Posts tagged ‘teachers’
My daughter is enrolled in Georgia’s “Governor’s Honor Program” which started this week. The program is highly competitive — my daughter filled out multiple applications, wrote essays, and went through two rounds of interviews. Over 700 high school students from across Georgia attend for four weeks of residential classes on a university campus for free.
At the parent’s orientation, we heard from two former GHP students, the Dean of Student Life, the Dean of Residence Halls, the GHP Program Manager, and the Dean of Instruction. It’s that last one who really got me.
“You heard from these students, and many other students. GHP changes lives. There is magic in our program.“
The program sounds remarkable. No grades, no tests. The Dean of Instruction said she told the teachers to “give these students learning opportunities beyond what’s in any high school classroom.” Students are only there to learn for learning’s sake.
I was thrilled for my daughter, that she was going to have this experience. I was also thrilled as a teacher.
I want to teach in a program whose leadership says, “There is magic in our program. Our program changes lives.” Last week, I took my daughter to tour three universities. Our daughter is the youngest of three, so I’ve attended other prospective student tours at other universities. I’ve never heard anybody at any of these universities make that kind of claim.
I don’t mean to critique my leadership at Georgia Tech in particular. When I was the Undergraduate Program Director, I never said anything like that to my teachers or to prospective parents. I am critical of higher education more broadly. Higher education in America sets goals like preparing students for careers, giving them experiences abroad and in research, giving them options so that they can tailor their program to meet their particular desires, and surrounding them with great fellow students — I’ve heard all of those claims many times on many tours. I’ve never heard anyone say, “We change lives.”
Rich DeMillo argued in his book Apple to Abelard that higher education institutions need to differentiate from one another. Offering the same thing in the same way makes it hard to compete with the on-line and for-profit options. At Georgia Tech, the faculty are frequently told, “We get amazingly smart students.” We’re told to think about how to tune our education for these super-smart students. I’ve never been told, “Give these students experiences beyond what they will get in any other program. Create magic. Change their lives.”
What I gained at GHP is a new definition for what higher education should be about. We need to step up our game.
I found the article below fascinating, but as an instance of a general model. The article describes how scientists who study gun control have very different opinions about gun control than the general American public — who (presumably) don’t draw on scientific evidence to inform their opinions. People who draw on evidence have different opinions than those who don’t. Most people do not draw on evidence when informing their opinions.
I don’t see that the story here is “Scientists are smart and the public is dumb.”
I would bet that if you asked these same gun control scientists about something outside of their area of expertise, they similarly ignore evidence. I work with CS professors all the time who draw on evidence to inform their opinions within their area of expertise (e.g., robotics, HCI, networking), but when it comes to education, evidence goes out the window. Davide Fossati and I did a study (yeah, evidence — we know what that’s worth) describing how CS faculty make decisions (see post here). In my experience, if the evidence is counter to their opinion, evidence is frequently ignored. One of the things we learned in “Georgia Computes!” was just how hard it is to change faculty (see our journal article where we tell this story). CS teachers are pretty convinced that they teach just fine, despite evidence to the contrary. I regularly try to convince my colleagues to teach using active learning approaches like peer instruction given the overwhelming evidence of its effectiveness (see this article, for just one), and I regularly get told, “It really doesn’t work for me.”
People are people, even when scientists and CS faculty.
Of the 150 scientists who responded, most were confident that a gun in the home increases the chance that a woman living there will be murdered (72 percent agreed, 11 percent disagreed), that strict gun control laws reduce homicide (71 percent versus 12 percent), that more permissive gun laws have not reduced crime rates (62 percent versus 9 percent), that guns are used more often in crimes that in self-defense (73 percent versus 8 percent), and that a gun in the home makes it a more dangerous place to be (64 percent versus 5 percent).
Eighty-four percent of the respondents said that having a firearm at home increased the risk of suicide.
These figures stand sharply at odds with the opinions of the American public. A November 2014 Gallup poll found that 63 percent of Americans say that having a gun in the house makes it a safer place to be, a figure that has nearly doubled since 2000. According to the same survey, about 40 percent of Americans keep a gun in the home.
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.
The Invented History of ‘The Factory Model of Education': Personalized Instruction and Teaching Machines aren’t new
When I was a PhD student taking Education classes, my favorite two-semester sequence was on the history of education. I realized that there wasn’t much new under the sun when it comes to thinking about education. Ideas that are key to progressive education movements date back to Plato’s Republic: “No forced study abides in a soul…Therefore, you best of men, don’t use force in training the children in the studies, but rather play. In that way you can also better discern what each is naturally directed toward.” Here we have learning through games (but not video games in 300BC) and personalized instruction — promoted over 2400 years ago. I named my dissertation software system Emile after Rousseau’s book with the same name whose influence reached Montessori, Piaget, and Papert decades later.
Audrey Watters takes current education reformers to task in the article linked below. Today’s reformers don’t realize the history of the education system, that many of the idea that they are promoting have been tried before. Our current education system was designed in part because those ideas have already failed. In particular, the idea of building “teaching machines” as a response to “handicraft” education was suggested over 80 years ago. Education problems are far harder to solve than today’s education entrepreneurs realize.
Many education reformers today denounce the “factory model of education” with an appeal to new machinery and new practices that will supposedly modernize the system. That argument is now and has been for a century the rationale for education technology. As Sidney Pressey, one of the inventors of the earliest “teaching machines” wrote in 1932 predicting “The Coming Industrial Revolution in Education,”
Education is the one major activity in this country which is still in a crude handicraft stage. But the economic depression may here work beneficially, in that it may force the consideration of efficiency and the need for laborsaving devices in education. Education is a large-scale industry; it should use quantity production methods. This does not mean, in any unfortunate sense, the mechanization of education. It does mean freeing the teacher from the drudgeries of her work so that she may do more real teaching, giving the pupil more adequate guidance in his learning. There may well be an “industrial revolution” in education. The ultimate results should be highly beneficial. Perhaps only by such means can universal education be made effective.
The reality is that technology never has and never will dramatically change education (as described in this great piece in The Chronicle). It will always be a high-touch endeavor because of how humans learn.
Education is fundamentally a human activity and is defined by human attention, motivation, effort, and relationships. We need teachers because we are motivated to make our greatest efforts for human beings with whom we have relationships and who hold our attention.
In the words of Richard Thaler, there are no Econs (see recommended piece in NYTimes).
I’m leaving May 24 for a two week trip to Germany. Both one week parts are interesting and worth talking about here. I’ve been reflecting on my own thinking on the piece between, and how it relates to computing education themes, too.
I’m attending a seminar at Schloss Dagstuhl on Human-Centric Development of Software Tools (see seminar page here). Two of the seminar leaders are Shriram Krishnamurthi of Bootstrap fame who is a frequent visitor and even a guest blogger here (see post here) and Andy Ko whose seminal work with Michael Lee on Gidget has been mentioned here several times (for example here). I’ve only been to Dagstuhl once before at the live-coding seminar (see description here) which was fantastic and has influenced my thinking literally years later. The seminar next week has me in the relative-outsider role that I was at the live-coding seminar. Most of the researchers coming to this event are programming language and software engineering researchers. Only a handful of us are social scientists or education researchers.
The Dagstuhl seminar ends Thursday after lunch. Saturday night, I’m to meet up with a group in Oldenburg Germany and then head up Sunday to Stadland (near the North Sea) for a workshop where I will be advising STEM Education PhD students. I don’t have a web link to the workshop, but I do have a page about the program I’ll be participating in — see here. My only contact there is Ira Diethelm, whom I’ve met several times and saw most recently at WIPSCE 2014 in Berlin (see trip report here). I really don’t know what to expect. Through the ICER DC and WIPSCE, I’ve been impressed by the Computing Education PhD students I’ve met in Germany, so I look forward to an interesting time. I come back home on Friday June 5 from Bremen.
There’s a couple day gap between the two events, from Thursday noon to Saturday evening. I got a bunch of advice on what to do on holiday. Shriram gave me the excellent advice of taking a boat cruise partway north, stopping at cities along the way, and then finishing up with a train on Saturday. Others suggested that I go to Cologne, Bremen, Luxembourg, or even Brussels.
I’ve decided to take a taxi to Trier from Dagstuhl, tour around there for a couple days, then take a seven hour train ride north on Saturday. Trier looks really interesting (see Tripadvisor page), though probably not as cool as a boat ride.
Why did I take the safer route?
The science writer, Kayt Sukel, was a once student of mine at Georgia Tech — we even have a pub together. I am so pleased to see the attention she’s received for her book Dirty Minds/This is Your Brain on Sex. She has a new book coming out on risk, and that’s had me thinking more about the role of risk in computing education.
In my research group, we often refer to Eccles model of academic achievement and decision making (1983), pictured below. It describes how students’ academic decisions consider issues like gender roles and stereotypes (e.g., do people who are like me do this?), expectation for success (e.g., can I succeed at this?), and the utility function (e.g., will this academic choice be fun? useful? money-making?). It’s a powerful model for thinking about why women and under-represented minorities don’t take computer science.
Eccles’ model doesn’t say much about risk. What happens if I don’t succeed? What do I need to do to reduce risk? How will I manage if I fail? How much am I willing to suffer/pay for reduced risk?
That’s certainly playing into my thinking about my in-between days in Germany. I don’t speak German. If I get into trouble in those in-between days, I know nobody I could call for help. I still have another week of a workshop with a keynote presentation after my couple days break. I’ve already booked a hotel in Trier. I plan on walking around and taking pictures, and then I will take a train (which I’ve already booked, with Shriram’s help) to Oldenburg on Saturday. A boat ride with hops into cities sounds terrific, but more difficult to plan with many more opportunities for error (e.g., lost luggage, pickpockets). That’s managing risk for me.
I hear issues of risk coming into students’ decision-making processes all the time, combined with the other factors included in Eccles’ model. My daughter is pursuing pre-med studies. She’s thinking like many other pre-med students, “What undergrad degree do I get now that will be useful even if I don’t get into med school?” She tried computer science for one semester, as Jeanette Wing recommended in her famous article on Computational Thinking: “One can major in computer science and go on to a career in medicine, law, business, politics, any type of science or engineering, and even the arts.” CS would clearly be a good fallback undergraduate degree. She was well-prepared for CS — she had passed the AP CS exam in high school, and was top of her engineering CS1 in MATLAB class. After one semester in CS for CS majors, my daughter hated it, especially the intense focus on enforced software development practices (e.g., losing points on homework for indenting with tabs rather than spaces) and the arrogant undergraduate teaching assistants. (She used more descriptive language.) Her class was particularly unfriendly to women and members of under-represented groups (a story I told here). She now rejects the CS classroom culture, the “defensive climate” (re: Barker and Garvin-Doxas). She never wants to take another CS course. The value of a CS degree in reducing risks on a pre-med path does not outweigh the costs of CS classes for her. She’s now pursuing psychology, which has a different risk/benefit calculation (i.e., a psychology undergraduate degree is not as valuable in the marketplace as a CS undergraduate degree), but has reduced costs compared to CS or biology.
Risk is certainly a factor when students are considering computer science. Students have expectations about potential costs, potential benefits, and about what could go wrong. I read it in my students’ comments after the Media Computation course. “The course was not what I expected! I was expecting it to be much harder.” “I took a light load this semester so that I’d be ready for this.” Sometimes, I’m quite sure, the risk calculation comes out against us, and we never see those students.
The blog will keep going while I’m gone — we’re queued up for weeks. I may not be able to respond much to comments in the meantime, though.
William Wulf is the 2014 recipient of the ACM Karl V. Karlstrom Outstanding Educator Award
Wulf is recognized for contributions as a teacher, author, and national leader who focused attention and changed the national education agenda and in the process supported the needs of underserved and under-represented students. As Assistant Director of the National Science Foundation’s Directorate for Computer and Information Science & Engineering (CISE), he understood the role NSF played in supporting science and engineering in the US for both basic research and operation of several high performance computing centers and networks. As President of the US National Academy of Engineering, he advocated for advances in engineering education and technical literacy. Wulf is professor emeritus of Computer Science at the University of Virginia. An ACM Fellow, he received the 2011 ACM Distinguished Service Award.
In the Preface to the new 4th ed book, I wrote a bit about what we know about how to teach computer science using Media Computation. These are probably useful in most CS classes, even without Media Computation:
Over the last 10 years, we have learned some of the approaches that work best for teaching Media Computation.
- Let the students be creative. The most successful Media Computation classes use open-ended assignments that let the students choose what media they use. For example, a collage assignment might specify the use of particular filters and com- positions, but allow for the student to choose exactly what pictures are used. These assignments often lead to the students putting in a lot more time to get just the look that they wanted, and that extra time can lead to improved learning.
- Let the students share what they produce. Students can produce some beautiful pictures, sounds, and movies using Media Computation. Those products are more motivating for the students when they get to share them with others. Some schools provide online spaces where students can post and share their products. Other schools have even printed student work and held an art gallery.
- Code live in front of the class. The best part of the teacher actually typing in code in front of the class is that nobody can code for long in front of an audience and not make a mistake. When the teacher makes a mistake and fixes it, the students see (a) that errors are expected and (b) there is a process for fixing them. Coding live when you are producing images and sounds is fun, and can lead to unexpected results and the opportunity to explore, “How did that happen?”
- Pair programming leads to better learning and retention. The research results on pair programming are tremendous. Classes that use pair programming have better retention results, and the students learn more.
- Peer instruction is great. Not only does peer instruction lead to better learning and retention outcomes, but it also gives the teacher better feedback on what the students are learning and what they are struggling with. We strongly encourage the use of peer instruction in computing classes.
- Worked examples help with learning creativity. Most computer science classes do not provide anywhere near enough worked-out examples for students to learn from. Students like to learn from examples. One of the benefits of Media Computation is that we provide a lot of examples (we’ve never tried to count the number of for and if statements in the book!), and it’s easy to produce more of them. In class, we do an activity where we hand out example programs, then show a particular effect. We ask pairs or groups of students to figure out which program generated that effect. The students talk about code, and study a bunch of examples.