For the Love of Maths

What Neuroscience can tell us about Arithmetic Success

“Then, little by little, one may introduce [students] to the power of symbolic mathematical notation and the shortcuts it provides — but at this stage, great care should be taken never to divorce such symbolic knowledge from the child’s quantitative intuitions.” -Stanislas Dehaene

In The Number Sense, Dehaene explains that many animals exhibit an ability to approximate numerical quantities. This ability is also present in human infants. It is quite likely that the ability to approximate numerical quantities is biologically primary knowledge and is part of our evolutionary growth as a species. Of interest in mathematics education is how students begin to “fine-tune” this approximate system and develop symbolic understanding of the Arabic numeral system.

Dr. Daniel Ansari has been a part of several studies exploring these ideas as they pertain to neurocognitive architecture and arithmetic fluency. He describes arithmetic fluency as “the speed and efficiency with which correct solutions to numerical computations are generated.” The first study I wish to discuss looked at numerical predictors of arithmetic success. The study used the tasks described below.

  • Numeral Ordering: students saw three numbers (either all one-digit or all two-digit) equidistant from the centre number and had to determine if the numbers were in increasing order.
  • Numeral Comparison: students saw two numbers and had to determine which numeral was larger.
  • Dot Comparison: students saw two arrays of dots and had to determine which array contained more dots. Since strategies such as using density or area to determine relative size are not as predictive of mathematics achievement, the researchers ensured number of dots and area/average size of dots were incongruent so that utilizing a non-numerical strategy would be the more difficult strategy.
  • Object Matching: students were shown an array of 1-6 objects and were asked to match the cardinality to two other arrays (only one of these other arrays contained the same number of objects).
  • Counting: students saw 1-9 dots and had to count them quickly and correctly.
  • Number Line Estimation: students were shown a horizontal line with endpoints 0 and 100 and had to estimate where a given number (given both verbally and on screen) would be on the number line.
  • Dot Quantity Estimation: students were shown an array of dots quickly (too fast to count) and were asked to estimate the number of dots on screen.

The study analyzed results taken from 1391 Dutch school children between grades 1 to 6 (inclusive). In general, performance significantly improved for each task as grade level increased. Of interest are the predictive values of the tasks on arithmetic ability over the grade levels. It was found that the number line estimation task was a strong predictor of arithmetic ability in grades 1 and 2, and tapered off for the later grades. This result was shared with the numeral comparison task – those students who were better able to compare two numbers tended to have higher arithmetic ability in grades 1 and 2. Numeral ordering was the poorest predictor for grade 1, yet increased gradually so that it was the strongest predictor of arithmetic ability by grade 6. A figure summarizing all tasks and their relative predictive values is given below.

Untitled

This study has interesting implications for teaching, suggesting that encoding relative magnitudes of Arabic numerals and mapping them onto the number line are important for arithmetic success in primary mathematics, while numeric ordering is most important for later-middle years mathematics. There were no distinct predictors for grades 3, 4 or 5; however, one might argue that numerical ordering becomes more important and relative magnitudes become less important as students progress through these grades.

Tying nicely into this study is a second one in which the authors observed areas of the brain while participants answered single digit arithmetic. A correlation was found between activated areas and arithmetic fluency on the PSAT mathematics test. It is known that activation in regions of the left inferior parietal lobe is associated with fact retrieval, while activation in regions of the right bilateral intraparietal sulcus is associated with procedural calculations such as processing or representing relative magnitudes.

In this analysis, thirty-three 12th grade participants were presented with a series of single-digit addition and subtraction statements that were either correct or incorrect (for example 3+2=5 or 3+2=6). Students were tasked with determining the validity of the statement. Incorrect solutions differed from the actual solution by +1 or -1. It was determined that individuals who scored higher on the PSAT mathematics test showed greater activation in regions of the left inferior parietal lobe, or the region associated with fact retrieval. Those with lower scores on the PSAT test showed more activation in the regions associated with processing relative magnitudes.

Again, we have some significant applications to teaching. First and foremost, it must be noted that reliance on procedural or magnitude-based mechanisms to solve basic arithmetic problems is associated with deficient mathematics competence as students move from elementary grades to the middle school and high school grade levels. This might be in part due to the higher cognitive demand of mathematical problems as one moves up through the grade levels. A reliance on more procedural-based mechanics rather than fact recall may strain the working memory and prevent the construction of appropriate neurological structures required to support higher learning in mathematics. Another important aspect to take away from this study is the importance of teachers understanding that the shift away from magnitude-based calculations is vital in development of higher-order mathematical skill, as those students who rely on these skills show less mathematical competence compared to their peers.

So we see that there is a period of growth between the primary years and middle years in which students ideally transition from a quantitative approach to a symbolic approach to numbers. During the primary years we often see students using quantitative mechanics to solve mathematical problems, such as counting on their fingers. It is important to help develop this cardinal approach to numbers in the primary grades, as it is predictive of arithmetic fluency at this level. However, we have also seen that those students who fail to transition to a more symbolic approach with fact recall show signs of mathematical deficiency in the middle and high school years. Thus, it is imperative for teachers to help develop an ordinal approach to numerals as students progress through grades 3 to 6. In addition to this, utilizing strategies, such as low stakes single-digit arithmetic fluency tests, that increase fact recall in students is equally as important. It very well may be that development of fact recall aids in developing the neurological pathways required for success in higher-level mathematics.

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Missing Messages from Jo Boaler’s Maths Video

“When students were given problems to solve, and they did not know methods to solve them, but they were given opportunity to explore the problems, they became curious, and their brains were primed to learn new methods, so that when teachers taught the methods, students paid greater attention to them and were more motivated to learn them.” -Jo Boaler

In Jo Boaler’s most recent mathematics announcement on YouTube, she gives her audience four important messages to think about. However, it is what is missing from these four messages that is more important for us to reflect upon.

Message One: “Everyone can learn maths to high levels”

Honestly, I really appreciate how they begin this section of the video. I agree that it can be detrimental to hold the belief that some people are “maths people” and some are not. Many of these harmful misconceptions are cultural – why is it in Western culture that we are proud of the fact that we have never used algebra in our post-high school lives? I do believe that removing these hidden messages from socio-cultural contexts, where possible, is very important. This said, this is not the focus of this section of the video, so let’s see what is mentioned regarding learning mathematics to higher levels.

Looking at the “BRAIN EVIDENCE!” leads us to a chat about neural pathways. While I fully agree with Greg Ashman’s critique of the language of this portion of the video, the discussion, I believe, is to motivate a concept of learning that most of the current theories (cognitivism, constructivism, cognitive load) agree on: humans recall past experiences or draw upon pre-existing knowledge to help them “make sense” of current stimuli and connect it to past knowledge. For example, the firing of a synapse may be us drawing on past experiences/knowledge, and creation of a new neural pathway might be indicative of new knowledge amalgamating with old knowledge.

Of interest here is Boaler’s discussion of the three week program in which participants worked for ten minutes on a subject. This study found that this amount of time was enough to cause permanent neural pathways to form. This is not terribly surprising, as some of the best ways to learn new material is repeated and spaced out practice (see How We Learn by Benedict Carey). Boaler’s student then states “If a three-week program can do that, what do you think a year of maths class can do to your brain?” I assume that if repeated and spaced out practice would be utilized more effectively in mathematics classes nationwide, a lot could be accomplished.

Unfortunately, Boaler seems to be at odds with memorization (see here and here) claiming that timed tests and memorization lead to maths anxiety. While it is true that high-stakes testing can lead to adverse effects, Boaler’s message is often misconstrued as “all testing is bad“, when this is not the case at all: low-stakes testing with feedback is known to be one of the best tools to enhance fluency and long-term storage of maths facts. The blog here can lead you to some of the relevant research on this subject.

Missing from Message One: Yes, you can learn mathematics to higher levels, but one cannot build a house without a strong foundation. Repeated and spaced out practice with mathematical concepts is vital to learning the fundamentals needed to access higher mathematics. Yes, it is true that your brain creates neural pathways when you learn new materials, and when you practice these facts over and over, the pathways become stronger. Testing is an excellent way to ensure fluency with maths facts – just keep it low-stakes.

Message Two: “Believe in yourself”

The next message we are to consider is that believing in ourselves as learners is important. The group goes on to briefly discuss Carol Dweck’s work with Growth and Fixed Mindsets. I want to be clear that I think having a belief in one’s own ability is a great thing; however, this message doesn’t contain the full truth. More often than naught, we fail to take out of Dweck’s message that learning comes with much effort and practice. If we fail to put in the desired work to better our knowledge of a subject, then the statement “I believe in myself.” bears no weight.

On a related note, let’s consider the argument of intrinsic motivation. Recently, Greg Ashman posted this blog reminding us that it is actually achievement in mathematics that predicts intrinsic motivation, and not the other way around. He cites a longitudinal study that focused on Grade 1-4 students that found higher achievement in mathematics led to higher motivation in mathematics. Similarly, Dörnyei noted in Teaching and Researching Motivation that motivation seems to be at its highest when students are competent with the subject material. So how do novice learners become competent with subject material in mathematics? By spending a lot of time developing content knowledge. So perhaps Jo Boaler has it backwards – maybe it is strong mathematics content knowledge that leads to a belief in our ability to do mathematics.

Missing from Message Two: Believe in yourself, yes. But belief in yourself can only take you so far. Eventually you will need to develop content knowledge in mathematics, and developing this knowledge takes time, hard work and effort. The more content knowledge you have in mathematics, the more likely it is that your achievement in mathematics will increase. Higher mathematics achievement may then lead to higher intrinsic motivation, self-efficacy and confidence – leading you to have more belief in your abilities.

Message Three: “Struggle and mistakes are really important”

Again, putting the odd language aside, the message being delivered here is misleading: “When people made mistakes their brains grew more than when they got work right.” While I agree that neural pathways are being created whether we answer incorrectly or correctly, I certainly want my students to be forming ‘good’ neural pathways that will help them search out an acceptable answer or procedure. If a student consistently answers incorrectly, and is not corrected, we know this ‘bad’ neural pathway will become stronger. These misconceptions can be very difficult to change later in our mathematics career, as new information now begins to consolidate on this misconception.

Perhaps Boaler was trying to motivate the idea of productive failure – situations in which a learner is set up to struggle with a problem just outside of their current abilities with only little guidance from the teacher. In this case, I think it is worthwhile to discuss the work by Manu Kapur. In this study Kapur followed 75 grade seven mathematics students in Singapore. Some students were a part of a traditional lecture and practice class, while another group was a part of an ill-structured problem-solving class with no structure or support. The later group, at the end of the unit, had a consolidation class where the teacher led a discussion on important concepts. Of interest is that the students in the productive failure group outperformed those in traditional lecture style on a test containing mostly well-structured response questions. A possible downside is that those students in the productive failure group also reported lower confidence in their solutions.

To round out this section of the video, the group discusses Michael Jordan and the failures he made as a basketball star (oddly enough they don’t mention the obvious failure of his 1993-94 MLB career). To me, this analogy doesn’t work given the context of the video. If an engineer stated “Twenty-six times I was trusted to build a bridge that was stable, but didn’t. I have failed over and over.” I don’t think this engineer would have much success in their field. In mathematics we do often make mistakes; however, as we progress further in our craft, we strive to minimize the errors with the facts/procedures that we are most fluent with. The use of Michael Jordan here simply does not add to Bolaer’s claim that “mistakes are important” and distracts the viewer from engaging in something of substance.

Missing from Message Three: To say that an individual’s brain will grow more making a mistake than with a correct answer is misleading. It is true that neural pathways will form in either case, and it is important to try to make ‘good’ neural connections. The more repeated practice a student has with a misconception, the harder it will be to remove this misconception later. We have also seen that perhaps struggling with a mathematics problem is important under certain conditions. Kapur shows us that there is such thing as productive failure – that is students may be able to consolidate information easier directly after struggling with specific problems. However, we note that these students also were not confident with their solutions. Thus, we conclude that the teacher utilizing productive failure as a teaching strategy must be well-trained, as we have already seen low confidence can lead to low achievement and motivation in mathematics.

Message Four: “Speed is not important”

There are many messages in this section that are misinformed. Let’s begin with “speed is not important”. I disagree with this claim to a certain extent. I do not expect my students to be The Flash on their tests of recall. However, if I am expecting them to perform the standard algorithm for multiplication, I do expect their basic fact recall to be decently quick. How fast is decently quick? I think that depends on the student. What I am looking for is that their working memory is not getting bogged down computing maths facts while thinking about the standard algorithm question. If this is happening then my student is too slow and needs to revisit computations of maths facts before coming back to the standard algorithm.

So what justification does Boaler give for speed not being important? Well, one of her students mentions that “some of the best mathematical thinkers of the world are really slow”. While I do not deny that mathematicians think deeply about problems, let’s also realize here that our top mathematical thinkers are often thinking about complex multi-dimensional problems. I doubt very much they take several minutes/hours/days/weeks to compute a multiplication fact. But this is beside the point – Boaler is using the argument that ‘mathematicians are slow’ as evidence that ‘speed is not important’. The use of one specialized cohort to generalize to all cohorts is false (even if we set aside our argument above that the types of problems being tackled are different).

Other phrases that are used in this section of the video are “maths isn’t just about calculations – they may be the least interesting part” and “maths is not about memorization and is not about calculations.” Boaler then goes on to discuss that it is more important to see deep connections, think creatively and see mathematics visually. While I agree that it is important to see connections in mathematics, I disagree that calculations are uninteresting. Often in mathematics, calculations lead us to general procedures or proofs. For example, the calculations used to get to the quadratic formula are given below. Notice that these calculations show us the procedure of completing the square (can you find it?)! If we had dismissed the calculations believing that they were uninteresting, we may not have seen this deeper connection – something that Boaler advocates for.pic1pic2pic3I find her second statement that “maths is not about memorization and is not about calculations” very misleading as well. Memorization is a very important strategy to use when learning new facts and should not be undersold. We have all used memorization as a strategy to learn before – how else would we have conceptualized cardinality of numbers? Dr. Ansari from Western Ontario reminds us of this – he has shown us that our ability to retrieve maths facts is a predictor of achievement on the PSAT test. Those students who did not have maths facts memorized (used a quantitative-based strategy) did not succeed as well on the test as those who had facts memorized.

Missing from Message Four: First, speed is not important, but it is also not unimportant. Fluency with basic facts is important when working on more challenging problems. If we are constantly slowing down to think about basic facts, we begin to lose sight of our original problem. Second, it is true that mathematics is more than calculations; however, calculations often lead us to the bigger connections in mathematics, so they should not be disregarded. Finally, memorization is an important strategy to learning new information. Blanket statements such as “memorization is not important” downplay the importance of this teaching tool for teachers.

Conclusion

Overall, we can see that the messages shared in this video are quite misleading or incomplete. At the end of this video they promote Boaler’s site, Youcubed because it has “real maths” on it. I am skeptical that real mathematics resides on this website (you can see my review of one of the activities that was devoid of mathematical language here) – however, I promise to keep an open mind as I review more of her activities in the future.

Cog-nitive Overload: “Counting Cogs” from youcubed.org

“This problem requires children to think about factors and multiples and, in particular, common factors, but it is not necessary for them to have met this term prior to having a go at the task. It offers opportunities for pupils to ask their own questions, find examples, make conjectures and begin to generalise.” -excerpt from Youcubed website

Today I want to review one of Jo Boaler’s lessons from youcubed.org. I recently came across a grade five class where the teacher used the “Counting Cogs” activity, which can be found here.

Premise:

The task can be summarized as follows: take two of the cogs, rotate them about to see if one tooth from the first cog will land in each gap of the second cog. There are nine cogs in total, having 4, 5, 6, 7, 8, 9, 10, 11, and 12 teeth, respectively.

Instructions:

While the instructions are fairly straightforward for an adult mind, no consideration is given to students who are novices to following instructions. The working memories of the grade five students quickly got overloaded and I had to intervene by telling them one direction at a time. Almost every group came to me or the teacher to ask us what to do since the instructions for the students were not clearly expressed on the handout. It was not that the students were not listening at the beginning of the lesson, it was because they would read the lesson, be overwhelmed by the number of tasks they had to do, and lose their indication of where to begin.

To counter this, I began by telling the pairs of students to cut out the nine different cogs. This was a bit of an annoyance, since it took more than half the class for the grade five students (even while they were working in pairs) to cut the cogs out. So now the students were learning how to cut along the lines properly, rather than focusing on the mathematics that the lesson was supposed to be engaging the students in. I felt more like a babysitter for the first half of this class than a mathematics teacher.

After a pair of students had successfully cut out the cogs that they were going to use, they again got overwhelmed by the instructions. Think about it: students were expected to colour in one tooth, move the cogs around, follow where the coloured tooth would land, keep track of all the places where the coloured tooth landed, know what a cog was, know what a “tooth” (in reference to a cog) was, and then start to find patterns all while working with a partner. Talk about working memory overload! For every group, me or the teacher had to model how to colour in one tooth and how to keep track of the gaps where the coloured tooth landed. Again more wasted time.

Key Questions:

The lesson focuses around the following key questions:

1) Which cogs have you found that work so far?

2) Which pairs didn’t work and can you explain why?

3) How could you predict if a pair will work before you try them?

Before we even get too far into this section, what is meant by the word “work”? It is such a vague word in this context. How will a grade five mind interpret this question, especially if he/she has no schema for cogs? Does “work” mean the coloured tooth enters the same gap each time? Does it mean that the coloured tooth enters only a few gaps? Maybe all the cogs “work” in the sense that they can rotate around each other! So there is a lot of potential confusion around this vague term. The teacher and myself took it to mean that the coloured tooth would land in each of the gaps of a different cog. Yet another aspect of the lesson that had to be explained to the students.

So now we get to the meat and potatoes, so to speak. I want you to note that the students had no prior knowledge of LCM, GCF, factors, prime numbers, etc. I know the big ideas being promoted here are the ideas of the GCF and factors. Two cogs will “work” when the GCF of the number of teeth is 1. For example, I know that GCF(5,7) = 1 so the cogs with 5 teeth and 7 teeth will “work” in the sense that all gaps will be filled by the full rotation of one coloured tooth. I also know that GCF(4,8) = 4, so the cogs with 4 teeth and 8 teeth will not “work.” This knowledge comes from a deep understanding of prime numbers and their relationships with other numbers – knowledge that a novice student does not have.

Next, go back and look at the choices we have for number of teeth. Do you notice a major flaw? The only odd number that is not prime on our list is 9, and we do not have a cog with 3 teeth, so GCF(5,7,9,11) = 1. Thus, all of the odd-toothed cogs are going to “work.” So a common misconception of the students was to state: “If two cogs have an odd number of teeth, then they work.” This is certainly true of the activity (because it has a major design flaw), but we know this is not true in practice: GCF(3,9) = 3, so a 3-toothed and a 9-toothed cog would not “work.”

Similarly, since the only pair of cogs sharing a factor of 3 are the 6-toothed and 9-toothed cog, most students didn’t even get to this pair (there are C(9,2) = 9!/7!2! = 36 different combinations to choose from, or a 1/36 = 2.78% chance of picking this pair). So they, again, often concluded incorrectly that a pair of cogs, one with an even number of teeth and one with an odd number of teeth, would always “work.”

Concluding Remarks:

The first major problem of the activity is that it doesn’t meet students at their cognitive level. The instructions overloaded the working memories of the grade five students, even after the teacher broke the lesson down into more manageable chunks. At this age level, students likely have no idea what a cog is, or what a tooth is (in this context) and will eat up a lot of working memory space just processing that. Then they have to add in the colouring of the teeth and the movement of the cogs – all of this definitely becomes very overwhelming for novice learners!

The second major flaw is the poor design. For a lesson that is centered around GCF and factors, one would hope that the misconceptions of GCF(2m+1,2n+1) = 1 and GCF(2m,2n+1) = 1 could be avoided. Realistically, addition of a 3-toothed cog, and removal of the 11-toothed cog would do the trick.

While the design of the activity is to have students using cogs to discuss the ideas of factors and the GCF of two numbers, the lesson falls utterly short of what its intended goal is. When introducing such an important idea as factors and the GCF of two numbers, does it not make more sense to use explicit instruction followed by discussion of some worked examples? This would not be a difficult thing to do using pattern-recognition (skip-counting and making lists for example). Also, explicit instruction has the advantage of not creating the misconceptions that this poorly-designed activity does.

I do want to remark that I actually enjoy the premise of the activity. I believe that it would be interesting to do after the students have an elementary notion of what factors and the GCF are. If students have some preliminary knowledge, then the discussion may reinforce their prior knowledge. Also, the lesson extends nicely to the ideas of prime numbers and the LCM. We can keep track of how many teeth hit the first cog before the coloured tooth comes back to the start to find the LCM of the two cogs, for example.

Altogether, as a teaching tool, the cog activity is not worth it. Maybe if it was redesigned a bit to account for some of the cognitive overload it places on the students, and to remove some of the possible misconceptions that students may create. Even then, I probably wouldn’t use it as an introductory activity for factors or the GCF (although it does have nice potential to reinforce prior knowledge in my opinion). Introduction of these key ideas is better done through explicit instruction and working with patterns of whole numbers. If I did use this activity, I would definitely decrease the number of cogs (remove some of the prime toothed ones), and cut them out myself so I am not wasting copious amounts of precious class time!

To leave you with some food for thought: Did you notice that my discussion of the GCF and LCM are not on the teacher handout? Go back and check for yourself – there is no connection to mathematics at all on this handout! Do you not find it odd that the most interesting aspects of this activity are not even mentioned to the teachers who would be using the activity? It makes me wonder if Jo Boaler has the best interests of the students/teachers in mind. How can students/teachers develop “rich and meaningful connections” to mathematics if the proper mathematical vocabulary isn’t even introduced? Very bizarre indeed.

Soft Skills Programs: Yea or Nay?

“Soft skills get little respect, but will make or break your career.” -Peggy Klaus

Quite a few teachers over the past few years seem to have bought into the idea of teaching “soft skills” in the classroom. From what I can tell, “soft skills” encompass (but are not limited to) the following: collaboration, citizenship, grit, leadership, conflict management, and a whole whack of others. This blog is a response to a photo that creeped across my twitter feed the other day by @danieldmccabe, who, to my knowledge is an assistant principle and co-moderator of a twitter chat #NYEDchat.

The real trouble I have with these so-called “soft skills” is that teachers really haven’t critically thought about what it means for their classrooms. Here is some food for thought on this issue.


Where did “Soft Skills” Originate?
While it may be possible to date “soft skills” programs back many centuries, there is one in particular that caught my attention as I was researching. In the 1960s, James Comer developed a program called the Comer School Developmental Program which had six cornerstones for child development in low socioeconomic areas:

1) Physical – helping children understand their development so that they may grow up as healthy individuals.

2) Cognitive – the ability to think critically and analyze information, but also to have mastery in required content areas.

3) Language – ability to understand, process and interpret written, verbal and non-verbal cues.

4) Social – helping students to maintain healthy relationships, even during times of stress.

5) Ethical – to help students with their capacity to judge situations with fairness and to maintain a sense of justice.

6) Psychological – ability to manage emotions, self-esteem and self-awareness, and to help foster a positive sense of self.

The original program had success in low socioeconomic areas.  However, let’s note a few key differences between this program and what is happening currently.

Teachers ARE NOT Psychologists
First, this program was designed by a PhD in child psychiatry, was implemented in a carefully designed and controlled setting for a specific group of individuals, and likely took years of precise and meticulous planning. Nowadays, it seems as if everyone and their grandma pretends they have a PhD in psychology and implements a curriculum that contains “soft skills” without critically thinking about what they are doing (take Growth Mindset as the current example). Teaching something like perseverance or self-awareness, which are complex psychological components, has become so over-simplified that it has serious potential to harm students. If you want to read more about the dangers of  “pop-psychology” and “soft psychotherapy” see this blog by @C_Hendrick. Also of interest is this blog by @polymathish that gives insight into Alberta’s Teacher Advisory program, where time and content has been cut so that teachers – who may or may not be trained in psychology – educate students in certain “skills” to give the illusion that “individual student needs” are being met. This is despite the research-based evidence that indicates there is no distinct advantage of having a Teacher Advisory program.

Removal of Content Knowledge
And this ties in to my second beef with “soft skills” teaching (and discussed in @polymathish’s blog as well): notice that Comer’s original program does not remove the requirement that students need to demonstrate mastery of content knowledge. This is a key point that I feel is certainly not addressed in today’s “soft skills” programs – likely due to the fact that current practices are neither carefully planned nor based on good research.

Many of the teachers I speak with barely have enough time to cover all of the outcomes in the curriculum over the course of the year without any extra activities. This implies that there must be a trade-off for the inclusion of “soft skills” activities into an already packed curriculum. So what gets removed from the curriculum then? Content knowledge. Students are being asked less and less to recall facts and apply basic skills, and are being asked more and more to problem solve open-ended and “context rich” questions. Fact recall and basic skills are fundamental to any program involving mathematics or science, and the problem with a “soft skills” program is that repeated practice with the fundamentals are tossed out the window in favor of a more problem-based approach. Many research papers I have recently read have shown that the basic mathematical skills of Canadian students entering first-year calculus is dropping at an alarming rate. In one study, more than 50% of the first-year students entering university-level calculus had computational skills equivalent to a student leaving elementary/middle school (Gr. 6-8).

Some might argue that students who master the basic skills do not understand the material. This trade-off is centered on the fallacy that students do not need to memorize facts to understand the content. This fallacy is thoroughly debunked in Daisy Christodoulou’s book 7 Myths About Education. Long story short – automatic fact recall allows us to remove the constraints of our working memory, and the facts we commit to our long-term memory help us to create understanding and become efficient problem solvers.  In addition to this, a ton of research within the last fifty years has shows us that use of problem-based learning and open-ended questions that introduce students to “content-rich” problems do not lead to student learning (some even report alearning loss!). See this article by Kirschner/Sweller/Clark, Project Follow Through and The Sutton Trust Report to name a few.

Some Closing Thoughts
So hopefully now you can see why I want you to thoroughly think about your implementation of a “soft skills” program, and why I get a little edgy when teachers promote “soft skills” in their classrooms.

Are your students still required to master content knowledge in your “soft skills” program, or has content knowledge been removed at the expense of teaching these skills? Has the program in your class or at your school been carefully planned, and founded off of rigorous scientific research in psychology that shows that implementing a program dedicated to “soft skills” improves student learning/achievement? Are there dedicated psychologists working with you to ensure minimal harm, or have you become the “psychologist” despite not having the credentials? Some food for thought for you to ponder over.

CLT Part I – Problems with Problem Solving

“Primary knowledge is learned easily, automatically and unconsciously and cannot be taught. Secondary knowledge is culturally acquired, … learned consciously with effort and should be explicitly taught.” -Sweller, Ayres & Kalyuga

I have started to read “Cognitive Load Theory” by Sweller, Ayres and Kalyuga. I intend to try to reflect on each section as it arises to help solidify my own knowledge and to invite those of you willing to read into my thoughts.

Just as we often do with a mathematical lesson, we first begin with some definitions.

Biologically Primary Knowledge
Geary describes biologically primary knowledge as skills that are readily learned because we have evolved to acquire them for biological survival. Primary knowledge is learnable, but not teachable. For instance, we come to acquire our first language, not by being taught, but by determining ourselves the varying motions of the breath, lips and tongue required to produce certain sounds. We readily acquire this skill because we have evolved to do so, irrespective of our culture. Individuals simply need to be members of a functioning society to attain this biologically primary knowledge. For now, we will assume that any skill area in which we can acquire information without first being explicitly taught is a biologically primary skill.

Biologically Secondary Knowledge
Biologically secondary knowledge, on the other hand, is knowledge that we have not encountered in our evolutionary journey as a species. We can assimilate this knowledge; however, it needs to be explicitly taught within a culture. Secondary knowledge is both learnable and teachable. However, acquiring secondary knowledge takes much effort and is done consciously. For example, while learning to speak is biologically primary, learning proper etiquette when speaking, such as saying “I am a boy.” rather than “I is a boy.” must be explicitly taught.

Problem Solving as Biologically Primary
Reflecting a bit on the phrase “problem solving” we might believe that this is a secondary skill. However, deeper reflection allows us to see that “problem solving” in its simplest sense (i.e. solving a problem) is biologically primary. Certainly our ancestors developed general strategies to solve basic problems such as how to find an efficient way to cross a river. A general problem-solving strategy called means-end analysis is well-known, and is argued to be biologically primary due to the fact that it is utilized in many problem domains, yet is not an explicitly taught skill.

Means-end analysis uses forward and backward reasoning over a problem space. There are two states: the current state and the goal state. Differences between these two states are used to determine actions that can lead to smaller differences between the two states. When an action is performed, we check to see if the current state is equal to the goal state; if not, we recursively continue until the goal state is reached.

For example, we might pose the problem “Who was the seventh president of the United States of America?”. Our current state (unless we know this fact) is that we don’t know his/her name, and our goal state is the name of the seventh president of the USA. We might reason that a Google search would be helpful in determining this information, so we type “seventh president of USA” into Google’s search bar (an action leading to a smaller difference in our two states). Now our current state has a page of relevant websites. Since the current and goal states are still different, we continue by clicking the first link, which is a Wikipedia article on Andrew Jackson (another action in hopes to simplify the difference of the two states). Finally, we reason that if we read the first sentence of this article, we might have our answer. Indeed, Wikipedia reveals that Andrew Jackson was the seventh president, so our current and goal states are equivalent. We are done our means-end analysis.

Instructional Consequences
As you can see from our previous example, means-end analysis, while biologically primary, is actually more complex cognitively than simply knowing the fact that Andrew Jackson was the seventh president. If we know this fact, we can by-pass the strain of using means-end analysis to determine the goal state. Of course, since you are likely an adult, you know how to use a search page relatively easily (you have already assimilated this secondary knowledge) so the strain on your cognitive processes might be minimal. For a more novice learner, the action of searching for this information using Google may be quite a strain on their cognitive processes. In either case, it is important to note that having domain-specific knowledge (i.e. knowing that Andrew Jackson was the seventh president of the United States) helps reduce the cognitive processes associated with general problem solving strategies such as means-end analysis.

To continue this discussion, let’s think about a mathematics problem such as simplifying 51 + 52 + 53 + 54 + … + 100. We could attempt to use means-end analysis by adding 51+52=103, then 103+53=156, then …, but this would be terribly taxing on our working memory (and we might be more likely to make an error due to this strain). If we notice, however, that 51+100=151, and that 52+99=151 and that 53+98=151, …, we may note that 51+52+…+100 = 25 x 151 = 3775. But the crux here is that we may not have thought of the second strategy unless we have either been explicitly shown this, or we have encountered a similar problem (and we reason through this problem thinking of the previous one). Again, we see that having domain-specific knowledge (i.e. having seen a similar problem) reduces the cognitive demand of this problem.

So should we teach problem solving to our students?

Well this depends on what your end-goal is as you teach problem solving. General problem solving skills are biologically primary, so we do not need to teach these skills. For instance, if your end-goal is to teach your students a general skill like “think of a similar problem”, it is probably not a beneficial problem solving session. Teaching domain specific-knowledge, such as giving worked examples, is likely to be very beneficial for students, as they can then use these worked examples to help them reason through future problems. In addition to this, the more domain-specific knowledge a student has, the easier problem solving becomes since domain-specific knowledge often lessens the cognitive processes of a more general problem solving strategy like means-end analysis (see the two examples given above).

Summary
1) There are two types of knowledge to think about: biologically primary knowledge, which is learned, but cannot be taught; and biologically secondary knowledge, which can be both learned and taught (but may take more effort than primary knowledge).

2) General problem solving strategies are a part of our biologically primary knowledge. We do not need to explicitly teach these skills. In fact, teaching general problem solving skills such as “think backwards” or “eliminate possibilities” is probably no more useful than a means-end analysis.

3) Having domain-specific knowledge is helpful when faced with a problem solving situation. In fact, the more domain-specific knowledge we have, the more likely we are to be able to find a strategy that will be useful to solve a particular problem.

Of course, I appreciate any thoughts.