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Constructivism vs. Constructivism vs. Constructionism

ComputingEd - Mon, 03/19/2018 - 09:00

I wrote the below in 1997. I’m surprised that I still find references to it from time-to-time. That website may be going away soon, so I thought I’d put it here (only very slightly edited) in case others may find it useful.

I’d like to offer my take on the meaning of these words. I hear them used in so many ways that I often get confused what others mean by them.

Constructivism, the cognitive theory, was invented by Jean Piaget. His idea was that knowledge is constructed by the learner. There was a prevalent idea at the time (and perhaps today as well) that knowledge is transmitted, that the learner was copying ideas read or heard in lecture directly into his or her mind. Piaget theorized that that’s not true. Instead, learning is the compilation of complex knowledge structures. The learner must consciously make an effort to derive meaning, and through that effort, meaning is constructed through the knowledge structures. Piaget liked to emphasize learning through play, but the basic cognitive theory of constructivism certainly supports learning through lecture — as long as that basic construction of meaning takes place.

I don’t know who invented the notion of Constructivism, the educational philosophy, but it says that each students constructs their own, unique meaning for everything that is learned. This isn’t the same as what Piaget said. Piaget’s theory does not rule out the possibility that you and I may construct exactly the same meaning (i.e., exactly the same knowledge constructions) for some concept or domain. The philosophy of constructivism say that learners will construct their own unique meanings for concepts, so it is not at all reasonable to evaluate students as to how well they have all met some normative goal. (Radical constructivists go so far as to say that the whole concept of a curriculum makes no sense since we cannot teach anyone anything — students will always simply create their own meaning, regardless of what teachers do.) Philosophical constructivists emphasize having students take control of their own learning, and they de-emphasize lecture and other transmissive forms of instruction. This philosophical approach gets complicated by varying concepts of reality: If we all interpret things differently, is there any correct reality?

From my perspective, the assumption of constructivists is currently an untestable hypothesis. We know of no way to peer into someone’s mental constructions. Until we can, we do not know if you and I think about the concept of velocity differently or the same.

Constructionism is more of an educational method which is based on the constructivist learning theory. Constructionism, invented by Seymour Papert who was a student of Piaget’s, says that learning occurs “most felicitously” when constructing a public artifact “whether a sand castle on the beach or a theory of the universe.” (Quotes from his chapter “Situating Constructionism” in the book “Constructionism” edited by Papert and Idit Harel.) Seymour does lean toward the constructivist learning philosophy in his writings, where he talks about the difficulty of conveying a complex concept when the reader is going to construct their own meaning. In general, though, his claim is more about method. He believes that students will be more deeply involved in their learning if they are constructing something that others will see, critique, and perhaps use. Through that construction, students will face complex issues, and they will make the effort to problem-solve and learn because they are motivated by the construction.

The confusion that I and others have about these terms stems from (a) similar looking words and (b) meaning at different levels of the word construct. Piaget was talking about how mental constructions get formed, philosophical constructivists talk about how these constructions are unique (noun construction), and Papert is simply saying that constructing is a good way to get mental constructions built. Levels here are shifting from the physical (constructionism) to the mental (constructivism), from theory to philosophy to method, from science to approach to practice.

Soot transported from elsewhere in world contributes little to melting of some Antarctic glaciers

News From NSF - Fri, 03/16/2018 - 09:00

Airborne soot produced by wildfires and fossil-fuel combustion and transported to the remote McMurdo Dry Valleys of Antarctica contains levels of black carbon too low to contribute significantly to the melting of local glaciers, according to a new study by researchers supported by the National Science Foundation (NSF).

Strong winds in the Dry Valleys, however, can temporarily cause large spikes in the amount of locally produced black carbon, which is distributed through the ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=244808&WT.mc_id=USNSF_51&WT.mc_ev=click

This is an NSF News item.

How CS differs from other STEM Disciplines: Varying effects of subgoal labeled expository text in programming, chemistry, and statistics

ComputingEd - Fri, 03/16/2018 - 07:00

My colleagues Lauren Margulieux and Richard Catrambone (with Laura M. Schaeffer) have a new journal article out that I find fascinating. Lauren, you might recall, was a student of Richard’s who applied subgoal labeling to programming (see the post about her original ICER paper) and worked with Briana Morrison on several experiments that applied subgoal labeling to textual programming and Parson’s problems (see posts on Lauren’s defense and Briana’s).

In this new paper (see link here), they contrast subgoal labels across three different domains: Chemistry, statistics, and computer science (explicitly, programming).  I’ve been writing lately about how learning programming differs from learning other STEM disciplines (see this post here, for example). So, I was intrigued to see this paper.

The paper contrasts subgoal labeled expository text (e.g., saying explicitly as a heading Compute Average Frequency) and subgoal labeled worked examples (e.g., saying Compute Average Frequency then showing the equation and the values and the computed result).  I’ll jump to the punchline with the table that summarizes the result:

Programming has high complexity.  Students learned best when they had both subgoal labeled text and subgoal labeled worked examples. Either one alone didn’t cut it. In Statistics, subgoal labeled examples are pretty important, but the subgoal labeled text doesn’t help much.  In Chemistry, both the text and the worked examples improve performance, and there’s a benefit to having both.  That’s an argument that Chemistry is more complex than Statistics, but less complex than Programming.

The result is fascinating, for two reasons.  First, it gives us a way to empirically order the complexity of learning in these disciplines. Second, it gives us more reason for using subgoal labels in programming instruction — students just won’t learn as well without it.


Dear Colleague Letter: Research Opportunities in Europe for NSF CAREER Awardees

News From NSF - Thu, 03/15/2018 - 14:32

Available Formats:
HTML: https://www.nsf.gov/pubs/2018/nsf18054/nsf18054.jsp?WT.mc_id=USNSF_25&WT.mc_ev=click
PDF: https://www.nsf.gov/pubs/2018/nsf18054/nsf18054.pdf?WT.mc_id=USNSF_25&WT.mc_ev=click

Document Number: nsf18054
Public Comment: This Dear Colleague Letter references NSF 18-055, Dear Colleague Letter: Research Opportunities in Europe for NSF Postdoctoral Research Fellows.

This is an NSF Program Announcements and Information item.

Dear Colleague Letter: Research Opportunities in Europe for NSF CAREER Awardees

News From NSF - Thu, 03/15/2018 - 14:32

Available Formats:
HTML: https://www.nsf.gov/pubs/2018/nsf18054/nsf18054.jsp?WT.mc_id=USNSF_179
PDF: https://www.nsf.gov/pubs/2018/nsf18054/nsf18054.pdf?WT.mc_id=USNSF_179

Document Number: nsf18054
Public Comment: This Dear Colleague Letter references NSF 18-055, Dear Colleague Letter: Research Opportunities in Europe for NSF Postdoctoral Research Fellows.

This is an NSF Publications item.

NSF 2018 Chief FOIA Officer Report

News From NSF - Mon, 03/12/2018 - 18:27

Available Formats:
PDF: https://www.nsf.gov/pubs/2018/ogc18001/ogc18001.pdf?WT.mc_id=USNSF_179

Document Number: ogc18001

This is an NSF Publications item.

Announcing Barbara Ericson’s Defense on Effectiveness and Efficiency of Parsons Problems and Dynamically Adaptive Parsons Problems: Next stop, University of Michigan

ComputingEd - Mon, 03/12/2018 - 07:00

Today, Barbara Ericson defends her dissertation. I usually do a blog post talking about the defending student’s work as I’ve blogged about it in the past, but that’s really hard with Barb.  I’ve written over 90 blog posts referencing Barb in the last 9 years.  That happens when we have been married for 32 years and collaborators on CS education work for some 15 years.

Barb did her dissertation on adaptive Parsons problems, but she could have done it on Project Rise Up or some deeper analysis of her years of AP CS analyses. She chose well. Her results are fantastic, and summarized below. (Yes, she does have six committee members, including two external members.)

Starting September 1, Barbara and I will be faculty at the University of Michigan. Barb will be an assistant professor in the University of Michigan School of Information (UMSI). I will be a professor in the Computer Science and Engineering (CSE) Division of the Electrical Engineering and Computer Science Department, jointly with their new Engineering Education Research program. Moving from Georgia Tech and Atlanta will be hard — all three of our children will still be here as we leave. We are excited about the opportunities and new colleagues that we will have in Ann Arbor.

Title: Evaluating the Effectiveness and Efficiency of Parsons Problems and Dynamically Adaptive Parsons Problems as a Type of Low Cognitive Load Practice Problem

Barbara J. Ericson

Human-Centered Computing

School of Interactive Computing

College of Computing

Georgia Institute of Technology

Date: Monday, March 12, 2018

Time: 12pm – 3pm

Location: TSRB 222


Dr. Jim Foley (Advisor, School of Interactive Computing, Georgia Institute of Technology)

Dr. Amy Bruckman (School of Interactive Computing, Georgia Institute of Technology)

Dr. Ashok K. Goel (School of Interactive Computing, Georgia Institute of Technology)

Dr. Richard Catrambone (School of Psychology, Georgia Institute of Technology)

Dr. Alan Kay (Computer Science Department, University of California, Los Angeles)

Dr. Mitchel Resnick (Media Laboratory, Massachusetts Institute of Technology)


Learning to program can be difficult and time consuming.  Learners can spend hours trying to figure out why their program doesn’t compile or run correctly. Many countries, including the United States, want to train thousands of secondary teachers to teach programming.  However, busy in-service teachers do not have hours to waste on compiler errors or debugging.  They need a more efficient way to learn.

One way to reduce learning time is to use a completion task.  Parsons problems are a type of code completion problem in which the learner must place blocks of correct, but mixed up, code in the correct order. Parsons problems can also have distractor blocks, which are not needed in a correct solution.  Distractor blocks include common syntax errors like a missing colon on a for loop or semantic errors like the wrong condition on a loop.

In this dissertation, I conducted three studies to compare the efficiency and effectiveness of solving Parsons problems, fixing code, and writing code. (Editor’s note: I blogged on her first study here.) I also tested two forms of adaptation. For the second study, I added intra-problem adaptation, which dynamically makes the current problem easier.  For the last study, I added inter-problem adaptation which makes the next problem easier or harder depending on the learner’s performance.  The studies provided evidence that students can complete Parsons problems significantly faster than fixing or writing code while achieving the same learning gains from pretest to posttest.  The studies also provided evidence that adaptation helped more learners successfully solve Parsons problems.

These studies were the first to empirically test the efficiency and effectiveness of solving Parsons problems versus fixing and writing code.  They were also the first to explore the impact of both intra-problem and inter-problem adaptive Parsons problems.  Finding a more efficient and just as effective form of practice could reduce the frustration that many novices feel when learning programming and help prepare thousands of secondary teachers to teach introductory computing courses.


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