Can You Either Confirm or Deny?: Confirmation Bias

Learning By Doing

Let's play a game. Unfortunately, I have to send you away from this page. But go play and come right back! 

Reflection Questions

• So...how did you do? 
• Did you figure out the rule that governs the sequence of numbers? 
• What problem-solving strategies did you use? 
• Was there something that you wish you would have done differently? 

The Two Flavors of Confirmation Bias

Informally, the confirmation bias is the tendency to seek evidence that is consistent with your beliefs. The more personal the beliefs, the stronger the bias. More formally, there are two parts to the definition. The first part is "searching for confirmatory evidence," and the second part is "selectively interpreting the data to fit with one's hypothesis."

Selective Search of Data: The Luminiferous Ether

The number generation game that you played at the beginning of this post is a good example of looking for evidence that conforms to your initial hypothesis [1]. It's a tricky puzzle, and an overwhelming majority of people submit triples that confirm their suspicions. If this describes you, then you are not alone.

Confirmation bias is not relegated to the psychological laboratory. It also operates in the real world. Scientists, for example, often have a vested and personal interest in seeing their hypotheses confirmed by their data. A classic example in the history of science is the search for evidence of the “luminiferous ether." Up until the 19th century, it was believed that this was the substance that carried light. Like sound, it was believed that light needed a medium through which to propagate. Finally, in 1887, Albert Michelson and Edward Morley conducted a famous experiment that conclusively disconfirmed the existence of the ether [2]. Before that experiment, there was a lot of effort invested in finding evidence for this mysterious ether.

Bottom line: The data are selectively collected and disconfirmatory evidence is deemed irrelevant.

Selective Interpretation of Data: The People v. O. J. Simpson 

The O. J. Simpson trial is a good example of selectively interpreting evidence to support your position or claim [3]. As in most trials, there was evidence that nobody can deny: blood at O. J.'s house contained the DNA of Nicole Brown Simpson. There was blood found in O. J.'s white Ford Bronco that matched both Nicole and Ron Goldman's DNA. O. J. Simpson had been arrested for physically assaulting Nicole. These are all incontrovertible facts. However, the defense and prosecution interpreted the data differently. The defense said that the blood samples were placed there by a racist LAPD cop. The defense claimed that the blood was not placed there, but was a result of the murders and subsequent coverup by O. J.

Bottom line: The data are right, but the interpretation of the data are subject to dispute.

The S.T.E.M. Connection

There are implications of the confirmation bias for the classroom as well. In the mid- to late-1960's, educational psychologists experimentally manipulated teachers' expectations of their students. They were told that certain students were about to experience a learning "spurt" (or not). They randomly selected kids to be in the "spurt" condition (or not). 

What did they find? They found that teacher expectations had a measurable impact on the number of IQ points the students gained over the course of an academic year. The effect was particularly strong for kids in first and second grade [4]. Although the authors did not provide a mechanism, we might expect that the confirmation bias was at work. Every time a child in the spurt condition did something notable, it confirmed that teacher's expectation. If the student failed to live up to her expectation, then you might imagine the teacher was able to explain away her behavior (e.g., she was just having a bad day).

Confirmation bias plagues us all, and it can be difficult to avoid. Given that, it is important to experience it first hand, receive feedback when it does happen, and practice looking for and interpreting evidence that goes against one's beliefs. Only then can we get a true picture of the world.  


Share and Enjoy!

Dr. Bob

Going Beyond the Information Given

[1] Wason, P. C. (1960). On the failure to eliminate hypotheses in a conceptual task. Quarterly journal of experimental psychology, 12(3), 129-140.

[2] Motta, L. (2007) Michelson-Morley experiment. Retrieved from http://scienceworld.wolfram.com/physics/Michelson-MorleyExperiment.html

[3] Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175.

[4] Rosenthal, R., & Jacobson, L. (1968) Pygmalion in the classroom. New York: Hold, Rinehart and Winston.

Departures and Arrivals: Linguistic Relativity

Learning By Doing

How many words do you have in your vocabulary for that white stuff that falls from the sky when the weather turns cold? How does your list of words compare to somebody who grew up in the desert?

Arrival

In the movie, Arrival (2016), we are introduced to Dr. Louise Banks, who is an expert in linguistics. When an alien ship touches down in Montana, she is called upon by her government to help translate the alien language. During the time she spends with the heptapods (i.e., the aliens), Dr. Banks introduces the audience to an idea from linguistics that helps explain what is happening. Here is her dialog with her collaborator, Ian Donnelly. 

Dr. Louise Banks: If you immerse yourself into a foreign language, then you can actually rewire your brain.
Ian Donnelly: Yeah, the Sapir-Whorf hypothesis. It's the theory that the language you speak determines how you think and...
Dr. Louise Banks: Yeah, it affects how you see everything.

First of all, I am impressed that Ian is familiar with the Sapir-Whorf Hypothesis because his character is a physicist by training. I guess he must have taken a linguistic or pyschology course just for fun. Second, I didn't realize it, but there are a bunch of misconceptions swirling around the Sapir-Whorf hypothesis.

"This is pure snow! It's everywhere!" –Charles De Mar

The first misconception I ran into was the name: Sapir-Whorf Hypothesis. According to some references [1], Benjamin Lee Whorf was a student of Edward Sapir. However, they never co-authored a paper espousing "the Sapir-Whorf Hypothesis," nor did they even formulate it as a testable hypothesis. It was only later that the field of linguistics gave it a name and a solid formulation. Hmm. This "hypothesis" is not off to a great start.

The second misconception is my favorite. According to the Sapir-Whorf Hypothesis (i.e., linguistic relatively), words that have many variations are important to that culture. For example, Eskimos have 50 different words for the word "snow." Given where they live, snow figures prominently into their daily lives. Ergo, they have lots of ways to refer to snow, right? They must! While it's true that there are lots of words for snow, it turns out that our language also has a lot of words for snow (e.g., snow, sleet, slush, powder, freezing rain, drifting snow, etc.). So it is difficult to establish a baseline as to what counts as "a lot of words" and what is not. 

Finally, the original statement of the hypothesis was tempered a bit. So now there are two formulations. The first is the strong version which stipulates that that language determines our thoughts. In other words, if I had grown up speaking German, my thought patterns would be different from those that I enjoy as an English speaker. Who knows what I could have achieved if I spoke a different language! If that version seems a bit heavy-handed, there is also the weak version which says that language influences our thoughts.

The S.T.E.M. Connection 

There is at least some evidence for the weak version of the Sapir-Whorf Hypothesis. Consider, for example, the well documented difference in mathematical achievement between Chinese and American students. Where does this advantage come from? One possible explanation is the differences in the way numbers are represented in Chinese and English [2]. In both languages, the digits between one and nine have an arbitrary mapping between the numeric concept (e.g., 9) and the spoken word (Jiǔ vs. nine). So we wouldn't expect any advantages either way for counting small numbers. 

But after ten, things start to get interesting. In Chinese, the way to represent numbers between 11-20 is to prefix the number with "ten." So the Chinese word for "11" can be translated as "ten one." In English, however, the arbitrary naming convention continues because the word "eleven" does not give any information about its place value. The hypothesis, then, is that Chinese students will have an easier time learning about place value than English-speaking students. Place value becomes extremely important, for example, when learning to "borrow" during multi-column subtraction.

Linguistic relativity is a fascinating topic, and I am glad that an academy-award winning movie introduced the topic to a broad audience. Maybe it will provoke us to think in new ways...now that we have a word for it!


Share and Enjoy!

Dr. Bob

Going Beyond the Information Given

[1] Am I embarrassed that I'm using wikipedia as a reference? Sure. Is there any reason to believe it isn't true? Not that I know. https://en.wikipedia.org/wiki/Linguistic_relativity

[2] Miller, K. F., Smith, C. M., Zhu, J., & Zhang, H. (1995). Preschool origins of cross-national differences in mathematical competence: The role of number-naming systems. Psychological Science, 6(1), 56-60.

Rise of the Machines: Machine Learning

Learning By Doing

Allow me to attempt to simulate what it's like to be two years old again. Below are two types of bugs. The first type (on the left) are called moneks, and the second type (on the right) are called plaples [1]. Study both types and pay particular attention to the attributes of each type of bug. You might even imagine your mom pointing to each one and saying, "That's a monek. Can you point to the monek?"

Figure 1. Examples of moneks and plaples.

Once you've familiarized yourself with these delightful creatures, test your knowledge by taking the following quiz [1]. You might want to scroll your window so that you're not tempted to cheat!

Figure 2. Test your knowledge of these two types of bugs. 

How did you do? Was it easy? What features did you rely on to figure out if something was a monek or a plaple?

What is "Machine Learning?"

Learning to categorize two different types of bugs may not seem all that incredible. That is, until you try to teach a computer how to recognize and classify visual objects. It's not easy! How might you approach this problem? One method is called machine learning.

Maybe you've heard about machine learning as it applies to Facebook's facial recognition software, or Google's reliance on machine learning to serve up highly specific (and accurate) search results. Or maybe you heard about the machine learning project to identify pictures of cats on the internet (I've heard a rumor that there are a couple of pictures of cats on the internet).

As it turns out, all of the big tech companies are using it. Apple, Microsoft, and Amazon all rely on machine learning to solve some of their thorniest technical problems. But have you ever wondered what the heck "machine learning" is? Have you also wondered, Can I learn how to harness the power of machine learning to solve my own problems? If you've given any thought to either of these two questions, then this is your lucky day! I am going to attempt to explain what machine learning is.

"I said you're holding back" –Walk the Moon

To talk about machine learning, it's useful to introduce a few concepts. The first concept is the outcome that we would like to predict. I'm going to refer to this as a labeled instance. If you recall the steps of the scientific method, you may remember talking about the dependent measure (or "outcome variable"). A labeled instance is analogous to the dependent measure. Second, each labeled instance has a set of quantifiable or measurable properties. The properties are used to describe the labeled instances.

Now that we've defined our data, there are three steps in developing our model.

Step 1 - Training

Like the monek/plaple example, we need to train our algorithms on a dataset for which we have known values for the instances. When we are training our machine learning algorithms, it helps if we can provide it with unequivocal examples, which we call the ground truth. Thus, the first step in machine learning is to run the algorithms on a training dataset. The training dataset has values for both the properties and the labels. The machine-learning algorithm is attempting to learn the association between the values of the properties and their labels. Table 1 is an example of a very small training dataset, which is derived from Fig. 1.

Table 1: Training Data (with Labeled Instances)

ID	Antenna	Head	Body	Legs	Tail	Number of Legs	Label
M-01	Fuzzy	Oval	Striped	Short	Stinger	8	Monek
M-02	Short	Oval	Spotted	Short	Stinger	8	Monek
P-01	Short	Oval	Striped	Long	Long	4	Plaple
P-02	Fuzzy	Square	Striped	Long	Long	4	Plaple

Step 2 - Validation

We withhold a subset of data so that we can start the second step, which is to evaluate our machine-learning model. We will call this the validation dataset. The goal is to measure how accurate our model is. We do this by feeding the model all of the property values, and we make it guess what the labels are. We then compare those guesses against the withheld "answers." It's common practice to keep track of the types of errors that the model makes and report them as accuracy statistics. Table 2 is an example of a validation dataset.

Table 2: Validation Data (label withheld)

ID	Antenna	Head	Body	Legs	Tail	Number of Legs	Label
M-03	Fuzzy	Oval	Spotted	Long	Stinger	4	Monek
M-04	Fuzzy	Square	Spotted	Short	Stinger	8	Monek
P-03	Short	Square	Striped	Short	Long	8	Plaple
P-04	Short	Square	Spotted	Long	Long	4	Plaple

Step 3 - Testing

Now it's time to release our fledgling machine and start categorizing instances for which we do not have labeled instances. In other words, we feed our machine the property values, and we let the algorithms choose the labels. The dataset in this case doesn't have a ground truth. We are letting the machine do all the work now. Table 3 is an example of the input into our machine-learning algorithm that has been trained to recognize the two types of bugs.

Table 3: Test Data (label unknown)

ID	Antenna	Head	Body	Legs	Tail	Number of Legs	Label
K-06	Fuzzy	Oval	Spotted	Short	Long	8	???
K-07	Short	Square	Striped	Short	Stinger	4	???

The S.T.E.M. Connection

Suppose you teach math or computer science, and your students are curious about learning to set up a machine-learning project. There are many different tutorials out there, but these two seem like particularly good starting places:

1. Categorize Lilies using Python libraries
2. Handwriting recognition using TensorFlow

The first is a little more basic, and it leaves out many details. However, the author does a good job of getting the user up and running quickly. You may need to install some software on your computer, but I found doing so was as simple as advertised. Personally, I'm not super-excited about categorizing lilies, but this is a good project to get your feet wet. 

The second tutorial is a little more advanced. The authors discuss matrix multiplication and vector addition. If you need a way to motivate these topics in your own class [2], then this would be a good resource. In addition, the topic is cool. Your goal is to teach a computer to recognize handwritten digits between zero and nine. Banks, for example, rely on this technology for cashing personal checks. 

Machine learning is cool for so many reasons. It is accessible to people who are interested in the topic [3], it solves many difficult problems, and it has a connection to psychology. For example, learning how to categorize objects is a fundamental skill that young brains must master to make sense of the world!


Share and Enjoy!

Dr. Bob

Going Beyond the Information Given

[1] I am indebted to Takashi Yamauchi for allowing me to recreate the stimuli he used in his study on categorization: 

Yamauchi, T., & Markman, A. B. (2000). Inference using categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(3), 776.

[2] By "motivate," I am of course referring to a potential answer to the age-old student lament: When are we ever going to need to know this?!

[3] I would be remiss if I didn't mention the weka workbench that's also freely available. It's generally used for educational data-mining projects.

Reading Room Material: Fences & Opportunity Costs

Editorial Note: I realize that "opportunity costs" isn't technically a term from Cognitive Science. Instead, it comes from Economics. However, it's such a powerful concept that I think we should talk about it! 

One of the big ideas that I was exposed to in college was opportunity costs. You might have run across this concept while planning your retirement or creating a budget. 

Since the origin of opportunity costs comes from the field of Economics, how do they introduce the idea? Many intro textbooks open the topic with the provocative question: Guns or butter? You might be tempted to answer: Both! But when you dig deeper, you find that there is a common, finite resource that they both depend on. Sodium nitrate can be used to make both gun powder and fertilizer. That means you can either make lots of gun powder or lots of fertilizer. Unfortunately, you can't have both.

Another place where you might have run into the concept of opportunity costs is in the award-winning film Fences (2016). The main character (Troy) and his son (Cory) are building a fence [1]. Troy played baseball back in the day, and Cory uses this interest to convince his father to purchase a TV. Here is their dialog: 

Cory: Hey, Pop, why don’t you buy a TV?
[...]
Troy: Yeah. And how much this TV cost?
Cory: I don’t know. They got them on sale for around two hundred dollars.
Troy: Two hundred dollars, huh?
Cory: That ain’t that much, Pop.
Troy: Nah, it’s just two hundred dollars.
Troy: See that roof you got over your head at night? Let me tell you something about that roof. It’s been over ten years since that roof was last tarred. See now, the snow come this winter and sit up there on that roof like it is, and it’s gonna seep inside. It’s just gonna be a little bit, ain’t gonna hardly notice it. Then the next thing you know, it’s gonna be leaking all over the house. Then the wood rot from all that water and you gonna need a whole new roof. Now, how much you think it cost to get that roof tarred?
Cory: I don’t know.
Troy: Two hundred and sixty-four dollars – cash money. While you thinking about a TV, I got to be thinking about the roof, and whatever else go wrong around here. Now if you had two hundred dollars, what would you do: fix the roof or buy a TV?

Troy is trying to teach his son about the importance of hard-work and the value of a dollar. He does a fantastic job because he sets up the perfect example of an opportunity cost: Fix the roof or buy a TV? Although Troy works for the Pittsburgh Department of Sanitation, he sure sounds like an Economist!


Share and Enjoy!

Dr. Bob

More Material

[1] The movie Fences is an adaptation of the play by August Wilson, who is a Pittsburgh native. They shot the movie on location in the Hill District, which overlooks downtown Pittsburgh. 

Reading Room Material: Limitless & Neuroplasticity

10% of All Statistics Are Made Up

In terms of its knowledge about neuro-science, Hollywood seems to be stuck in the 1840's. Maybe that's not a fair criticism because the movie industry's job is to entertain, not to educate. I like to think, however, that screenwriters sometimes glance through the Science section of the New York Times.

As recently as 2014, the movie Lucy, starring Scarlett Johansson, helped perpetuate the following myth. The tag lines for the movie reads, "The average person uses 10% of their brain capacity. Imagine what she could do with 100%."

When will Hollywood learn? 

Limitless: Season 1, Episode 21 "Finale: Part One!"

I think they're finally getting the message. Take a look at the penultimate episode of the TV show Limitless [1]. The main character, Brian [2], is looking to score some NZT, which is a brain-enhancing drug. Here is his dialog with his dealer, Alice.

Alice: And the trip can be intense. Usually, you use about ten percent of your brain.
Brian: Yeah, that's a myth, actually, that whole ten percent thing. But who's to say, really? 
Brian: So do we got to take this with food or? 
Alice: Whatever flies your kite.
Brian: All right, thank you.

Where did this myth come from? One potential source is from the research carried out by the French physiologist Marie Jean Pierre Flourens (1794 – 1867). He conducted what are called ablation studies, where he removed (or "ablated") areas of animals' brains. Flourens concluded that different areas of the brain were responsible for different functions (e.g., the back of the brain is responsible for vision and not movement). He also found that animals were incredibly resilient and were able to survive without large parts of their brains. This led to the conclusion that we don't actually use all of our brain. Hence the myth: We only use 10% of our brain.

Fortunately for us, that turns out to be completely false. We use all of our brain pretty much all of the time. What is also true is that the brain is incredibly flexible and able to adapt to trauma. The technical term for "brain flexibility" is neuroplasticity.

Let's see what kind of stories Hollywood can tell with that concept.


Share and Enjoy!

Dr. Bob

More Material

[1] The Columbia Broadcast System (CBS) owns the copyright to the TV Show Limitless.

[2] I didn't put it together until just now that "Brian" is an anagram of the word Brain. I guess I need to up my dose of NZT.

Reading Room Material: Luke Cage & Expertise

Editorial Note: One of the goals for my blog is to connect educators with Cognitive Science. To make that connection, I try bring in real-world examples. I've been mildly successful in doing so, but I feel like there's something missing. I feel like there's more I can do. 

With today's post, I am going to start publishing a new type of blog called Reading Room Material. The goal is to share examples of Cognitive Science from the outside world. The focus isn't necessarily to define a technical term from the field, like my traditional posts. Instead, the goal is to connect Cognitive Science to our daily lives. 

Luke Cage: Season 1, Episode 2 "Code of the Streets"

Luke Cage is a Netflix television show that's based on a Marvel comic. The titular character works at a barbershop, and it is owned by a man everyone lovingly refers to as, "Pop." Like barbershops of old, Pop offers a straight-razor shave. Cornell Stokes (a.k.a. "Cottonmouth") is one of Pop's oldest associates; however, he has somewhat lost his way. 

In this particular episode, Cornell comes in for a shave so he can chat with Pop about a missing person. Here's a snippet of their dialog [1]: 

Stokes: The clippers are idiot-proof. That's what's missin' nowadays, Pop. Attention to detail. Everyone wants things fast, quick. Me? I like to take my time.
Pop: Time is a luxury most working class men cannot afford.
Stokes: True. Time is precious. Shouldn't be wasted. Mmm A good razor shave is like a vacation to me. It's incredible how few people take advantage.
Pop: It's a lost art.
Stokes: Exactly. That's the problem with these youngsters. They want it all. But they don't want to put in the work. They'll rob lie, cheat, steal, just to get what they want. Damn shame if you ask me.
Pop: Yeah.
Stokes: Shame.
Pop: Mmm-hmm.

There's definitely some subtext here. So what are they really talking about? Some may disagree, but what I think they're really talking about is deliberate practice [2]. Students just aren't willing to put in the 10,000 hours of deliberate practice to become experts! Moreover, the vanguard seem to lament that fact. 

Whether a person is a gangester or a violinist, they have to put in the time. There is no free lunch when it comes to expertise!


Share and Enjoy!

Dr. Bob

More Material

[1] Here is the full transcript of the episode.

[2] Ericsson, A., & Pool, R. (2016). Peak: Secrets from the New Science of Expertise. Houghton Mifflin Harcourt.

Pop a Cap: The iCAP Framework

Learning By Doing

Before we begin, let's learn about how a jet engine works [1]. While you watch this 5-minute video, do your best to learn the contents of the video, while paying attention to your learning process. That is, make a mental note of what you're doing to learn the material. I know that's probably going to split your attention across two different sources of information (I therefore apologize!). Finally, while you are watching, remember not to fall prey to the illusion of explanatory depth! I know that's a lot to ask, but try your best. Here you go:

Friggin' jet engines...how do they work?

Pop Quiz! Do your best to answer the following questions:

• Why is cold air super-heated in the combustion chamber?
• What shape are the stator blades on the turbine?
• Why is the outlet narrower than the intake?
• We all know jet engines are extremely loud. What makes them so noisy?

Now it's time to introspect on your learning experience. While you were watching the video, what did you do to learn the material? Did you:

1. Passively listen to the voiceover and watch the animations?
2. Pause the video and take notes?
3. Ask yourself questions or attempt to connect the material to what you already know?
4. Talk to a friend about the video? 

Depending on the activities in which you engaged, we can make an educated guess about the likelihood of your learning the material. The iCAP Framework [2] makes the following predictions:

1. Shallow learning occurs when a student passively processes the material;
2. Better learning results when the student actively does something to learn the material;
3. We would observe even better learning if the student is making connections and constructively working with the material;
4. The best learning outcome would be observed in an interactive discussion.

Each of these four learning processes are defined in the sections that follow.

Passive

This is the easiest learning process to describe. Passive learning occurs when the student engages in no overt behavior. A great example is a student listening to a lecture. Presumably, the learner is listening to the words in the lecture and looking at the images (assuming there is a slide presentation). In other words, during passive learning, the information is being attended to and it passes through working memory. From there, it is anybody's guess as to the ultimate fate of that information. In the best case, the presented material is stored in long-term memory in such a way that it is available for later recall. However, from the outside observer's point of view, the student is not overtly doing anything to remember the material.

Active

Here's where things get a little more interesting. An active learning process requires that the student engage in an overtly observable behavior. Going back to our lecture example, a student would be said to be engaged in active learning if she is taking notes. Another example would be highlighting a passage in a textbook. The external behavior that occurs during active learning results in some external representation (e.g., notes or highlighting). Active learning is the hallmark of many other learning theories, which suggest that the student should be doing something while learning. In fact, active learning forms the basis of John Dewey's pragmatic educational philosophy [3]. 

Constructive

The problem with active learning is that it mainly focuses on the overt learning behavior instead of considering the content or quality of those behaviors. Thus, a constructive learning process is one in which the learner goes beyond the information that is immediately presented. For example, suppose I gave a student the following function: f(x) = ax² + bx + c, and I tell the student, "This is a quadratic function." Recognizing that the prefix "quad-" is the same as a class of shapes (i.e., quadrilaterals), the student points out that a square is a quadrilateral, and that the formula for calculating the area of a square is A = x². This student is wildly constructive because she has made connections to her previous knowledge and elaborated the original message. Thus, constructive learning takes active learning one step further by adding new information to the target material [4].

Interactive

Being constructive is a great learning strategy because a student is much more likely to remember something when he or she generates it for him- or herself (see the generation effect). However, construction typically happens while learning alone. Interactive learning says that the lesson or material will be better understood if it is done in the context of a learning partner. The reason for the advantage is that two different people typically have non-overlapping knowledge, in that they share some of the same knowledge, but they also know some things that the other person does not. We also see things differently. The reason interactive learning can be better than constructive learning is when collaborators infer new knowledge together. In the literature, this idea goes by many names, including co-construction or co-inference.

To summarize, Table 1 defines each learning process and provides a concrete example.

Learning Process	Definition	Example
Passive	No overt activity	Listening to a lecture
Active	Overt activity is observed Learning by doing	Taking notes during a lecture
Constructive	Going beyond the given information	Drawing a concept map
Interactive	Co-inferring new information with a partner	Collaboratively identifying differences and similarities

Table 1. A summary of each learning process.

The S.T.E.M. Connection

Marshall McLuhan famously said, "The medium is the message." When students are given a video to learn from, it is very tempting to sit back and assume a passive learning orientation. After all, that's what we do when we watch television. That might change with the rise in popularity of educational videos; however, most of us have been trained to treat videos as entertainment. One way to combat passive learning is to give the student a task while watching the video that pushes them toward the active/constructive end of the continuum. 

Another suggestion is to ask students to watch videos in pairs with the explicit instructions to pause the video and ask each other questions. This is a good way to structure collaborative learning because the students can learn from the video as well as from each other [5].

I realize it's not always possible, or even desirable, to ask students to work collaboratively and co-construct new information. But I think the iCAP Framework is a useful way of organizing the learning literature because it helps highlight learning processes that are more (or less) effective. Our goal as instructional designers is to use the framework to select the appropriate learning process for the task at hand.


Share and Enjoy!

Dr. Bob

Going Beyond the Information Given

[1] The video "Jet Engine, How it works ?" is produced by Learn Engineering. If you are interested in learning more about a variety of other engineering topics (e.g., wind turbines), this is a good resource.

[2] Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49, 219-243.

[3] I find this idea so useful that I open each blog with the heading Learning By Doing.

[4] Again, I find this idea so compelling that each blog also has also has a section entitled Going Beyond the Information Given.

[5] Chi, M. T. H., Roy, M., & Hausmann, R.G.M. (2008) Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive Science, 32(2), 301-341.