Posts tagged ‘science education’
Rules work as a way of communicating computation at a mechanistic level without teaching programming
Sometimes as a reviewer, you get to read a paper that you wish was published immediately. That’s how I felt when I got to review Eliane Wiese and Marcia Linn’s paper “It Must Include Rules”: Middle School Students’ Computational Thinking with Computer Models in Science. It was published in ACM TOCHI in April (see link here).
Eliane and Marcia offer a solution to a problem that teachers face when they want to teach about computational models, but they don’t want to teach programming. How do you get students to reason about the models underlying the simulations they’re exploring without talking about program code? And if you do talk about some notation, some representation of the model, what can you expect students to reason about without teaching them the notation or representation first?
Eliane and Marcia show that rules work. They have students interact with simulations, and then show them rules that might be in that model. Like in a simulation of light, photosynthesis, and glucose levels in plants, a rule might be: When light is on, total glucose made increases.. Eliane and Marcia show rules to students and ask “Are these in the model?” In their abstract, they write:
In our sample, 99% of students identified at least one key rule underlying a model, but only 14% identified all key rules; 65% believed that model rules can contradict; and 98% could not distinguish between emergent patterns and behaviors that directly resulted from model rules. Despite these misconceptions, compared to the “typical” questions about the science content alone, questions about model rules elicited deeper science thinking, with 2–10 times more responses including reasoning about scientific mechanisms. These results suggest that incorporating computational thinking instruction into middle school science courses might yield deeper learning and more precise assessments around scientific models.
The misconceptions don’t bother me. Students will have misconceptions about models — that’s part of teaching science with models. What’s fascinating to me is that the rules worked. Students reasoned mechanistically about the computational models.
My favorite result in this study was where they asked students to predict what would happen if they added a new rule to the model. Basically, “What happens if we change the program like this?” Students were way better at playing these what-if games if the question was posed as a rule. Quoting from the paper:
Asking students to make predictions about the implementation of incorrect rules led to more scientific reasoning about mechanisms than simply asking students about a causal relationship portrayed in a correct model. This pattern was evident for both model contexts, with twice as many workgroups proposing mechanisms with the New Rule question compared to the Typical question for Global Climate (29% vs. 14%) and ten times as many workgroups doing so for Chemical Reactions (53% vs. 5%).
Students can reason about computational models described as rules, even without instruction on rules. That’s a terrific result. It’s one that I’m thinking about how to use in my task-specific programming languages.
Now, this isn’t saying that students can’t reason with function or with imperative statements. Maybe functional or procedural programming paradigms would work, too. Eliane and Marcia have found one approach that does work. They offer us a way to integrate computational modeling into science education, with real discussion of the mechanism of the models, without teaching programming first.
NSF Education Research Questions and Warnings for #CSforAll during #CSEdWeek
Joan Ferrini-Mundy spoke at our White House Symposium on State Implementation of CS for All (pictured above). Joan is the Assistant Director at NSF for the Education and Human Resources Directorate. She speaks for Education Research. She phrased her remarks as three research areas for the CS for All initiative, but I think that they could be reasonably interpreted as three sets of warnings. These are the things that could go wrong, that we ought to be paying attention to.
1. Graduation Requirements: Joan noted that many states are making CS “count” towards high school graduation requirements. She mentioned that we ought to consider the comments of organizations such as NSTA (National Science Teachers Association) and NCTM (National Council of Teachers of Mathematics). She asked us to think about how we resolve these tensions, and to track what are the long term effects of these “counting” choices.
People in the room may not have been aware that NSTA had just (October 17) come out with a statement, “Computer Science Should Supplement, not Supplant Science Education.”
The NCTM’s statement (March 2015) is more friendly towards computer science, it’s still voiced as a concern:
Ensuring that students complete college- and career-readiness requirements in mathematics is essential. Although knowledge of computer science is also fundamental, a computer science course should be considered as a substitute for a mathematics course graduation requirement only if the substitution does not interfere with a student’s ability to complete core readiness requirements in mathematics. For example, in states requiring four years of mathematics courses for high school graduation, such a substitution would be unlikely to adversely affect readiness.
Both the NSTA and NCTM statements are really saying that you ought to have enough science and mathematics. If you only require a couple science or math courses, then you shouldn’t swap out CS for one of those. I think it’s a reasonable position, but Joan is suggesting that we ought to be checking. How much CS, science, and mathematics are high school students getting? Is it enough to be prepared for college and career? Do we need to re-think CS counting as science or mathematics?
2. Teacher Credentialing: Teacher credentials in computer science are a mishmash. Rarely is there a specific CS credential. Most often, teachers have a credential in business or other Career and Technical Education (CTE or CATE, depending on the state), and sometimes mathematics or science. Joan asked us, “How is that working?” Does the background matter? Which works best? It’s not an obvious choice. For example, some CS Ed researchers have pointed out that CTE teachers are often better at teaching diverse audiences than science or mathematics teachers, so CTE teachers might be better for broadening participation in computing. We ought to be checking.
3. The Mix of Curricular Issues: While STEM has a bunch of frameworks and standards to deal with, we know what they are. There’s NGSS (Next Generation Science Standards) and the National Research Council Framework. There’s Common Core. There are the NCTM recommendations.
In Computer Science, everything is new and just developing. We just had the K-12 CS Framework released. There are ISTE Standards, and CSTA Standards, and individual state standards like in Massachusetts. Unlike science and mathematics, CS has almost no assessments for these standards. Joan explicitly asked, “What works where?” Are our frameworks and standards good? Who’s going to develop the assessments? What’s working, and under what conditions?
I’d say Joan is being a critical friend. She wants to see CS for All succeed, but she doesn’t want that to cost achievement in other areas of STEM. She wants us to think about the quality of CS education with the same critical eye that we apply to mathematics and science education.
What Science Literacy Really Means: Concepts, Contexts, and Consequences
I’ve only just started reading this new report from National Academies Press, but am finding it useful and interesting. What do we mean when we say that we want people to be scientifically literate? It’s an important question to ask when considering the goal of computational literacy.
Science is a way of knowing about the world. At once a process, a product, and an institution, science enables people to both engage in the construction of new knowledge as well as use information to achieve desired ends. Access to science—whether using knowledge or creating it—necessitates some level of familiarity with the enterprise and practice of science: we refer to this as science literacy.
Science literacy is desirable not only for individuals, but also for the health and well-being of communities and society. More than just basic knowledge of science facts, contemporary definitions of science literacy have expanded to include understandings of scientific processes and practices, familiarity with how science and scientists work, a capacity to weigh and evaluate the products of science, and an ability to engage in civic decisions about the value of science. Although science literacy has traditionally been seen as the responsibility of individuals, individuals are nested within communities that are nested within societies—and, as a result, individual science literacy is limited or enhanced by the circumstances of that nesting.
Science Literacy studies the role of science literacy in public support of science. This report synthesizes the available research literature on science literacy, makes recommendations on the need to improve the understanding of science and scientific research in the United States, and considers the relationship between scientific literacy and support for and use of science and research.
Source: Science Literacy: Concepts, Contexts, and Consequences | The National Academies Press
Bootstrap computer science in Physics, as well as Algebra
This is a really cool announcement. I believe that computing helps with all kinds of STEM learning, and admire the work at Northwestern on Agent Based Learning in STEM, Project GUTS, and Bootstrap. It’s particularly important for getting CS into schools, since so few schools will have dedicated CS teachers for many years yet (as described here for Georgia). I’m excited to see that Bootstrap will be moving into Physics as well as Algebra.
Bootstrap, one of the nation’s leading computer science literacy programs, co-directed by Brown CS faculty members Shriram Krishnamurthi and Kathi Fisler (adjunct), continues to extend its reach. Bootstrap has just announced a partnership to use its approach to building systems to teach modeling in physics, an important component of the Next Generation Science Standards (NGSS). This project is a collaboration with STEMTeachersNYC, the American Association of Physics Teachers, and the American Modeling Teachers Association.
Get CS into Schools through Math and Science Classes: What we might lose
The August issue of Communications of the ACM (see here) includes a paper in the Viewpoints Education column by Uri Wilensky, Corey E. Brady, and Michael S. Horn on “Fostering Computational Literacy in Science Classrooms.” I was eager to get Uri’s perspective on CS education in high schools into the Viewpoints column after hearing him speak at the January CS Education Research workshop.
Uri suggests that the best way to get computational literacy into high schools is by adding computer science to science classes. He’s done the hard work of connecting his agent-based modeling curriculum to Next Generation Science Standards. In Uri’s model, Computer Science isn’t a “something else” to add to high school. It helps science teachers meet their needs.
Uri isn’t the only one pursuing this model. Shriram and Matthias suggested teaching computer science through mathematics classes in CACM in 2009. Bootstrap introduces computer science at the middle school level as a way to learn Algebra more effectively. Irene Lee’s GUTS (“Growing Up Thinking Scientifically”) introduces computation as a tool in middle school science.
In most states today, computer science is classified as a business/vocational subject, called “Career and Technical Education (CTE).” There are distinct advantages to a model that puts CS inside science and mathematics classes. Professional development becomes much easier. Science and mathematics teachers have more of the background knowledge to pick up CS than do most business teachers. CS becomes the addition of some modules to existing classes, not creating whole new classes.
It’s an idea well worth thinking about. I can think of three reasons not to pursue CS through math/science model, and the third one may be a show-stopper.
(1) Can science and math teachers help us broaden participation in computing? Remember that the goal of the NSF CS10K effort is to broaden access to computing so as to broaden participation in computing. As Jane Margolis has noted, CTE teachers know how to teach diverse groups of students. Science and mathematics classes have their own problems with too little diversity. Does moving CS into science and mathematics classes make it more or less likely that we’ll attract a more diverse audience to computing?
(2) Do we lose our spot at the table? I’ve noted in a Blog@CACM post that there are computer scientists annoyed that CS is being classified by states as “science” or “mathematics.” Peter Denning has argued that computer science is a science, but cuts across many fields including mathematics and engineering. If we get subsumed into mathematics and computer science classes, do we lose our chance to be a peer science or a peer subject to mathematics? And is that going against the trend in universities? Increasingly, universities are deciding that computer science is its own discipline, either creating Colleges/Schools of CS (e.g., Georgia Tech and CMU) or creating Colleges/Schools of Information/Informatics (e.g., U. Washington, U. Michigan, Drexler, and Penn State).
(3) Do we lose significant funding for CS in schools? Here’s the big one. Currently, computer science is classified as “Career and Technical Education.” As CTE, CS classes are eligible for Perkins funding — which is not available for academic classes, like mathematics or science.
I tried to find out just how much individual schools get from Perkins. Nationwide, over $1.2 billion USD gets distributed. I found a guide for schools on accessing Perkins funds. States get upwards of $250K for administration of the funds. I know that some State Departments of Education use Perkins funding to pay for Department of Education personnel who manage CTE programs. To get any funding, high schools must be eligible for at least $15K. That’s a lot of money for a high school.
The various CS Education Acts (e.g., on the 2011 incarnation and on the 2013 incarnation) are about getting CS classified as STEM in order to access funding set aside for STEM education. As I understand it, none of these acts has passed. Right now, schools can get a considerable amount of funding if CS stays in CTE. If schools move CS to math and science, there is no additional funding available.
Perkins funding is one of the reasons why CS has remained in CTE in South Carolina. It would be nice to have CS in academic programs where it might be promoted among students aiming for college. But to move CS is to lose thousands of dollars in funding. South Carolina has so far decided that it’s not in their best interests.
Unless a CS education act ever passes Congress, it may not make economic sense to move CS into science or mathematics courses. The federal government provides support for STEM classes and CTE classes. CS is currently in CTE. We shouldn’t pull it out until it counts as STEM. This is another good reason to support a CS education act.
Engaging Women with Context in Hard Science: A Visit to SpaceX
After the NCWIT Summit, we had two days of meetings with ECEP State Partners and our Advisory Board, hosted by Debra Richardson at the University of California at Irvine. Then, Barbara and I got a chance to visit with Alan Kay for a few hours on Friday. As always, we came away with pages of notes and a long list of things to read and think about. All of these meetings were productive and interesting, but the next stage on our California adventure has had me thinking about how we teach hard science and hard computer science.
A former student at Georgia Tech and one of the first MediaComp Teaching Assistants, Jim Gruen, now works at SpaceX. He invited Barb and I to come up for a tour. We rented a car and drove to Hawthorne.
Barb at SpaceX
What an amazing place! The front third of the building are where the 40 programmers (“Everything is software,” Jim told us) sit with other engineers and developers. The back 2/3’s of the building is the factory floor where rockets are assembled. As you walk onto the floor, there is mission control to your right, and above your head is the actual Dragon capsule that first docked with the International Space Station. It is an inspiring sight as you walk onto the factory floor.
We saw rockets being built! Jim showed us where engines are being assembled into racks, where carbon composites are molded into parts, where detailed metal parts are made with 3-D (metal!) printers, and where the parts of the fuel tanks are welded together then painted. We saw the shop where they’re making prototype space suits. We saw via live video stream (on a giant TV on the wall of the developers’ floor) the amazing Dragon Taxi that was just recently unveiled. We saw lots of people (mostly men, unfortunately) working to build a future where humans are space-faring.
I was deeply impressed. SpaceX has a corporate goal to put human beings on Mars. What a noble goal! (Perhaps we could compare that to a corporate goal of, say, getting more people around the world to drink fizzy, flavored sugar-water?)
Jim does kernel-level hacking. He works on the boot sequence for the flight computer, networking, and device drivers. He showed us his current project. He is integrating in the module responsible for firing the rocket that will pull the astronauts off of the rocket in case there is an explosion during take-off.
I left the SpaceX feeling like I just had a glimpse of the future. The discussions when I tell people about our visit have had me thinking about how we prepare students for that future.
SpaceX is exciting and motivating to everyone I’ve talked to. Admittedly, I tend to hang out with people interested in science and engineering. Our daughters were jealous that we got to visit SpaceX. The other night, my 16 year old daughter had a girlfriend over for dinner, and the friend had questions for me about SpaceX. I was shocked — my teenage daughter is telling her female friends stories about her parents’ adventures?!? All the undergraduate and graduate students that I have told about SpaceX were impressed and had questions about our visit, both male and female students.
I do believe in the literature that suggests that women are socialized to be motivated to help people, and that efforts like service learning can motivate women to study CS. That’s part of the motivation for efforts like HFOSS. Many people are asking the question why women aren’t pursuing the “hard sciences.”
Maybe we’re using the wrong context in the hard sciences. Many people (not just women) don’t get too excited about physics, chemistry, and engineering. Everyone I’ve talked to is very excited about SpaceX. Working at SpaceX requires lots of “hard science.” The stuff that Jim is doing is low-level and geeky — rebuilding the Linux kernel stuff. My kids are still fascinated about it. Maybe women and other students would be more excited about science if the connection was made to end goals like SpaceX and to helping get humans onto other planets.
Context matters for science education, as well as for computing education. As my colleagues Betsy DiSalvo and Amy Bruckman (2011) wrote:
Computer science is not that difficult but wanting to learn it is.
Maybe that goes for “hard science,” too. SpaceX is a great reason to want to learn a lot of “hard science.”
Postscript: I told my daughters about this blog post. One daughter said, “We’ve both been to Space Camp (in Huntsville). Space Camp would be great except for that one annoying guy who always thinks he knows everything and wants to tell everyone all about it.” The other daughter agreed. Context is important, but we have to get the social stuff right, too.
Interesting new NSF Career award in interactive data visualization
Here’s an interesting project that could really get at generalizable “computational thinking” skills:
Wilkerson-Jerde’s research project will explore how young people think and learn about data visualization from the perspective of a conceptual toolkit. Her goals for “DataSketch: Exploring Computational Data Visualization in the Middle Grades” are to understand the knowledge and skills students bring together to make sense of novel data visualizations, and to design tools and activities that support students’ development of critical, flexible data visualization competence.
“Usually when we think of data visualization in school, we think of histograms or line graphs. But in contemporary science and media, people rely on novel, interactive visualizations that tell unique stories using data,” she explains.
Carl Wieman Moves to Stanford to Focus on Better Science Teaching
Carl Weiman has accepted a position at Stanford to focus on science teaching. It’s a great place for him, and I expect that we’ll hear more interesting things from him in the future. One aspect of the story that I find particular interesting is Weiman’s dislike of MOOCs, and how that conflicts with the perspective of some of the MOOC advocates at Stanford.
Mr. Wieman left the White House last summer, after receiving a diagnosis of multiple myeloma and after spending two years searching for ways to force universities to adopt teaching methods shown through scientific analysis to be more effective than traditional approaches.
His health has improved, Mr. Wieman said in an interview last week. But rather than try again through the political process to prod universities to accept what research tells them would be better ways of teaching and retaining students in the sciences, he now hopes at Stanford to work on making those methods even better.
Off to Michigan State, to talk Education and Engineering, then CSTA Conference for ECEP
I’m going to Michigan State University on Wednesday July 10 through Friday August 12. On the 10th, I’m visiting with colleagues whom I knew in Education at the University of Michigan (Bob Geier and Joe Krajcik) and giving a brownbag talk. I’m really looking forward to hanging out with Education folks for the day. I’ve just learned that Danny Caballero has moved to MSU, so I’m hoping to meet up with him, too. On Thursday and Friday, I’m attending a workshop on integrated engineering education. Since I used to do work like that, and haven’t done much in Engineering Education in years, I thought it would be fun and interesting — something I might want to get involved in again. Plus, it was a great chance to get back ‘home’ to Michigan.
The day after I get back, we are heading off to Boston and the CSTA Conference in Quincy, Massachusetts. We are holding an ECEP Day on Sunday July 14, to connect with CSTA Chapter Leaders and Leadership Cohort in the states where we’re working. On Monday, July 15, I’m just hanging out at the CSTA Conference, so if you’re there, I hope you will stop by the ECEP table and visit!
Guided Computer Science Inquiry in Data Structures Class
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.
Applying New Research to Improve Science Education by Carl Wieman: Value of Competitions?
(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.
via Issues in Science and Technology, Fall 2012, Applying New Research to Improve Science Education.
How did math and science education grow out of math and science departments?
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?
New National Academies report on Discipline-Based Education Research
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.
Science Education Research: Misconceptions are suppressed, not supplanted
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.
Tell Achieve that the Next Generation Science Standards Should Include CS
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.
Computing Ed Research - Guzdial's Take 
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