An almost wholly new and updated perspective on the history and state of the art in computing education research (CEdR). I’ve been reading it on my Kindle and enjoying it tremendously.
To the list of books mentioned in the video, I would add:
- Ken Bain: What the Best College Teachers Do
- Alan Alda: If I Understood You, Would I Have This Look on My Face?: My Adventures in the Art and Science of Relating and Communicating
- Steven Strogatz: Calculus of Friendship
- National Academies of Sciences: How People Learn
The graduate school application process has surface similarities to applying to undergraduate programs, but behind the scenes it’s quite different.Continue reading Applying to Graduate School
Teachers should prepare the student for the student’s future, not for the teacher’s past.
~ Richard Hamming, The Art of Doing Science and Engineering: Learning to Learn (1991)
A note to myself
I believe that I can be a better educator through reflection and active engagement. I believe that I can better serve my students and colleagues by being honest with them. I believe that reflection, engagement, and honesty can help other educators improve their praxis, should they feel so inclined.
It has always been about the students
A note to students
To amend the Elementary and Secondary Education Act of 1965 to strengthen elementary and secondary computer science education, and for other purposes.
The Washington Post– and many other outlets– recently reported on the resignation letter of Gerald J. Conti, a social studies teacher at Westhill High School, Syracuse, New York. Mr. Conti has 40 years of teaching experience, but feels that teaching has been marginalized in the increasingly aggressive drive for standardization of curricula, instruction, and assessment.
With regard to my profession, I have truly attempted to live John Dewey’s famous quotation (now likely cliché with me, I’ve used it so very often) that “Education is not preparation for life, education is life itself.” This type of total immersion is what I have always referred to as teaching “heavy,” working hard, spending time, researching, attending to details and never feeling satisfied that I knew enough on any topic. I now find that this approach to my profession is not only devalued, but denigrated and perhaps, in some quarters despised. STEM rules the day and “data driven” education seeks only conformity, standardization, testing and a zombie-like adherence to the shallow and generic Common Core, along with a lockstep of oversimplified so-called Essential Learnings.
– Gerald J. Conti
“Adjuncts are not regular members of the faculty; we are paid an hourly rate for time spent in the classroom. We are not paid to advise students, grade papers, or prepare materials or lectures for class. . . . To ensure that we remain conscious of the adjunctification of CUNY, we ask that you do not call us ‘Professor.'”
To learn more about the source and context, be sure to read The Village Voice’s article on the outsourcing of education.
Logan’s thoughtful, well-presented talk at a recent TEDx conference reminds me just how much massified education is failing our youth.
Learners need agency over their learning experiences.
When a close friend sent me a copy of this book, his inscription read, in part
it has always been about the students
In this short video, Dr. Steven Strogatz– a Cornell Mathematician– reminds us that the student-teacher relationship is complex, dynamic, enduring, and often unpredictable; far from the Brave New World-style cold, isolationism espoused by the so-called professionalization of education that the United States has experienced over the past 100 years.
Bret offers some interesting insights into the importance of immediate, direct feedback while learning to program—really, while programming at all in his CUSEC talk from early 2012.
This 70-minute lecture by Charlie Kaufman— Eternal Sunshine of the Spotless Mind, Adaptation, Being John Malkovich— on screenwriting applies equally well, I think, to being an educator. Consider the following excerpt, but replace screenplay with learning– for the student perspective– or even teaching!
A screenplay is an exploration. It’s about the thing you don’t know. To step into the abyss. It necessarily starts somewhere, anywhere, there is a starting point, but the rest is undetermined, it is a secret, even from you. There’s no template for a screenplay, or there shouldn’t be. There are at least as many screenplay possibilities as there are people who write them. We’ve been conned into thinking there is a pre-established form.
While I sometimes found it difficult to distinguish quotations from his original thoughts, I found both to be engaging and inspiring.
One of the first lessons any successful graduate student (and that should read “undergraduate student”) learns is to introduce themselves to the reference librarian who is responsible for their favorite subject areas. They can serve as guides to the existing collection, alert you to new acquisitions, and help you to acquire books that you may be interested in reading.
Know the LOC system, know which sections interest you, and know who is responsible for maintaining those sections at your institutions. You’ll make a librarian’s day when you introduce yourself as being “particularly interested in the QAs” or any other category.
For me, I always visit these sections, at least:
- K7555 – Copyright
- LB – Theory and practice of education
- Q – Cybernetics/Information Theory
- QA – Computers/Programming Languages
- TK – Electronics/Computer Engineering
History of the LOC system: http://www.loc.gov/catdir/cpso/lcc.html
The categories: http://www.loc.gov/catdir/cpso/lcco/
For any matched pair of non-trivial examples
there exists (n == 1) pattern that the creator of the examples intended to highlight
but there also exist (1 < n <= infinity) unintended patterns that students will find.
It’s difficult to live-code programming examples… the conventions we use by habit often invite students to find the unintended patterns.
As an instructor, how do I get students to see the single pattern in which I’m interested, rather than the possibly infinite patterns that exist? Or, is that even the best goal? Should I, instead, be encouraging students to look beyond the first pattern they detect in order for them to appreciate the inherent complexity of interpretation?
Students reasonably need to understand what is expected of them in a course. Educators need to make clear what is acceptable and unacceptable student engagement with a course. The syllabus is the natural place for this to happen, as long as both students and educators recognize it for what it is.
Students shouldn’t approach the syllabus as the maximum they’ll do… education is about expanding your horizon! The syllabus is the absolute minimum you should expect to do; the engaged and interested student will use it as a lower bound, not an upper bound.
For me, the take away messages from this speech are:
- Find your passion
- Learn whatever you can wherever you are
- Life is a learning experience
- Looking forward is impossible; looking backward is deceptively obvious
- Rejection is not failure
- Rejection is only temporary
- Be gracious
- Be humble
- Be dedicated
- Be of service
Education researchers have shown that the most powerful way we learn is by trying to articulate what we know, believe, and feel (Connally, 1989). The creative process of transforming what is inside our heads into a form that can be shared with others is difficult but absolutely necessary for meaningful learning to take place. How many times have you passively listened to someone (ME!) talking about a topic thinking to yourself how boring it was or how obvious or how random, but when you later tried to explain it to someone, you found it nearly impossible to do so?
Continue reading “Don’t be ‘a writer’. Be writing.” ― William Faulkner
9/4/2011 Update: granovaGG is now available directly from CRAN.
Just over one year ago, I wrote about creating Dependent Sample Assessment Plots (DSAP) Using granova and R. Since then, Brian Danielak has been developing a new, ggplot2-based version of granova named granovaGG, which is almost ready for release on CRAN. This article updates my earlier granova-based version, but leaves much of the article text unchanged.
Continue reading Dependent Sample Assessment Plots Using granovaGG and R
At the ICER 2011 keynote, Eric Mazur reported that when students see a demonstration and either do or do not engage in a discussion of the demonstration, they adjust their memory to fit their model1. In other words, they retain their prior (possibly non-canonical) mental model and mis-remember the facts of the event to fit that model, rather than updating their mental model to account for the new facts2.
In physics education, given the following modes of instruction
- No demonstration
- Demonstration to students
- Student Prediction without discussion
- Student-to-student Discussion (similar to peer instruction)
students do equally poorly on a standard instrument intended to assess students’ understanding of Newtonian mechanics.
So, if we assume that we can’t skip demonstration altogether, and if we can’t just demonstrate, and if demonstration followed by discussion all suffer this fate, then what can be done? Engage students directly.
It’s not the act of predicting or discussing a prediction that triggers changes in student mental models, but rather confrontation with confusing experiences: staking ones intellectual ground so that one knows what one believes, then being confronted by a confounding example, and finally needing to substantially defend and explain the new experience.
Confusion seems to be an essential part of the learning process, or at least the ability of students to reflect and express their confusion3. In a physics class where students were asked to report on what they were most confused about each week, those who expressed confusion did much better than students who claimed no confusion. Willingness to express confusion positively correlates with understanding4.
So, in a peer instruction environment, we teach by questioning, not by telling or showing. We facilitate students’ engagement with the material rather than their obedience while in our classroom.
There is work on students’ use of mechanistic reasoning (i.e., trying to articulate the underlying entities, entity properties, activities in which entities engage, and the mechanism by which those activities give rise the the phenomena of interest) in physics and math education by David Hammer (now at Tufts), Rosemary S. Russ5 (now at Northwestern), Andrew Elby, Ayush Gupta, and Brian Danielak that relates to this… how students express their understandings of and reasoning about mechanisms underlying physical phenomena.
In short, if we’re not changing students’ mental models, than any learning that may occur is shallow and fragile. Some modes of instruction have a better chance of engaging students and changing their models, but unfortunately not the most popular modes of instruction, currently.
- Mazur, E. (2011) International Computing Education Research Conference (ICER) Keynote. Providence, RI. Slides available from http://mazur.harvard.edu ↩
- The keynote is also discussed by Mark Guzdial on his blog at http://computinged.wordpress.com/2011/08/17/eric-mazurs-keynote-at-icer-2011-observing-demos-hurts-learning-and-confusion-is-a-sign-of-understanding/ ↩
- see the Dunning–Kruger effect http://en.wikipedia.org/wiki/Dunning–Kruger_effect ↩
- forthcoming from Mazur, E., et al ↩
- Russ, R. S. (2005) A Framework for Recognizing Students’ Mechanistic Reasoning. A dissertation available from http://drum.lib.umd.edu/handle/1903/4146 ↩
Dependent Sample Assessment Plots (DSAP) constitute a way of visualizing data in the context of two dependent sample analyses. One (of at least four ways1) to think about this would be to think of pre-intervention and post-intervention response data scores, when studying the effects of intervention.
Suppose you’re an educator and you administer an assessment to students at the beginning of a unit asking about their level of confidence or understanding of a topic. You then teach a lesson that spans some period of time. At the end you collect responses to the same questions again. You now have a dependent sample: two responses that related to the same individual for some number of individuals.
- See Pruzek and Helmreich’s paper in the Journal of Statistics Education Volume 17, Number 1 (2009), Enhancing Dependent Sample Analyses Using Graphics ↩