problem to code is always a big one. It is somewhat similar to solving word problems in math, every student’s favorite task. For programming there are no colleagues available that have as much or more experience to pull teaching ideas from, if there are any other programming teachers at all. There are no pedagogical resources anywhere online for teaching strategies. After watching a number (3) of programming teachers teach it seems the teaching strategy is pretty consistent; show and tell and hope.