Posts tagged ‘educational psychology’

Interesting Pushback Against Incentivizing Active Learning in CS Classes

My Blog@CACM post this month makes a concrete proposal (quoted and linked below). We (all academic computing programs) should incentivize faculty to use active learning methods by evaluating teaching statements for hiring, tenure, and promotion more highly that reference active learning and avoid lecture.

On my Facebook page, I linked to the article and tagged our Dean of Engineering, the Vice-Provost for Undergraduate Education, and the RPT Chair for our College, and asked, “Can we do this at Georgia Tech?”  The pushback on my Facebook page was the longest thread I’ve ever been part of on Facebook.

The issues raised were interesting and worth discussing:

  • Would implementing this put at a disadvantage new PhD’s who have no teaching experience and don’t learn about active teaching?  Yes, but that incentivizes those PhD programs to change.
  • My blog post title is “Be It Resolved: Teaching Statements must embrace Active Learning and eschew Lecture.”  I chose the word “eschew” deliberately.  It doesn’t mean “ban.” It means “deliberately avoid using” which is what I meant.  Lecture has its place — I wrote a blog post defending lecture which still gets viewed pretty regularly.  The empirical evidence suggests that we should use active learning more than lecture for undergraduate STEM education.
  • Should such a requirement for teaching statements emerge from faculty talking about it, or should it be done by administrative fiat?  I lean toward the latter.  As I’ve pointed out, CS faculty tend to respond to authority more than evidence. The administration should do the right thing, and deal with educating teachers (e.g., what are active learning methods first? how do we use them? even in large classes?) later. Faculty will learn the active learning methods in order to create those teaching statements.  The incentive comes first.
  • Lots of respondents thought I was saying that we should require all teaching to be active learning. I wasn’t, and I don’t know how to enforce that anyway.  By evaluating teaching statements more heavily that emphasize active learning, we create an incentive, not a requirement.
  • Some faculty pushed back, “How about students that like lecture? Tough luck for them?” Since we know that active learning is better, even for students who like lecture — yes.
  • Several respondents suggested that active learning is just too hard, that faculty are over-stressed as it is.  Faculty are over-stressed, but active learning isn’t that hard.  In fact, it’s hard for faculty because they have to be quiet and listen in class more.  It is hard to make change, but that’s the point of incentives.  We start somewhere.
  • The biggest theme in the thread is that we should first aim to get faculty to care about teaching and to take active steps to improve their teaching.  I don’t think that’s enough.  Libertarian paternalism (see Wikipedia page) suggests that we set the incentive at the minimal acceptable level (use of active learning) then encourage choice above that (choosing among the wide variety of active learning methods).  We don’t want people to choose options that won’t be in the best interests of the largest number of people.

The discussion went on for four days (and hasn’t quite petered out yet).  I do wonder if active learning methods will be forced upon faculty if we don’t willingly pick them up.  The research evidence is overwhelming, with articles in Nature and hundreds of studies reviewed in the Proceedings of the National Academy of Sciences.  How long before we get sued for teaching but not using the best teaching methods?  One of the quotes in the blog post says, “At this point it is unethical to teach any other way.” We should take concrete steps towards doing the right thing, because it’s the right thing to do.

Here is something concrete that we in academia can do. We can change the way we select teachers for computer science and how we reward faculty.

All teaching statements for faculty hiring, promotion, and tenure should include a description of how the candidate uses active learning methods and explicitly reduces lecture.

We create the incentive to teach better.  We might simply add a phrase to our job ads and promotion and tenure policies like, “Teaching statements will be more valued that describe how the candidate uses active learning methods and seeks to reduce lecture.”

via Be It Resolved: Teaching Statements Must Embrace Active Learning and Eschew Lecture | blog@CACM | Communications of the ACM.

August 21, 2015 at 8:24 am 4 comments

Technology Can Make a Better World: But Not Technology Alone

NYTimes recently had a series of op-ed articles about the role of technology in our world, specifically, “Is Silicon Valley Saving the World, or Just Making Money?” The piece by Melinda Gates (quoted below) caught my attention because she’s invoking the desire to meet students’ “different learning styles” (see blog post on this theme, and why it leads to worse learning).

There’s an important issue here (beyond me critiquing Melinda Gates, who does important work that I admire). It’s not all technology. We need other disciplines as well. Educational psychologists should be informing these developers at Facebook to tell them, “Stop. That’s a bad idea.”

I was at a workshop last year at Stanford about how to grow more CS Education Research in the United States. Andrew Ng spoke to us about the research going on at Coursera. He was clearly not previously informed about the focus of the workshop. When asked, “Would you want to hire more PhD’s in CS Education?” he answered (my paraphrase), “Sure, but we just hire CS PhD’s, and they’re smart enough to pick up anything on-the-fly.” No, that’s wrong. CS is not a superset of all other disciplines.  That belief is exactly the problem I see in the below quoted piece. Scholars in other areas do know things that CS PhD’s don’t, and they bring something unique to the table. Believing that it’s all technology is exactly why Silicon Valley gets accused of being more interested in money than having actual positive impact.

One of the biggest problems in American education is that teachers have to teach 30 students with different learning styles at the same time. Developers at Facebook, however, have built an online system that gives teachers the information and tools they need to design individualized lessons. The result is that teachers can spend their time doing what they’re best at: inspiring kids.

via Technology Can Make a Better World, If We Want It To – NYTimes.com.

August 19, 2015 at 8:44 am 7 comments

ICER 2015 Preview: Subgoal Labeling Works for Text, Too

Briana Morrison is presenting the next stage of our work on subgoal labeled worked examples, with Lauren Margulieux. Their paper is “Subgoals, Context, and Worked Examples in Learning Computing Problem Solving.” As you may recall, Lauren did a terrific set of studies (presented at ICER 2012) showing how adding subgoal labels to videos of App Inventor worked examples had a huge effect on learning, retention, and transfer (see my blog post on this work here).

Briana and Lauren are now teaming up to explore new directions in educational psychology space and new directions in computing education research.

  • In the educational psychology space, they’re asking, “What if you make the students generate the subgoal labels?” Past research has found that generating the subgoal labels, rather than just having them given to the students, is harder on the students but leads to more learning.
  • They’re also exploring what if the example and the practice come from the same or different contexts (where the “context” here is the cover story or word problem story). For example, we might show people how to average test grades, but then ask them to average golf scores — that’s a shift in context.
  • In the computing education research space, Briana created subgoal labeled examples for a C-like pseudocode.

One of the important findings is that they replicated the earlier study, but now in a text-based language rather than a blocks-based language. On average, subgoal labels on worked examples improve performance over getting the same worked examples without subgoal labels. That’s the easy message.

The rest of the results are much more puzzling. Being in the same context (e.g., seeing averaging test scores in the worked examples, then being asked to average test scores in the practice) did statiscally worse than having to shift contexts (e.g., from test scores to golf scores). Why might that be?

Generating labels did seem to help performance. The Generate group had the highest attrition. That make sense, because increased complexity and cognitive load would predict that more participants would give up. But that drop-our rate makes it hard make strong claims. Now we’re comparing everyone in the other groups to only “those who gut it out” in the Generate group. The results are more suspect.

There is more nuance and deeper explanations in Briana’s paper than I’m providing here. I find this paper exciting. We have an example here of well-established educational psychology principles not quite working as you might expect in computer science. I don’t think it puts the principles in question. It suggests to me that there may be some unique learning challenges in computer science, e.g., if the complexity of computer science is greater than in other studies, then it’s easier for us to reach cognitive overload. Briana’s line of research may help us to understand how learning computing is different from learning statistics or physics.

August 7, 2015 at 7:40 am 3 comments

How to Learn Computer Programming Efficiently through Computer Games: Michael Lee and Gidget

I was honored to serve on Michael Lee’s dissertation committee. Mike’s basic thesis is available at this link, or you can get the jumbo-expanded edition with an enormous appendix describing everything in his software plus his learning evaluation (described below) at this link. His thesis brings together several studies he’s done on Gidget, his game in which he teaches programming. I’ve written about his work before, like his terrific finding that including assessments improves engagement in his game (see blog post here) and about how Gidget offers us a new way to think about assessing learning (see blog post here).

Michael had several fascinating results with Gidget. One of my favorites that I have not blogged on yet was that personifying the programming tool improves retention (see his ICER 2011 paper here). When Gidget sees a syntax error, she (I’m assigning gender here) doesn’t say, “Missing semicolon” or “Malformed expression.” Instead, she says “I don’t what this is, so I’ll just go on to the next step” and looks sad that she was unable to do what the programmer asked her to do. The personification of the programming tool dramatically improved the number of game levels completed. They kept going. In course terms, they were retained.

gidget-personified

The dissertation has yet another Big Wow result. Mike developed an assessment of computing knowledge based on Allison Elliott Tew’s work on FCS1 (see here). He did a nice job validating it using Amazon’s Mechanical Turk.

He then compares three different conditions for learning differences:

  • Gidget, as a game for learning.
  • CodeAcademy, as a tutorial for learning.
  • The Gidget game level designer. The idea was to provide a constructionist learning environment without a curriculum. Mike wanted it be like using Scratch or Alice or any other open-ended creative programming environment. What would the students learn without guidance in Gidget?

Gidget and CodeAcademy are statistically equivalent for learning, and both blow away the constructionist option. A designed curriculum beats a discovery-based learning opportunity. That’s interesting but not too surprising. Here’s the wild part: The Gidget users spend 1/2 as much time. Same learning, half as much time. I would not have predicted this, that Mike’s game is actually more efficient for learning about CS than is a tutorial. I’ve argued that learning efficiency is super important especially for high school teachers (see post here).

Mike is now an assistant professor at the New Jersey Institute of Technology (see his web page here). I wish him luck and look forward to what he does next!

July 29, 2015 at 7:32 am 3 comments

WYSIATI: CS Teachers need to ask “What am I not seeing?”

I’m currently reading Nobel laureate Daniel Kahneman’s book, “Thinking Fast, Thinking Slow” (see here for the NYTimes book review).  It’s certainly one of the best books I’ve ever read on behavioral economics, and maybe just the best book I’ve ever read about psychology in general.

One of the central ideas of the book is our tendency to believe “WYSIATI”—What You See Is All There Is.  Kahneman’s research suggests that we have two mental systems: System 1 does immediate, intuitive responses to the world around us.  System 2 does thoughtful, analytical responses.  System 1 aims to generate confidence.  It constructs a story about the world given what information that exists.  And that confidence leads us astray. It keeps System 2 from asking, “What am I missing?”  As Kahneman says in the interview linked below, “Well, the main point that I make is that confidence is a feeling, it is not a judgment.”

It’s easy to believe that University CS education in the United States is in terrific shape.  Our students get jobs — multiple job offers each.  Our graduates and their employers seem to be happy.  What’s so wrong with what’s going on? I see computation as a literacy. I wonder, “Why is our illiteracy rate so high? Why do so few people learn about computing? Why do so many flunk out, drop out, or find it so traumatic that they never want to have anything to do with computing again?  Why are the computing literate primarily white or Asian, male, and financially well-off compared to most?”

Many teachers (like the comment thread after this post) argue for the state of computing education based on what they see in their classes.  We introduce tools or practices and determine whether they “work” or are “easy” based on little evidence, often just discussion with the top students (as Davide Fossati and I found). If we’re going to make computing education work for everyonewe have to ask, “What aren’t we seeing?”  We’re going to feel confident about what we do see — that’s what System 1 does for us.  How do we see the people who aren’t succeeding with our methods?  How do we see the students who won’t even walk in the door because of how or what we teach? That’s why it’s important to use empirical evidence when making educational choices. What we see is not all there is.

But, System 1 can sometimes lead us astray when it’s unchecked by System 2. For example, you write about a concept called “WYSIATI”—What You See Is All There Is. What does that mean, and how does it relate to System 1 and System 2?

System 1 is a storyteller. It tells the best stories that it can from the information available, even when the information is sparse or unreliable. And that makes stories that are based on very different qualities of evidence equally compelling. Our measure of how “good” a story is—how confident we are in its accuracy—is not an evaluation of the reliability of the evidence and its quality, it’s a measure of the coherence of the story.

People are designed to tell the best story possible. So WYSIATI means that we use the information we have as if it is the only information. We don’t spend much time saying, “Well, there is much we don’t know.” We make do with what we do know. And that concept is very central to the functioning of our mind.

via A machine for jumping to conclusions.

July 24, 2015 at 7:23 am 2 comments

What Convinces CS Faculty to Change: Authority over Evidence

My Blog@CACM Post for July 2015 is on the Top Ten Myths of Teaching Computer Science. You can go take a take a look at it here look at it here.

I wrote that blog post because we really have had a long debate in our faculty email list about many of those topics. I recently saw our Dean at an event, and he told me that he hadn’t read the thread yet (but he planned to) because “it must be 100 messages long.” Most of the references in that blog post came from messages that I wrote in response to that thread. It was a long post because people generally didn’t agree with me.  Several senior, well-established (much more famous than me) faculty strongly disagreed with the evidence-based argument I was making. The thread finally ended when one of the most senior, most respected faculty in the College wrote a note saying (paraphrased), “There are probably better teaching evaluation methods than the ones we now use. I’m sure that Mark knows teaching methods that would help the rest of us teach better.” And that was it. Thread ended. The research-based evidence that I offered was worth fighting about. The word of authority was not.

I’ll bet that faculty across disciplines similarly respond to authority more than evidence. We certainly see the role of authority in Physics Education Research (PER). Pioneering PER researchers were not given much respect and many were ostracized from their departments. Until Eric Mazur at Harvard had his students fail the Force Concept Inventory (FCI), and he changed how he taught because of it. Until Nobel laureate Carl Wieman decided to back PER (all the way to the Office of Science Technology and Policy in the White House). Today, the vast majority of physics teachers know research-based teaching methods (even if they don’t always use them). FCI existed before Mazur started using it, but it really started getting used after Mazur’s support. The evidence of FCI didn’t change physics teaching. The voice of authority did.

While we might wish that CS faculty would respond more to evidence than authority (see previous post on this theme), this insight suggests a path forward.  If we want CS faculty to improve their teaching and adopt evidence-based practices, top-down encouragement can have large impact.  Well-known faculty at top institutions publicly adopting these practices, and Deans and Chairs promoting these practices can help to convince faculty to change.

July 15, 2015 at 7:21 am 24 comments

Growing CS Ed through Schools of Ed, and CT is Unlikely: Report from Oldenburg

I’ve written a couple times now about the workshop I attended at the University of Oldenburg the first week of June. (See the post where I talked about my two weeks in Germany.)  For Blog@CACM, I wrote a post about teaching as collective practice and the workshop I took with Barbara Hofer (see post here). I wrote about learning about teacher beliefs and self-efficacy from Helenrose Fives here (see post).

Before we left for the workshop, I got to spend time with Ira Diethelm at the University of Oldenburg and one of her students.  Ira is one of at least 16 (that Ira could count) CS Education professors in Germany.  Ira works with pre-service teachers, in-service teachers, and graduate students.  Her graduate students build outreach efforts and curricula as part of their research, then roll them out and provide resources to teachers.  It’s remarkable what Ira is doing, and I understand that the other German CS Ed professors do similar things.  I came away with a new insight: If we want to bootstrap and sustain CS Education in the United States, we should fund several endowed chairs of CS Education at top Schools of Education.  Eventually, we have to have pre-service computing education programs if we want to make CS education sustainable (see that post here).  Creating these endowed chairs gives us the opportunity to create positions like Ira’s in the United States.

Overall, the workshop was a terrific experience.  The PhD student work was fascinating, and I enjoyed discussing their research with them. It was great to hear about German research perspectives that I hadn’t previously, like the Model of Educational Reconstruction that informs science education (see paper here). Barbara and Helenrose were only two of a several outstanding international education researchers who attended.  As I mentioned to Pat Alexander (who has a lengthy Wikipedia page of her accomplishments), I enjoyed being able to wallow in educational psychology for a week, because I so rarely get to do that.  I gave a talk on three of our projects related to the theme of developing teachers: on Lijun Ni’s work on teacher identity, on the Disciplinary Commons for Computing Education, and on our ebook for preparing CS teachers.  (See Slideshare here.)

Providing_learning_and_reflection_opportunities_to_develop_in-service…

The response to my talk was fascinating.  Some of the German mathematics education researchers are deeply opposed to computing education in schools. (I suspect that more than one of them completely skipped my talk because they are so opposed.)  “Computing education keeps stealing from mathematics teachers, and learning mathematics is more important.”  At my talk, Pat Alexander asked me the same question that Peter Elias asked Alan Perlis in 1961, “Won’t the computer eventually just understand us?  Doesn’t the computer just become invisible and not need to be programmed?”  I told the story about Alan Perlis’s talk and about Michael Mateas’s argument, “There will always be friction.” From the computing educators, I heard a lot of anger. The German computing education researchers feel that other fields squeeze CS out because the they are not willing to allow computing education to take up any time or budget in the curriculum.

Probably the most interesting pushback was against computational thinking.  The educational psychologists thought it was unbelievable that learning computing would in any way impact the way that people think or problem-solve in everyday life.  “Didn’t we believe that once about Latin? and Geometry?” asked Gavin Brown.  The psychologists at the workshop I attended saw a clear argument that we need to introduce computing in high school so that students can see if it’s for them, but not to teach general problem-solving skills. If we really want algorithmic thinking, they can design easier ways to achieve that goal than teaching programming.

We can probably help students to learn about computing in such a way that it might influence problem-solving on the computer. That’s part of Jeanette Wing’s model of Computational Thinking (see her 2010 paper). It’s the “Computational Thinking in Daily Life” part that the psychologists weren’t buying.  That learning about computation helps with computational X is quite reasonable. If you understand what IP addresses are, we can help you to understand DNS problems and to realize that it’s not really that big of a deal if Wikipedia stores your IP address (see story about Erika Poole’s research).  There is evidence that learning one programming language will likely transfer to another one (see Michal Armoni’s paper on transfer from Scratch to a text-based language).  Learning to program is unlikely to influence any problem-solving in everyday life.

July 13, 2015 at 7:31 am 7 comments

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