Posts tagged ‘science education’
Inquiry-based learning is the best practice for science education. Education activities focus on a driving question that is personally meaningful for students, like “Why is the sky blue?” or “Why is the stream by our school so acidic (or basic)?” or “What’s involved in building a house powered entirely by solar power?” Answering those questions leads to deeper learning about science. Learning sciences results support the value of this approach.
It’s hard for us to apply this idea from science education and teach an introductory computing course via inquiry, because students may not have many questions that relate to computer science when they first get started. Questions like “How do I make an app to do X?” or “How do I use Snap on my laptop?” are design and task oriented, not inquiry oriented. Answering them may not lead to deeper understanding of computer science. Our everyday experience of computing, through (hopefully) well-designed interfaces, hides away the underlying computing. We only really start to think about computing at moments of breakdown (what Heidigger called “present-at-hand”). ”Why can’t I get to YouTube, even though the cable modem light is on?” and “How does a virus get on my computer, and how can it pop up windows on my screen?” It’s an interesting research project to explore what questions students have about computing when they enter our classes.
I realized this semester that I could prompt students to define questions for inquiry-based learning in a second computer science class, a data structures course. I’m teaching our Media Computation Data Structures course this semester. These students have seen under the covers and know that computing technology is programmed. I can use that to prompt them about how new things work. What I particularly like about this approach is how it gets me out of the “Tour of the Code” lecturing style.
Here’s an example. We had already created music using linked lists of MIDI phrases. I then showed them code for creating a linked list of images, then presented this output.
I asked students, “What do you want to know about how this worked?” This was the gamble for me — would they come up with questions? They did, and they were great questions. ”Why are the images lined up along the bottom?” “Why can we see the background image?”
I formed the students into small groups, and assigned them one of the questions that the students had generated. I gave them 10 minutes to find the answers, and then report back. The discussion around the room was on-topic and had the students exploring the code in depth. We then went through each group to get their answers. Not every answer was great, but I could take the answer and expand upon it to reach the issues that I wanted to make sure that we highlighted. It was great — way better and more interactive than me paging through umpteen Powerpoint slides of code.
Then I showed them this output from another linked list of images.
Again, the questions that the students generated were terrific. ”What data are stored in each instance such that some have positions and some are just stacked up on the bottom?” and “Why are there gaps along the bottom?”
Still later in the course, I showed them an animation, rendered from a scene graph, and I showed them the code that created the scene graph and generated the animation. Now, I asked them about both the animation code and the class hierarchy that the scene graph nodes was drawing upon. Their questions were both about the code, and about the engineering of the code — why was it decomposed in just this way?
(We didn’t finish answering these questions in a single class period, so I took pictures of the questions so that I could display them and we could return to them in the next class.)
I have really enjoyed these class sessions. I’m not lecturing about data structures — they’re learning about data structures. The students are really engaged in trying to figure out, “How does that work like that?” I’m busy in class suggesting where they should look in the code to get their questions answered. We jointly try to make sense of their questions and their answers. Frankly, I hope to never again have to show sequences of Powerpoint slides of code ever again.
(Thanks to Beth Simon for pointing this out to me!) A new paper from Carl Wieman reviewing the literature on science education is always worth reading, but the one linked below is particularly useful to us in computer science. One of the issues that Carl addresses in this paper is whether competitions and other informal science learning efforts really do help with student learning. We do have a lot of different kind of competitions in computing education, from the First Robotics league to the USA Computing Olympiad. His finding (quoted below): “there is little evidence that such programs ultimately succeed, and some limited evidence to the contrary.”
We use competitions in “Georgia Computes!” but for a very different purpose, not considered in Carl’s analysis below. As he points out later in the article, most efforts at improving teacher quality through in-service workshops fail because the teachers don’t have enough STEM knowledge to begin with, and content knowledge precedes pedagogical content knowledge. What Barbara Ericson has found is that competitions inspire the teachers to learn more. Competitions inspire students, but even more, teachers are inspired to learn in order to support their students. When we have Alice or Scratch competitions, teachers start showing up for our Alice and Scratch professional development, because they want to learn in order to help their students. While the impact of the competitions on the students might be short-lived, I would love to see some measure of the longer-term impact on the teachers.
Competitions and other informal science programs: Attempting to separate the inspiration from the learning. Motivation in its entirety, including the elements of inspiration, is such fundamental requirement for learning that any approach that separates it from any aspect the learning process is doomed to be ineffective. Unfortunately, a large number of government and private programs that support the many science and engineering competitions and out-of-school programs assume that they are separable. The assumption of such programs is that by inspiring children through competitions or other enrichment experiences, they will then thrive in formal school experiences that provide little motivation or inspiration and still go on to achieve STEM success. Given the questionable assumptions about the learning process that underlie these programs, we should not be surprised that there is little evidence that such programs ultimately succeed, and some limited evidence to the contrary. The past 20 years have seen an explosion in the number of participants in engineering-oriented competitions such as First Robotics and others, while the fraction of the population getting college degrees in engineering has remained constant. A study by Rena Subotnik and colleagues that tracked high-school Westinghouse (now Intel) talent search winners, an extraordinarily elite group already deeply immersed in science, found that a substantial fraction, including nearly half of the women, had switched out of science within a few years, largely because of their experiences in the formal education system. It is not that such enrichment experiences are bad, just that they are inherently limited in their effectiveness. Programs that introduce these motivational elements as an integral part of every aspect of the STEM learning process, particularly in formal schooling, would probably be more effective.
In the 18th and 19th centuries, mathematics became part of the core curriculum, and in the early 20th century, mathematics education started being taken seriously. The first Chair of mathematics education was created in 1893 — in a mathematics department.
I don’t know how science education research came to be seen as a standalone field. I know that the earliest Physics Education Researchers (like Lillian McDermott) started (and in Lillian’s case, remain) in Physics.
If you look at most Schools/Colleges/Departments of Education today, there are programs in science education, mathematics education, and sometimes even history or reading education. At what point did these fields break away from their original domain departments become established in Education? What was the development path? Clearly, becoming part of the core curriculum is key. Then it’s important to teach teachers about it.
I honestly don’t know the answer, and I’m hoping that readers here might be able to lend some light. What is the developmental path such that computing education is becomes entrenched, part of what we teach teachers about, and something that grows beyond computer science departments?
I’ve just started looking at this report — pretty interesting synthesis of work in physics education research, chemistry ed research, and others.
The National Science Foundation funded a synthesis study on the status, contributions, and future direction of discipline-based education research (DBER) in physics, biological sciences, geosciences, and chemistry. DBER combines knowledge of teaching and learning with deep knowledge of discipline-specific science content. It describes the discipline-specific difficulties learners face and the specialized intellectual and instructional resources that can facilitate student understanding.
Discipline-Based Education Research is based on a 30-month study built on two workshops held in 2008 to explore evidence on promising practices in undergraduate science, technology, engineering, and mathematics (STEM) education. This book asks questions that are essential to advancing DBER and broadening its impact on undergraduate science teaching and learning. The book provides empirical research on undergraduate teaching and learning in the sciences, explores the extent to which this research currently influences undergraduate instruction, and identifies the intellectual and material resources required to further develop DBER.
Discipline-Based Education Research provides guidance for future DBER research. In addition, the findings and recommendations of this report may invite, if not assist, post-secondary institutions to increase interest and research activity in DBER and improve its quality and usefulness across all natural science disciples, as well as guide instruction and assessment across natural science courses to improve student learning. The book brings greater focus to issues of student attrition in the natural sciences that are related to the quality of instruction. Discipline-Based Education Research will be of interest to educators, policy makers, researchers, scholars, decision makers in universities, government agencies, curriculum developers, research sponsors, and education advocacy groups.
Very interesting report from Neil Brown. Here’s the question I’d like to know: So what are students intuitions about computing as they enter the classroom? Are they suppressed or supplanted through instruction? My guess is that it’s different for computing than for science. We live our lives for many years, 24 hours a day, in the real world before we enter school. That’s a lot of time to invent science hypotheses about the world. Not so much for computing. While we may increasing live our lives in a computing world, it’s a constructed, designed world — a world in which the computer science is explicitly hidden. I bet that students only make up theories about computing in times of break down, when they have to invent a theory to explain what went wrong. How often does that happen? What theories do they develop?
The paper title here says it all: Scientiﬁc knowledge suppresses but does not supplant earlier intuitions. A consistent theme across the research described in this post is that when you are explaining science to pupils, you are not adding totally new knowledge, in the way that you might when explaining a lesser-known historical event. When you explain forces to someone, they will already have an idea about the way the world works (drop something, and it falls to the ground), so you are trying to adjust and correct their existing understanding (falling is actually due to gravity), not start from scratch. The paper suggests that the old knowledge is generally not replaced, but merely suppressed, meaning people carry their original misconceptions with them forever-after.
There are draft letters available on the website.
On May 11, the Washington, DC-based group Achieve released its first public draft of the “Next Generation Science Standards” — or NGSS. These standards, coupled with the “Common Core” standards for mathematics are meant to define how states should think about K-12 science, technology, engineering, and mathematics (STEM) education. Since these standards will ultimately drive what gets taught in science classrooms across the country, the stakes are high.
Computing in the Core (CinC), which runs CSEdWeek, is deeply disappointed that both the math and science standards leave computer science by the wayside. While the math standards are well on their way to being implemented and assessed, Achieve’s new effort on the science standards is still in development, and they need to hear from you about the importance of having real, engaging computer science in these standards.
“While the draft science standards include elements of computer science and computing concepts in the Engineering, Technology, and Applications of Science topics, the attention paid to the discipline of computer science does not match its importance in terms of workforce demand and the opportunities it presents young people in the 21st century,” the coalition says.
I received the below statement via email, and I found it somewhat disappointing. Wholehearted support for the NRC Science Standards even though they ignore computing? From companies like Intel and Cisco? I had not heard of P21 previously, and wonder what if there’s any connection between this group and Computing in the Core. My guess is that there isn’t, but there probably should be.
Next-Generation Molecular Workbench – HTML5-based Scientific Models, Visualizations, Graphing, & Probeware
I have written before about Molecular Workbench. It’s pretty cool that it can now be made all-in-the-browser.
Molecular Workbench is already one of the most versatile ways to experience the science of atoms and molecules. Now thanks to Google’s generosity and the power of HTML5, we’re bringing it to Web browsers everywhere.
Check out “Gas station without pumps” for more on the Next Generation Science Standards, available now for comment (but only through this week). There is a bit of computational thinking and computing education in there, but buried (as the blog post points out). I know that there is a developing effort to get more computation in there.
The first public draft of the Next Generation Science Standards is available from May 11 to June 1. We welcome and appreciate your feedback. [The Next Generation Science Standards]
Note that there are only 3 weeks given for the public review of this draft of the science standards, and that time is almost up. I’ve not had time to read the standards yet, and I doubt that many others have either. We have to hope that someone we respect has enough time on their hands to have done the commenting for us (but the people I respect are all busy—particularly the teachers who are going to have to implement the standards—so who is going to do the commenting?).
I’m also having some difficulty finding a document containing the standards themselves. There are clear links to front matter, how to interpret the standards, a survey for collecting feedback, a search interface, and various documents about the standards, but I had a hard time finding a simple link to a single document containing all the standards. It was hidden on their search page, rather than being an obvious link on the main page.
I doubt that the NAEP included computing education in its report, but my guess is that such inclusion would only draw the average down further. I suppose that this post isn’t saying more than what Alan Kay has been telling us all along, but it bears repeating, and is always worth revisiting when more data become available.
The National Assessment of Educational Progress recently released a report on the science achievement levels of 8th graders in the US: The Nation’s Report Card: Science 2011: Executive Summary.
The results are pretty dismal, with only 2% of students scoring at an “advanced” level (which is pretty much where they need to be if they are going to go into a science or engineering program in college) and only 31% scoring proficient or better (which is where we as a society need our politicians and voters to be in order to make reasonable decisions about issues like pollution, climate change, and funding of medical programs). With fewer than a third of our students having the science understanding that they should have entering high school, our high school science teachers are reduced to doing remedial education, teaching middle school science, and our college teachers then having to teach high school science.
Here’s a great piece to read when wondering about the questions, “Do scientists really all need to learn to program? Surely they’re not going to program are they? What would they do?” What they’ll do is patch together piece of other’s code, with lots of data transformation. What do they need to know? A robust mental model of how the modules work and what the data needs for each are. This is beyond computational thinking.
In my past 20 years as a programmer, I’ve seen the rise of object-oriented programming and ‘modularity’ is something that was hammered onto my forehead. These days, I organise my entire life as a computational biologist around little modules that I re-use in almost every workflow. Yes, sure, you may call me a one-trick pony, but in terms of productivity, call me plough horse.
My core modules are ACQUISITION, COMPUTATION, VISUALISATION, and usually I glue those together with a few lines of Perl or the Unix command line. Here come the constraints again: To overcome the limitations of the software that I’m often “misusing”, I use my own scripts to shove data from one format into the next, and back again. I think every biologist who deals with lots of data, not only us computational folk, should know a few handy lines to quickly turn comma-separated files into tab-delimited, strip a table of empty quotes or grep some essential info.
Interesting: The International Baccalaureate program has re-defined computer science as an “experimental science” rather than as a “mathematics.” Only a few states classify CS as a math or science for high school graduation, andGeorgia is the only one that (like IB) classifies it as a science.
The International Baccalaureate (IB) computer science course will be taught as an option in group 4, experimental sciences, from August 2012.
Computer science previously formed an option in group 5 of the Diploma Programme curriculum but now lies within group 4. As such, it is regarded as an experimental science, alongside biology, chemistry, design technology, physics and environmental systems and societies. This group change is significant as it means DP students can now select computer science as their group 4 subject rather than having to select it in addition to mathematics as was previously the case.
via Computer Science.
I used Arnold Arons’ work a lot when I did my dissertation, so I particularly liked this quote from a recent Richard Hake post. There are direct implications for us in CS, where just about everything (from FOR loops to linked lists) are abstract ideas. Lectures, even lucid ones on these topics, don’t work for most students.
“I point to the following unwelcome truth: much as we might dislike the implications, research is showing that didactic exposition of abstract ideas and lines of reasoning (however engaging and lucid we might try to make them) to passive listeners yields pathetically thin results in learning and understanding – except in the very small percentage of students who are specially gifted in the field.”
Arnold Arons (1997)
REFERENCES [URL's shortened by <
> and accessed on 06 March 2012.] Arons, A.B. 1997. “Teaching Introductory
Physics,” p. 362. Wiley, publisher’s information at <
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I really enjoyed this interview with my colleague, Nancy Nersessian. (Yes, she’s a Professor in the College of Computing.) It helped me understand better why her perspective is revolutionary, and why she’s been racking up awards for the importance of her work.
One of her arguments is that they way we think about the scientific method is wrong, that our “received” notion of the scientific method is not how scientists really work. Rather than test hypotheses, scientists do experiments to influence their models of how the world works. The hypotheses they test come out of those models, and a “failed” experiment doesn’t disprove the hypotheses as much as it feeds more information into developing a more correct model. That’s another reason why failed experiments are so important — they lead to better models.
Georgia Tech’s Nancy Nersessian talked about a project that’s been running at her university since 2001 to investigate how bioengineering scientists think and work, and how to pass their skills on to students. Nersessian said that there is a “received view” of the scientific method — you formulate a hypothesis and then test it to either validate or invalidate it — and then there is the way scientists actually go about their day-to-day work.
In the real world of scientific investigation, she said, scientists usually rely on a model-based process rather than a hypothesis-driven one. They formulate models based on what they know from previous research and then derive testable hypotheses from those models. Data from experiments don’t validate or invalidate hypotheses as much as they feed back into the models to generate better research questions.