# Spreadsheets, statistics, mathematics and computational thinking

We need to teach all our students how to design, create, test, debug and use spreadsheets. We need to teach this integrated with mathematics, statistics and computational thinking. Spreadsheets can be a valuable tool in many other subject areas including biology, physics, history and geography, thus facilitating integrated learning experiences.

Spreadsheets are versatile and ubiquitous – and most have errors. A web search on “How many spreadsheets have errors?” gives alarming results. The commonly quoted figure is 88%. These spreadsheets with errors are not just little home spreadsheets for cataloguing your Lego collection or planning your next vacation. These spreadsheets with errors involve millions of dollars, and life-affecting medical and scientific research.

# Using spreadsheets to teach statistics

## Use a spreadsheet to draw graphs

One of the great contributions computers make to statistical analysis is the ability to display graphs of non-trivial sets of data without onerous drawing by hand. In the early 1980s I had a summer job as a research assistant to a history professor. One of my tasks was to create a series of graphs of the imports and exports for New Zealand over several decades, illustrating the effect of the UK joining the Common Market (now the EU). It required fastidious drawing and considerable time. (And correcting fluid) These same graphs can now be created almost instantaneously, and the requirement has shifted to interpreting these graphs.

Similarly, in the classroom we should not be requiring students of any age to draw statistical graphs by hand. Drawing statistical graphs by hand is a waste of time. Students may enjoy creating the graphs by hand – I understand that – it is rewarding and not cognitively taxing. So is colouring in. The important skill that students need is to be able to read the graph – to find out what it is telling them and what it is not telling them. Their time would be far better spent looking at multiple graphs of different types, and learning how to report and critique them. They also need to be able to decide what graph will best show what they are looking for or communicating. (There will be teachers saying students need to draw graphs by hand to understand them. I’d like to know the evidence for this claim. People have said for years that students need to calculate standard deviation by hand to understand it, and I reject that also.)

At primary school level, the most useful graph is almost always the bar or column chart. These are easily created physically using data cards, or by entering category totals and using a spreadsheet. Here is a video showing just how easy it is.

## Use a spreadsheet for statistical calculations

Spreadsheets are also very capable of calculating summary statistics and creating hypothesis tests and confidence intervals. Dedicated statistical packages are better, but spreadsheets are generally good enough. I would also teach pivot-tables as soon as possible, but that is a topic for another day.

# Using spreadsheets to teach mathematics

Spreadsheets are so versatile! Spreadsheets help students to understand the concept of a variable. When you write a formula in a cell, you are creating an algebraic formula. Spreadsheets illustrate the need for sensible rounding and numeric display. Use of order of operations and brackets is essential. They can be used for exploring patterns and developing number sense. I have taught algebraic graphing, compared with line fitting using spreadsheets. Spreadsheets can solve algebraic problems. Spreadsheets make clear the concept of mathematics as a model. Combinatorics and Graph Theory are also enabled through spreadsheets. For users using a screenreader, the linear nature of formulas in spreadsheets makes it easier to read.

# Using spreadsheets to teach computational thinking

In New Zealand we are rolling out a new curriculum for information technology, including  computational thinking. At primary school level, computational thinking includes “[students] develop and debug simple programs that use inputs, outputs, sequence and iteration.” (Progress outcome 3, which is signposted to be reached at about Year 7) Later the curriculum includes branching.

In most cases the materials include unplugged activities, and coding using programmes such as Scratch or Java script. Robots such as Sphero and Lego make it all rather exciting.

All of these ideas can also be taught using a spreadsheet. Good spreadsheet design has clear inputs and outputs. The operations need to be performed in sequence, and iteration occurs when we have multiple rows in a spreadsheet. Spreadsheets need to be correct, robust and easy to use and modify. These are all important principles in coding. Unfortunately too many people have never had the background in coding and program design and thus their spreadsheets are messy, fragile, oblique and error-prone.

When we teach spreadsheets well to our students we are giving them a gift that will be useful for their life.

I designed and taught a course in quantitative methods for business, heavily centred on spreadsheets. The students were required to use spreadsheets for mathematical and statistical tasks. Many students have since expressed their gratitude that they are capable of creating and using spreadsheets, a skill that has proved useful in employment.

# Why decimals are difficult

Recently a couple of primary teachers admitted a little furtively to me that they “never got decimals”. It got me wondering about what was difficult about decimals. For people who “get” decimals, they are just another number, with the decimal point showing. Clearly this was not the case for all.

So in true 21st century style I Googled it: “Why are decimals difficult”

I got some wonderfully interesting results, one of which is a review paper by Hugues Lortie-Forgues, Jing Tian and Robert S. Siegler, entitled “Why is learning fraction and decimal arithmetic so difficult?”, which I draw on in this post.

# You need to know

For teachers of statistics, this is important. In particular, students learning about statistics sometimes have difficulty identifying if a p-value of 0.035 is smaller or larger than the alpha value of 0.05. In this post I talk about why that may be. I will also give links to a couple of videos that might be helpful for them. For teachers of mathematics it might give some useful insights.

# Whole numbers and rational numbers

Whole numbers are the numbers we start with when we begin to learn maths – 1, 2, 3, 4,… and 0. Zero has an interesting role of having no magnitude in itself, but acting as a place-filler to make sure we can tell the meaning of a number. Without zero, 2001 and 201 and 21 would all look the same! From early on we recognise that longer numbers represent larger quantities. We know that a salary with lots of zeroes is better than one with only a few. \$1000000 is more than \$200 even though 2 is greater than 1.

Rational numbers are the ones that come in between, but also include whole numbers. All of the following are considered rational numbers: ½, 0.3, 4/5, 34.87, 3¾, 2000

When we talk about whole numbers, we can say what number comes before and after the number. 35 comes before 36. 37 comes after 36. But with rational numbers, we cannot do this. There are infinite rational numbers in any given interval. Between 0 and 1 there are infinite rational numbers.

Rational numbers are usually expressed as fractions (½, 3¾) or decimals (0.3, 34.87).

There are several things that make rational numbers (fractions and decimals) tricky. In this post I focus on decimals

# Decimal notation and size of number

As I explained before, when we learn about whole numbers, we learn a useful rule-of-thumb that longer strings of digits correspond to larger numbers. However, the length of the decimal is unrelated to its magnitude. For example, 10045 is greater than 230. The longer number corresponds to greater magnitude. But 0.10045 is less than 0.230. We look at the first digit after the point to find out which number is bigger. The way that you judge which is bigger out of two decimals is quite different from how you do it with whole numbers. The second of my videos illustrates this.

# Effect of multiplying by numbers between 0 and 1

The results of multiplying by decimals between 0 and 1 are different from what we are used to.

When we learn about multiplication of whole numbers, we find that when we multiply, the answer will always be bigger than both of the numbers we are multiplying.
3 × 4 = 12. 12 is greater than either 3 or 4.
However, if we multiply 0.3 × 0.4 we get 0.12, which is smaller than either 0.3 and 0.4. Or if we multiply 6 by 0.4, we get 2.4, which is less than 6, but greater than 0.4. This can be quite confusing.

## Aside for statistics teachers

In statistics we often quote the R squared value from regression. To get it, we square r, the correlation coefficient, and what is quite a respectable value, like 0.6, gets reduced to a mere 0.36.

# Effect of dividing by decimals between 0 and 1

Similarly, when we divide whole numbers by whole numbers, the answer will be less than the number we are dividing. 100 / 5 = 20. Twenty is less than 100, but in this case is greater than 5.  But when we divide by a decimal between 0 and 1 it all goes crazy and things get bigger! 100/ 0.5 = 200. People who are at home with all this madness don’t notice it, but I can see how it can alarm the novice.

# Decimal arithmetic doesn’t behave like regular arithmetic

When we add or subtract two numbers, we need to line up the decimal places, so that we know that we are adding values with corresponding place values. This is looks different from the standard algorithm where we line up the right-hand side. In fact it is the same, but because the decimal point is invisible, it doesn’t seem the same.

## Method for multiplication of decimals

When you multiply numbers with decimals in, you do it like regular multiplication and then you count the number of digits to the right of the decimal in each of the factors and add them together and that is how many digits to have to the right of the decimal in the answer! I have a confession here. I know how to do this, and have taught how to do this, but I don’t recall ever working out why we do this or getting students to work it out.

## Method for division of decimals

Is this even a thing? My immediate response is to use a calculator. I seem to remember moving the decimal point around in a somewhat cavalier manner so that it disappears from the number we are dividing by. But who ever does long division by hand?

Okay teacher friends – I now see why you find decimals difficult.

The paper talks about approaches that help. The main one is that students need to spend time on understanding about magnitude.

My suggestion is to do plenty of work using money. Somehow we can get our heads around that.

And use a calculator, along with judicious estimation.

Here are two videos I have made, to help people get their heads around decimals.

# Statistical software for worried students

Statistical software for worried students: Appearances matter

Let’s be honest. Most students of statistics are taking statistics because they have to. I asked my class of 100 business students who choose to take the quantitative methods course if they did not have to. Two hands went up.

Face it – statistics is necessary but not often embraced.

But actually it is worse than that. For many people statistics is the most dreaded course they are required to take. It can be the barrier to achieving their career goals as a psychologist, marketer or physician. (And it should be required for many other careers, such as journalism, law and sports commentator.)

## Choice of software

Consequently, we have worried students in our statistics courses. We want them to succeed, and to do that we need to reduce their worry. One decision that will affect their engagement and success is the choice of computer package. This decision rightly causes consternation to instructors. It is telling that one of the most frequently and consistently accessed posts on this blog is Excel, SPSS, Minitab or R. It has been  viewed 55,000 times in the last five years.

The problem of which package to use is no easier to solve than it was five years ago when I wrote the post. I am helping a tertiary institution to re-develop their on-line course in statistics. This is really fun – applying all the great advice and ideas from ”
Guidelines for Assessment and Instruction in Statistics” or GAISE. They asked for advice on what statistics package to use. And I am torn.

## Requirements

Here is what I want from a statistical teaching package:

• Easy to use
• Attractive to look at (See “Appearances Matter” below)
• Good instructional materials with videos etc (as this is an online course)
• Supports good pedagogy

If I’m honest I also want it to have the following characteristics:

• Guidance for students as to what is sensible
• Only the tests and options I want them to use in my course – not too many choices
• An interpretation of the output
• Data handling capabilities, including missing values
• A pop up saying “Are you sure you want to make a three dimensional pie-chart?”

Is this too much to ask?

Possibly.

## Overlapping objectives

Here is the thing. There are two objectives for introductory statistics courses that partly overlap and partly conflict. We want students to

• Learn what statistics is all about
• Learn how to do statistics.

They probably should not conflict, but they require different things from your software. If all we want the students to do is perform the statistical tests, then something like Excel is not a bad choice, as they get to learn Excel as well, which could be handy for c.v. expansion and job-getting. If we are more concerned about learning what statistics is all about, then an exploratory package like Tinkerplots or iNZight could be useful.

Ideally I would like students to learn both what statistics is all about and how to do it. But most of all, I want them to feel happy about doing statistical analysis.

## Appearances matter

Eye-appeal is important for overcoming fear. I am confident in mathematics, but a journal article with a page of Greek letters and mathematical symbols, makes me anxious. The Latex font makes me nervous. And an ugly logo puts me off a package. I know it is shallow. But it is a thing, and I suspect I am far from alone. Marketing people know that the choice of colour, word, placement – all sorts of superficial things effect whether a product sells. We need to sell our product, statistics, and to do that, it needs to be attractive. It may well be that the people who design software are less affected by appearance, but they are not the consumers.

## Terminal or continuing?

This is important: Most of our students will never do another statistical analysis.

Most of our students will never do another statistical analysis.

Here are the implications: It is important for the students to learn what statistics is about, where it is needed, potential problems and good communication and critique of statistical results. It is not important for students to learn how to program or use a complex package.

Students need to experience statistical analysis, to understand the process. They may also discover the excitement of a new set of data to explore, and the anticipation of an interesting result. These students may decide to study more statistics, at which time they will need to learn to operate a more comprehensive package. They will also be motivated to do so because they have chosen to continue to learn statistics.

## Excel

In my previous post I talked about Excel, SPSS, Minitab and R. I used to teach with Excel, and I know many of my past students have been grateful they learned it. But now I know better, and cannot, hand on heart recommend Excel as the main software. Students need to be able to play with the data, to look at various graphs, and get a feel for variation and structure. Excel’s graphing and data-handling capabilities, particularly with regard to missing values, are not helpful. The histograms are disastrous. Excel is useful for teaching students how to do statistics, but not what statistics is all about.

## SPSS and Minitab

SPSS was a personal favourite, but it has been a while since I used it. It is fairly expensive, and chances are the students will never use it again. I’m not sure how well it does data exploration. Minitab is another nice little package. Both of these are probably overkill for an introductory statistics course.

## R and R Commander

R is a useful and versatile statistical language for higher level statistical analysis and learning but it is not suitable for worried students. It is unattractive.

R Commander is a graphical user interface for R. It is free, and potentially friendlier than R. It comes with a book. I am told it is a helpful introduction to R. R Commander is also unattractive. The book was formatted in Latex. The installation guide looks daunting. That is enough to make me reluctant – and I like statistics!

The screenshot displayed on the front page of R Commander

## iNZight and iNZight Lite

I have used iNZight a lot. It was developed at the University of Auckland for use in their statistics course and in New Zealand schools. The full version is free and can be installed on PC and Mac computers, though there may be issues with running it on a Mac. The iNZight lite, web-based version is fine. It is free and works on any platform. I really like how easy it is to generate various plots to explore the data. You put in the data, and the graphs appear almost instantly. IiNZIght encourages engagement with the data, rather than doing things to data.

For a face-to-face course I would choose iNZight Lite. For an online course I would be a little concerned about the level of support material available. The newer version of iNZight, and iNZight lite have benefitted from some graphic design input. I like the colours and the new logo.

## Genstat

I’ve heard about Genstat for some time, as an alternative to iNZight for New Zealand schools, particularly as it does bootstrapping. So I requested an inspection copy. It has a friendly vibe. I like the dialog box suggesting the graph you might like try. It lacks the immediacy of iNZight lite. It has the multiple window thing going on, which can be tricky to navigate. I was pleased at the number of sample data sets.

## NZGrapher

NZGrapher is popular in New Zealand schools. It was created by a high school teacher in his spare time, and is attractive and lean. It is free, funded by donations and advertisements. You enter a data set, and it creates a wide range of graphs. It does not have the traditional tests that you would want in an introductory statistics course, as it is aimed at the NZ school curriculum requirements.

## Statcrunch

Statcrunch is a more attractive, polished package, with a wide range of supporting materials. I think this would give confidence to the students. It is specifically designed for teaching and learning and is almost conversational in approach. I have not had the opportunity to try out Statcrunch. It looks inviting, and was created by Webster West, a respected statistics educator. It is now distributed by Pearson.

## Jasp

I recently had my attention drawn to this new package. It is free, well-supported and has a clean, attractive interface. It has a vibe similar to SPSS. I like the immediate response as you begin your analysis. Jasp is free, and I was able to download it easily. It is not as graphical as iNZight, but is more traditional in its approach. For a course emphasising doing statistics, I like the look of this.

Data, controls and output from Jasp

# Conclusion

So there you have it. I have mentioned only a few packages, but I hope my musings have got you thinking about what to look for in a package. If I were teaching an introductory statistics course, I would use iNZight Lite, Jasp, and possibly Excel. I would use iNZight Lite for data exploration. I might use Jasp for hypothesis tests, confidence intervals and model fitting. And if possible I would teach Pivot Tables in Excel, and use it for any probability calculations.

This is a very important topic and I would appreciate input. Have I missed an important contender? What do you look for in a statistical package for an introductory statistics course? As a student, how important is it to you for the software to be attractive?

# Improvisation in the Mathematics Classroom

The following is a guest post by Andrea Young, requested by Dr Nic Petty.

# Improvisation comedy

Improvisation comedy, or improv for short, is theater that is unscripted.  Performers create characters, stories, and jokes on the spot, much to the delight of audience members.  Surprisingly, the goal of improv is not to be funny!  (Or maybe this isn’t surprising–people trying hard to be funny rarely succeed.)  Rather, improv comedians are encouraged to be “in the moment,” to support their fellow players, and to take risks–the humor follows as a natural consequence.

What does this have to do with mathematics and mathematics education?  If you are a math teacher or professor, you might want to have a classroom where students are deeply engaged with the lesson (i.e. are “in the moment”), actively collaborating with peers (i.e. supporting their fellow players), and willing to make mistakes (i.e. taking risks).  In other words, you want them to develop the skills that improvisers are trained in from their very first improv class.

I started taking improv classes in 2002 at the Hideout Theatre in Austin, TX right around the same time I started a Ph.D. program in mathematics at the University of Texas at Austin.  I realized that the dynamics being developed in my improv classes and troupes were exactly the ones I wanted to develop among the students in my math classes.  So I started using improv games and exercises in my courses.  And I haven’t stopped.  I have now taught mathematics to hundreds of college students, and in every course, I have incorporated some amount of improv.  I have given workshops and presentations to mathematicians, high school teachers, and students about how to use improv to improve group dynamics or to foster communication.   It is powerful to see joy and play cultivated in a college-level mathematics course.  Anecdotally, these techniques work–not for every student, every time–but for enough students enough of the time that I keep using my old favorites and finding new ones to try.

Andrea Young teaches math using Improv principles and games

## Some improv exercises to try

Here are three exercises that you might try in your own math classes.  I use these in college classes, but they are easily (and some might argue, more readily) adaptable to younger ages.

Scream circle:  Have the students stand in a circle and put their heads down.  On the count of three, they should all raise their heads and look directly at another student.  If the person they are looking at is also looking at them, both students should scream and leave the circle.  If the other person is not looking at them, they put their head back down.  The game continues until there is only one or two (depending on group size) left.

This exercise is a great way to pair up students to work together.  It also develops the idea of risk-taking because students are encouraged to scream as loud as they can.  It is also quick–depending on the size of the class, this can take fewer than 2 minutes.

Five-headed expert:  Have five students come to the front of the room and stand in a line.  This can be played a few ways.   Here are two:

1. The students respond to questions one word at a time, as though they are five heads on the same body. Introduce the visiting “expert” and ask them questions, related to course content.  Time permitting, have the class ask questions.
2. The students respond to questions all in one voice. Otherwise, the game is the same.

This game is a fun way to review concepts and definitions. (For example, what is the limit definition of the derivative?)  It also works on the skills of collaboration and being “in the moment.”  Students must  listen to each other and work together to say things that make sense.

For an example of how this game works in an improv performance, watch this video from the improv group Stranger Things Have Happened.

I am a tree:   Have the students stand in a circle.  One student walks to the center and makes an “I am” statement while striking a pose.  The next student enters the circle and adds to the tableau with another “I am” statement.  A third (and probably final student) enters the tableau like the second.   The professor then clears the tableau, either leaving one of the students to repeat their “I am” statement or not.

This game really highlights the need for collaboration, especially when used in a math context.  I use this as a review or as a way to synthesize concepts. For example, this could be used to review different sets of numbers.  Student one might start with “I am the set of real numbers” and hold his or her arms in a big circle to indicate a set.  Student two could enter the “set” and say, “I am the rationals.”  Another student might intersect the reals with their arms and say, “I am the complex numbers.”

For an introduction to I am a tree, check out this demonstration video from my former improv teacher and troupe mate, Shana Merlin of Merlin Works.

## Courage and innovation

I use a lot of active learning techniques in my classes, and I have found improv exercises to be a quick and fun way to develop some of the non-mathematical skills that my students need to be successful in my classroom.  It takes some courage to engage with your students in this way, but I think it is well worth it.

As a final thought, improvisational comedy techniques are not just for students. They can help professional mathematicians become better communicators and more effective teachers. They can even stimulate creativity and problem-solving skills. I encourage you to visit your local comedy theater and to sign up for an improv class.

Andrea Young and fellow trouper performing improvisational musical comedy

Andrea Young is the Special Assistant to the President and Liaison to the Board of Trustees AND an Associate Professor of Mathematical Sciences at Ripon College.  For many years, she performed improv all around the country with Girls, Girls, Girls Improvised Musicals and a variety of other Austin improv troupes.  These days she mostly does community theater, although she regularly improvises silly songs and dances for her toddler.  For more about using improv in math courses, check out mathprov.wordpress.com.

# Comment from Dr Nic

Thanks Andrea – it was so great to find someone who was already doing what I was thinking about doing. I would love to hear from other people who have used improv games and techniques in maths and statistics classes. I am learning improv at present, and like the idea of “Yes and…” I will write some more about this in time.

# There are many good ways to teach mathematics and statistics

Hiding in the bookshelves in the University of Otago Library, I wept as I read the sentence, “There are many good ways to raise children.”  As a mother of a baby with severe disabilities the burden to get it right weighed down on me. This statement told me to put down the burden. I could do things differently from other mothers, and none of us needed to be wrong.

The same is true of teaching maths and stats – “There are many good ways to teach mathematics and statistics.” (Which is not to say that there are not also many bad ways to both parent and teach mathematics – but I like to be positive.)

My previous post about the messages about maths, sent by maths and stats videos, led to some interesting comments – thanks especially to Michael Pye who “couldn’t get the chart out of [his] head”. (Nothing warms a blogger’s heart more!). He was too generous to call my description of the “procedural approach” a “straw-person”, but might have some justification to do so.

His comments (you can see the originals here) have been incorporated in this table, with some of my own ideas. In some cases the “explicit active approach” is a mixture of the two extremes. The table was created to outline the message I felt the videos often give, and the message that is being encouraged in much of the maths education community. In this post we expand it to look at good ways to teach maths.

 Procedural approach Explicit but active approach Social constructivist approach Main ideas Maths is about choosing and using procedures correctly Maths is about understanding ideas and recognising patterns Maths is about exploring ideas and finding patterns Strengths Orderly, structured, safe, cover the material, calm Orderly, structured, safe, cover the material, calm and satisfying Exciting, fun, annoying Skills valued Computation, memorisation, speed, accuracy Computation, memorisation, (not speed), accuracy + the ability to evaluate and analyse Creativity, collaboration, communication, critical thinking Teaching methods Demonstration, notes, practice Demonstration, notes, practice, guided discussion and exploration via modelling. Open-ended tasks, discussion, exploration Grouping Students work alone or in ability grouping Students discuss as a whole class or in mixed-ability groups Role of teacher Fount of wisdom, guide, enthusiast, coach. Fount of wisdom, guide, enthusiast, coach. Another learner, source of help, sometimes annoyingly oblique Attitude to mistakes Mistakes are a sign of failure Mistakes happen when we learn. (high percentage of success) Mistakes happen when we learn. Challenges Boredom, regimentation, may not develop resilience. Boredom, regimentation, could be taught purely to the test Can be difficult to tell if learning is taking place, difficult if the teacher is not confident Who (of the learners) succeeds? People like our current maths teachers Not sure – hopefully everyone! Use of worksheets and textbooks Important – guide the learning Develops mastery and provide assessment for learning. Limits gaps in understanding. Occasional use to supplement activities Role of videos Can be central Reinforce ideas and provide support out of class. Support materials

We agree that speed is not important, so why are there still timed tests and “mad minutes” .

## What is good mathematics teaching?

The previous post was about the messages sent by videos, and the table was used to fit the videos into a context. If we now examine the augmented table, we can address what we think good mathematics teaching looks like.

# For WHOM?

The biggest question when discussing what works in education is “for whom does it work?”  Just about any method of teaching will be successful for some people, depending on how you measure success. Teachers have the challenge of meeting the needs of around thirty students who are all individuals, with individual needs.

## Introversion/extraversion

I have recently been considering the scale from introvert – those who draw energy from working alone, and extraversion – those who draw energy from other people. Contrary to our desire to make everything binary, current thinking suggests that there is a continuum from totally introverted to totally extraverted. I was greatly relieved to hear that, as I have never been able to find my place at either end. I am happy to present to people, and will “work a room” if need be, thus appearing extraverted, but need to recover afterwards with time alone – thus introverted. Apparently I can now think of myself as an ambivert.

The procedural approach to teaching and learning mathematics is probably more appealing to those more at the introverted end of the spectrum, who would rather have fingernails extracted than work in a group. (And I suspect this would include a majority of incumbent maths teachers, though I am not sure about primary teachers.) I suspect that children who are more extroverted will gain from group work and community. If we choose either one of these modes of teaching exclusively we are disadvantaging one or other group.

# Different cultures

In New Zealand we are finding that children from cultures where a more social approach is used for learning do better when part of learning communities that value their cultural background and group endeavour. In Japan it is expected that all children will master the material, and children are not ability-grouped into lowered expectations. Dominant white western culture is more competitive. One way for schools to encourage large numbers of phone calls from unhappy white middle-class parents is to remove “streaming”, “setting”, or “ability grouping.”

# Silence and noise

I recently took part in a Twitter discussion with maths educators, one of whom believed that most maths classes should be undertaken in silence. One of the justifications was that exams will be taken in silence and individually. This may have worked for him, but for some students the pressure not to say anything is stifling. It also removes a great source of learning, their peers. Students who are embarrassed to ask a teacher for help can often get help from others. In fact some teachers require students to ask others before approaching the teacher.

# Moderation

As is often the case, the answer lies in moderation and variety. I would not advocate destroying all worksheets and textbooks, nor mandate frequent silent individual work. Here are some of suggestions for effective teaching of mathematics.

# Ideal maths teaching includes:

• Having variety in your approaches, as well as security
• Aiming for understanding and success
• Trying new ideas and having fun
• Embracing your own positive mathematical identity (and getting help if your mathematical identity is not positive)
• Allowing children to work at different speeds without embarrassment
• Having silence sometimes, and noise sometimes
• Being competent or getting help – a good teaching method done poorly is not a good teaching method

Here are links to other posts related to this:
The Golden Rule doesn’t apply to teaching

Educating the heart with maths and statistics

The nature of mathematics and statistics and what it means to learn and teach them

And thank you again to those who took the time to comment on the previous post. I’m always interested in all viewpoints.

# The problem with videos for teaching maths and stats

The message of many popular mathematics and statistics videos is harming people’s perceptions of the nature of these disciplines.

I acknowledge the potential for conflict of interest in this post –  critically examining the role of video in learning and teaching mathematics and statistics – when StatsLC has a YouTube channel, and also provides videos through teaching and learning systems.

But I do wonder what message it sends when people like Sal Khan of Khan Academy and Mister Woo are applauded for their well-intentioned, and successful attempts to take a procedural view of mathematics to the masses. Video by its very nature tends towards procedures, and encourages the philosophy that there is one way to do something. Both Khan and Woo, and my personal favourite, Rob Tarrou, all show enthusiasm, inclusion and compassion. And I am sure that many people have been helped by these teachers. In New Zealand various classroom teachers ‘flip” their classrooms, and allow others to benefit from their videos on YouTube. One of the strengths, according to Khan, is that individual students can proceed at their own pace. However Jo Boaler states in her book, Mathematical Mindsets, that “Sadly I have yet to encounter a product that gives individualised opportunities and also teaches mathematics well.”

So what is the problem then? Millions of students love Khan, Woo, ProfRobBob and even Dr Nic. Millions of people also love fast food, and that isn’t good as a total diet.

In my work exploring people’s attitudes to mathematics, I find that many, including maths educators, have a procedural view of mathematics, which fails to unlock the amazing potential of our disciplines.

## Procedural maths

Many people have the conception that to do mathematics is to work out the correct procedure to use in a specific instance and use it correctly in order to get the correct answer. This leads to a nice red tick. (Check mark) That was my view of maths for a very long time. I remember being most upset in my first year of university when the calculus exam was in a different format from the ones I had practised on. I was indignant and feared a C at best, and possibly even a failing grade. I liked the procedural approach. I felt secure using a procedural approach, and when I became a maths teacher, I was pretty much wedded to it. And the thing is, the procedural approach has worked very well for most of the people who are currently high school maths teachers.

Computation was an important part of mathematics

I recently read the inspiring “Hidden Figures”, about African American women who had pivotal roles in the development of space travel. For many of them, their introduction into life as a mathematician was as a computer. They did mathematical computations, and speed and accuracy were essential. I wonder how much of today’s curriculum is still aiming to produce computers, when we have electronic devices that can do all of that faster and more accurately.

# Open-ended, lively maths

In parallel to the mass-maths-educators, we have the likes of Jo Boaler and Youcubed, Dan Meyer and Desmos, Bobbie Hunter and Mathematics Inquiry Communities, Marian Small, Tracy Zager, Fawn Nguyen and pretty much the entire Math-Twitter-Blogosphere spreading the message that mathematics is open-ended, exciting and far from procedural. Students work in groups to construct and communicate their ideas. Wrong answers are valued as evidence of thinking and the willingness to take risks. Productive struggle is valued and lessons are designed to get students outside of their comfort zones, but still within their zone of proximal development. Work is collective, rather than individualised, and ability grouping is strongly discouraged.

I find this approach enormously exciting, and believe that it could change the perception of the world towards mathematics.

# The problem of the social contract

Thus I and many teachers are keen to develop a more social constructivist approach to learning mathematics at all levels. However, teachers – especially at high school – run into the problem of the implicit social contract that places the teacher as the owner of the knowledge, who is then required to distribute said knowledge to the students in the class. Students want to get the knowledge, to master the procedure and to find the right answers with as little effort or pain as possible. They are not used to working in groups, and find it threatening to their comfortably boring, procedural vision of maths class.

Some years ago I filled in for a maths teacher for a week at a school for girls from privileged backgrounds. I upset one class of Year 12 students by refusing to use up class time getting them to copy notes from the whiteboard. I figured they had perfectly good textbooks, and were better to spend their time working on examples when I was there to help them learn. Silly me! But I was breaking with what they felt was the correct way for them (and me) to behave in maths class. In fact their indignation at my failure to behave in the way they felt I should, actually did get in the way of their learning.

So who is right?

I guess my working theory is that there is a place for many types of learning and teaching in mathematics. Videos can be helpful to introduce ideas, or to provide another way of explaining things. They can help teachers to expand their own understanding, and develop confidence. Videos can provide well-thought-out images and animations to help students understand and remember concepts. They can do something the teacher cannot.  I like to think that our StatsLC videos fit in this category. Talking head or blackboard videos can act as “the kid next door” tutor, who helps a student piece something together.

Just as candy cereal can be only “part of a healthy breakfast”, videos should never be anything more than part of a learning experience.

We also want to think about what kinds of learning we want students to experience. We need our students to be able to communicate, to be creative, to think critically and problem solve and to work collaboratively. These are known as the 4 Cs of 21st Century learning. We don’t actually need people to be able to follow procedures any more. What we need is for people to be able to ask good questions, build models and answer them. I don’t think a procedural approach is going to do that.

The following table summarises some ideas I have about ways of teaching mathematics and statistics.

 Procedural approach Social constructivist approach Main ideas Maths is about choosing and using procedures correctly Maths is about exploring ideas and finding patterns Strengths Orderly, structured, safe, cover the material, calm Exciting, fun, annoying Skills valued Computation, memorisation, speed, accuracy Creativity, collaboration, communication, critical thinking Teaching methods Demonstration, notes, practice Open-ended tasks, discussion, exploration Grouping Students work alone or in ability grouping Students discuss as a whole class or in mixed-ability groups Role of teacher Fount of wisdom, guide, enthusiast, coach. Another learner, source of help, sometimes annoyingly oblique Attitude to mistakes Mistakes are a sign of failure Mistakes happen when we learn. Challenges Boredom, regimentation, may not develop resilience. Can be difficult to tell if learning is taking place, difficult if the teacher is not confident. Who succeeds? People like our current maths teachers Not sure – hopefully everyone! Use of worksheets and textbooks Important – guide the learning Occasional use to supplement activities Role of videos Can be central Support materials

If we are to have a world of mathematicians, as is our goal as a social enterprise, then we need to move away from a narrow procedural view of mathematics.

I would love to hear your thoughts on this as mathematicians, statisticians, teachers and learners. Do we need to be more careful about the messages our resources such as textbooks and videos give about mathematics and statistics?

# The Central Limit Theorem – with Dragons

To quote Willy Wonka, “A little magic now and then is relished by the best of men [and women].” Any frequent reader of this blog will know that I am of a pragmatic nature when it comes to using statistics. For most people the Central Limit Theorem can remain in the realms of magic. I have never taught it, though at times I have waved my hands past it.

Sometimes you don’t need to know.

Students who want that sort of thing can read about it in their textbooks or look it up online. The New Zealand school curriculum does not include it, as I explained in 2012.

But – there are many curricula and introductory statistics courses that include The Central Limit Theorem, so I have chosen to blog about it, in preparation to making a video. In this post I will cover what the Central Limit does. Maybe my approach will give ideas to teachers on how they might teach it.

## Sampling distribution of a mean

First let me explain what a sampling distribution is. (And let me add the term to Dr Nic’s long list of statistics terms that cause unnecessary confusion.) A sampling distribution of a mean is the distribution of the means of samples of the same size taken from the same population. The distribution of the means will be different from the distribution of values in the original population.  The Central Limit Theorem tells us useful things about the sampling distribution and its relationship to the distribution of the values in the population.

## Example using dragons

We have a population of 720 dragons, and each dragon has a strength value of 1 to 8. The distribution of the strengths goes from 1 to 8 and has a population mean somewhere around 4.5. We take a sample of four dragons from the population. (Dragons are difficult to catch and measure so it will just be 4.)

We find the mean. Then we think about what other values we might have got for samples that size. In real life, that is all we can do. But to understand what is happening, we will take multiple samples using cards, and then a spreadsheet, to explore what happens.

# Important aspects of the Central Limit Theorem

Aspect 1: The sampling distribution will be less spread than the population from which it is drawn.

Dragon example

What do you think is the largest value the mean strength of the four dragons will take? Theoretically you could have a sample of four dragons, each with strength of 8, giving us a sample mean of 8. But it isn’t very likely. The chances that all four values are greater than the mean are pretty small.  (It’s about a 6% chance). If there are equal numbers of dragons with each strength value, then the probability of getting all four dragons with strength 8 is 0.0002.

So already we have worked out that the distribution of the sample means is going to be less spread than the distribution of the original population.

Aspect 2: The sampling distribution will be well-modelled by a normal distribution.

Now isn’t that amazing – and really useful! And even more amazing, it doesn’t even matter what the underlying population distribution is, the sampling distribution will still (in most cases) look like a normal distribution.

If you think about it, it does make sense. I like to see practical examples – so here is one!

Dragon example

We worked out that it was really unlikely to get a sample of four dragons with a mean strength of 8. Similarly it is really unlikely to get a sample of four dragons with a mean strength of 1.
Say we assumed that the strength of dragons was uniform – there are equal numbers of dragons with each of the strengths. Then we find out all the possible combinations of strengths from samples of 4 dragons. Bearing in mind there are eight different strengths, that gives us 8 to the power of 4 or 4096 possible combinations. We can use a spreadsheet to enumerate all these equally likely combinations. Then we find the mean strength and we get this distribution.

Or we could take some samples of four dragons and see what happens. We can do this with our cards, or with a handy spreadsheet, and here is what we get.

Four samples of four dragons each

The sample mean values are 4.25, 5.25, 4.75 and 6. Even with really small samples we can see that the values of the means are clustering around some central point.

Here is what the means of 1000 samples of size 4 look like:

And hey presto – it resembles a normal distribution! By that I mean that the distribution is symmetric, with a bulge in the middle and tails in either direction. A normal distribution is useful for modelling just about anything that is the result of a large number of change effects.

The bigger the sample size and the more samples we take, the more the distribution of the means (the sampling distribution) looks like a normal distribution. The Central Limit Theorem gives mathematical explanation for this. I put this in the “magic” category unless you are planning to become a theoretical statistician.

Aspect 3: The spread of the sampling distribution is related to the spread of the population.

If you think about it, this also makes sense. If there is very little variation in the population, then the sample means will all be about the same.  On the other hand, if the population is really spread out, then the sample means will be more spread out too.

Dragon example

Say the strengths of the dragons occur equally from 1 to 5 instead of from 1 to 8. The spread of the means of teams of four dragons are going to go from 1 to 5 also, though most of the values will be near the middle.

Aspect 4: Bigger samples lead to a smaller spread in the sampling distribution.

As we increase the size of the sample, the means become less varied. We reduce the effect of one extreme value. Similarly the chance of getting all high values in our sample or all low values gets smaller and smaller. Consequently the spread of the sample means will decrease. However, the reduction is not linear. By that I mean that the effect achieved by adding one more to the sample decreases, depending on how big the sample is in the first place. Say you have a sample of size n = 4, and you increase it to n = 5, that is a 25% increase in information. If you have a sample n = 100 and increase it to size n=101, that is only a 1% increase in information.

Now here is the coolest thing! The spread of the sampling distribution is the standard deviation of the population, divided by the square root of the sample size. As we do not know the standard deviation of the population (σ), we use the standard deviation of the sample (s) to approximate it. The spread of the sampling distribution is usually called the standard error, or s.e.

# Implications of the Central Limit Theorem

The properties listed above underpin most traditional statistical inference. When we find a confidence interval of a mean, we use the standard error in the formula. If we used the sample standard deviation we would be finding the values between which most of the values in the sample lie. By using the standard error, we are finding the values between which most of the sample means lie.

# Sample size

The Central Limit Theorem applies best with large samples. A rule of thumb is that the sample should be 30 or more. For smaller samples we need to use the t distribution rather than the normal distribution in our testing or confidence intervals. If the sample is very small, such as less than 15, then we can still use the t-distribution if the underlying population has a normal shape. If the underlying population is not normal, and the sample is small, then other methods, such as resampling should be used, as the Central Limit Theorem does not hold.

# Reminder!

We do not take multiple samples of the same population in real life. This simulation is just that – a pretend example to show how the Central Limit Theorem plays out. When we undergo inferential statistics we have one sample, and from that we use what we know about it to make inferences about the population from which it is drawn.

## Teaching suggestion

Data cards are extremely useful tools to help understand sampling and other aspects of inference. I would suggest getting the class to take multiple small samples(n=4), using cards, and finding the means. Plot the means. Then take larger samples (n=9) and similarly plot the means. Compare the shape and spread of the distributions of the means.

The Dragonistics data cards used in this post can be purchased at The StatsLC shop.

This hour long conversation gives insights into how three high achieving women feel about mathematics. Nicola, the host, is the author of this blog, and has always had strong affection for mathematics, though this has changed in nature lately. Gina and Suzy are both strongly negative in their feelings about maths. As the discussion progresses, listen for the shift in attitude.

And here is a picture of the three of us.

Dr Nic, Gina and Suzy.

Here are some of the questions we discuss over the hour:

3. If you saw this as an opportunity to talk to people who teach mathematics, what message would you like to give them?
4. How do you feel about the idea that you could change how you feel about maths?

# Videos for teaching and learning statistics

It delights me that several of my statistics videos have been viewed over half a million times each. As well there is a stream of lovely comments (with the odd weird one) from happy viewers, who have found in the videos an answer to their problems.

In this post I will outline the main videos available on the Statistics Learning Centre YouTube Channel. They already belong to 24,000 playlists and lists of recommended resources in textbooks the world over. We are happy for teachers and learners to continue to link to them. Having them all in one place should make it easier for instructors to decide which ones to use in their courses.

# Philosophy of the videos

Early on in my video production I wrote a series of blog posts about the videos. One was Effective multimedia teaching videos. The videos use graphics and audio to increase understanding and retention, and are mostly aimed at conceptual understanding rather than procedural understanding.

I also wrote a critique of Khan Academy videos, explaining why I felt they should be improved. Not surprisingly this ruffled a few feathers and remains my most commented on post. I would be thrilled if Khan had lifted his game, but I fear this is not the case. The Khan Academy pie chart video still uses an unacceptable example with too many and ordered categories. (January 2018)

Before setting out to make videos about confidence intervals, I critiqued the existing offerings in this post. At the time the videos were all about how to find a confidence interval, and not what it does. I suspect that may be why my video, Understanding Confidence Intervals, remains popular.

# Introducing statistics

## Understanding Summary Statistics 5:14 minutes

Why we need summary statistics and what each of them does. It is not about how to calculate the statistics, but what they mean. It uses the shoe example, which also appears in the PPDAC and OSEM videos.

## Understanding Graphs 6:06 minutes

I briefly explains the use and interpretation of seven different types of statistical graph. They include the pictogram, bar chart, pie chart, dot plot, stem and leaf, scatterplot and time series.

## Analysing and commenting on Graphical output using OSEM 7:13 minutes

This video teaches how to comment on graphs and other statistical output by using the acronym OSEM. It is especially useful for students in NCEA statistics classes in New Zealand, but many people everywhere can find OSEM awesome! We use the example of comparing the number of pairs of shoes men and women students say they own.

## Variation and Sampling error 6:30 minutes

Statistical methods are necessary because of the existence of variation. Sampling error is one source of variation, and is often misunderstood. This video explains sampling error, along with natural variation, explainable variation and variation due to bias. There is an accompanying video on non-sampling error.

## Sampling methods 4:54 minutes 500,000 views

This video describes five common methods of sampling in data collection – simple random, convenience, systematic, cluster and stratified. Each method has a helpful symbolic representation.

## Types of data 6:20 minutes 600,000 views

The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. This video is particularly popular at the start of courses.

## Important Statistical concepts 5:34 minutes 50,000 views

This video does not receive the views it deserves, as it covers three really important ideas. Maybe I should split it up into three videos. The ideas are the difference between significance and usefulness, evidence and strength of effect, causation and association.

Other videos complementary to these, but not on YouTube are:

• The statistical enquiry process
• Understanding the Box Plot
• Non-sampling error

# Videos for teaching hypothesis testing

## Understanding Statistical inference 6:46 minutes 40,000 views

The most difficult concept in statistics is that of inference. This video explains what statistical inference is and gives memorable examples. It is based on research around three concepts pivotal to inference – that the sample is likely to be a good representation of the population, that there is an element of uncertainty as to how well the sample represents the population, and that the way the sample is taken matters.

## Understanding the p-value 4:43 minutes 500,000 views

This video explains how to use the p-value to draw conclusions from statistical output. It includes the story of Helen, making sure that the choconutties she sells have sufficient peanuts. It introduces the helpful phrase “p is low, null must go”.

## Inference and evidence 3:34 minutes

This is a newer video, based on a little example I used in lectures to help students see the link between evidence and inference. Of course it involves chocolate.

## Hypothesis tests 7:38 minutes 350,000 views

This entertaining video works step-by-step through a hypothesis test. Helen wishes to know whether giving away free stickers will increase her chocolate sales. This video develops the ideas from “Understanding the p-value”, giving more of the process of hypothesis testing. It is also complemented by the following video, that shows how to perform the analysis using Excel.

## Two-means t-test in Excel 3:54 minutes 50,000 views

A step-by-step lesson on how to perform an independent samples t-test for difference of two means using the Data Analysis ToolPak in Excel. This is a companion video to Hypothesis tests, p-value, two means t-test.

## Choosing which statistical test to use 9:33 minutes 500,000 views

I am particularly proud of this video, and the way it links the different tests together. It took a lot of work to come up with this. First it outlines a process for thinking about the data, the sample and the thing you are trying to find out. Then it works through seven tests with scenarios based around Helen and the Choconutties. This video is particularly popular near the end of the semester, for tying together the different tests and applications.

# Confidence Intervals

## Understanding Confidence Intervals 4:02 minutes 500,000 views

This short video gives an explanation of the concept of confidence intervals, with helpful diagrams and examples. The emphasis is on what a confidence interval is and how it is used, rather than how they are calculated or derived.

## Calculating the confidence interval for a mean using a formula 5:29 minutes 200,000 views

This video carries on from “Understanding Confidence Intervals” and introduces a formula for calculating a confidence interval for a mean. It uses graphics and animation to help understanding.

There are also videos pertinent to the New Zealand curriculum using bootstrapping and informal methods to find confidence intervals.

# Probability

## Introduction to Probability 2:54 minutes

This video explains what probability is and why we use it. It does NOT use dice, coins or balls in urns. It is the first in a series of six videos introducing basic probability with a conceptual approach. The other five videos can be accessed through subscription.

## Understanding Random Variables 5:08 minutes 90,000 views

The idea of a random variable can be surprisingly difficult. In this video we help you learn what a random variable is, and the difference between discrete and continuous random variables. It uses the example of Luke and his ice cream stand.

## Understanding the Normal Distribution 7:44 minutes

In this video we explain the characteristics of the normal distribution, and why it is so useful as a model for real-life entities.

There are also two other videos about random variables, discrete and continuous.

## Risk and Screening 7:54 minutes

This video explains about risk and screening, and shows how to calculate and express rates of false positives and false negatives. An imaginary disease, “Earpox” is used for the examples.

# Other videos

## Designing a Questionnaire 5:23 minutes 40,000 views

This was written specifically to support learning in Level 1 NCEA in the NZ school system but is relevant for anyone needing to design a questionnaire. There is a companion video on good and bad questions.

# Line-fitting and regression

## Scatterplots in Excel 5:17 minutes

The first step in doing a regression in Excel is to fit the line using a Scatter plot. This video shows how to do this, illustrated by the story of Helen and the effect of temperature on her sales of choconutties

## Regression in Excel 6:27 minutes

This video explains Regression and how to perform regression in Excel and interpret the output. The story of Helen and her choconutties continues. This follows on from Scatterplots in Excel and Understanding the p-value.

There are three videos introducing bivariate relationships in a more conceptual way.

There are also videos covering experimental design and randomisation, time series analysis and networks. In the pipeline is a video “understanding the Central Limit Theorem.”

# Supporting our endeavours

As explained in a previous post, Lessons for a budding Social Enterprise, Statistics Learning Centre is a social enterprise, with our aim to build a world of mathematicians and enable people to make intelligent use of statistics. Though we get some income from YouTube videos, it does not support the development of more videos. If you would like to help us to create further videos contact us to discuss subscriptions, sponsorship, donations and advertising possibilities. info@statsLC.com or n.petty@statsLC.com.

# Teach the students you have

Our job as teachers at any level is to teach the students we have. I embrace this idea from Dr Kevin Maxwell:

“Our job is to teach the students we have.
Not the ones we would like to have.
Not the ones we used to have.
Those we have right now.
All of them.”

I believe Maxwell’s focus was on the diverse learning needs we have in our classes. I would like to take another angle on this. If students do not have the needed skills to learn what we are teaching, then we need to teach those skills.

In many subjects, content and the skills are largely uncoupled. For example in history, a skill might be to integrate material from two conflicting sources. You can learn this in multiple contexts, and you do not need to know the history of the world up until 1939 in order to study World War II.

In mathematics, there are clear progressions. It is very difficult to learn about trigonometry if you do not have a good working knowledge of the Pythagorean theory. And learning Pythagoras is built on applying formulas, which is built on basic algebra. I admit, that as I write this I can see other approaches, but the point is that later learning in maths is built on earlier knowledge, understanding and skills. Learning in maths is also built on earlier feelings – a post for another day.

## The Gaps

There are two gaps we need to mind. The gap between levels of schooling, and the gap between what the preparation the students need, and what they have. I taught at the University of Canterbury for twenty years, and often heard colleagues complain about the level of preparation in our students. I am ashamed to say that it took me several years to realise that if our students do not have the foundation they need to learn what we are teaching, then we need to do something about it. As a result I created a course that started with making sure students knew how to use < and >, and which is bigger out of 0.04 and  0.2. These are necessary in order to make decisions about rejecting a null hypothesis.

Recently at a workshop I asked a group of about forty teachers how many of them have students starting high school who do not have the necessary knowledge of number skills – basic facts and multiplication tables. Every hand went up. There is a gap. I asked them what they are doing about it. Some suggested working in “Communities of Learning” to help primary schools to prepare the students better. This is fine, but what are they doing now! There was some discussion that if we are teaching lower curriculum levels at high school, they may never cover the materials at higher levels.

For that I have two responses. The first relates to the Maxwell quote I started with. “Our job is to teach the students we have.” Our job is to teach the students we have, the things they need to learn. If our students start high school without a good enough grasp of basic facts, then we need to help them to develop them. And we need to work out good ways to do this. I suspect part of the problem is that secondary maths teachers do not have training or knowledge in teacher beginning maths. Do we believe it is not part of our job?

The second response is that there is no point in moving on to later maths if the students’ foundation is weak. Now I say this with some trepidation as I can picture students being held back until they become fluent in their tables. This is not what I mean. One of the participants in the workshop asked me how I would go about setting up a programme to help such students. Obviously this is not a question I could answer on the spot, but here are some ideas and principles.

## Ideas and principles for building foundation skills

Summary:

• Do not under any circumstances give these students tests with time pressure.
• Expect the students to be able to learn what is needed more quickly than they would have done when younger.
• Engage students in deciding what they need to learn and how.
• Integrate the skills into other activities
• Make it fun

Explained:

## No time pressure

Read Fluency without fear by Jo Boaler. Read this about Maths trauma. Do not add to the students’ feelings of inadequacy. One possibility if you wish to give a diagnostic test, and want to have some idea of how long they take, tell them they have as long as they need, but after a certain amount of time get them to change to a different coloured pen.

## Learn quickly

My experience with teaching adults and teens is that once they realise they can learn, they learn quickly. Believe it. I don’t mean that they can answer questions quickly, but that they will be able to progress more quickly as they have better metacognitive skills, literacy, maturity.

## Student agency

This is their learning. Make sure the students know why they need the skills and how they will help them. Talk to students about how they would like to learn them, and let them choose their own reward system if appropriate. Different students will have different areas of weakness, and different ways to improve.

## Integrate the skills into other activities

I can’t imagine much worse than an entire maths lesson on basic facts. If we are working on multiplication, this fits well with area calculations. We also need to keep revisiting.

There is a place for well made and used flash cards to improve retrieval. There are multiple posts on using flashcards well. I would recommend them for some students for the last sticky facts, like 6 x 7, 6 x 8, 7 x 8 etc. Those were the ones that got me stumped. However, most knowledge is better gained in context. Create or find rich, open-ended tasks that help develop the skills the students need.

## Make it fun

Maths lends itself to games and fun. If you can’t think up a way – find it on line. But if you don’t think it’s fun the students aren’t going to. (Not sure the converse is true, but…)

# Mind the Gap

Our aim at Statistics Learning Centre is a world of mathematicians. My dream is for math trauma to be a thing of the past, and for all citizens to embrace mathematical thinking similarly to literacy. As maths and statistics educators we can work towards this. The most important student you have in your maths class is the one who becomes a primary school maths teacher. Make sure she loves maths!