Month: October 2025

When Grade 5 teacher Jared Bremner joined First Baptist Christian School in the Cayman Islands, Mathletics was already woven into their math curriculum.

Six years later, he can’t imagine teaching without it.

First Baptist’s approach was different from the start: they were intentional about implementation. What started as simply adding another digital resource has become a complete shift in how students experience math from Kindergarten through Grade 8.

Mathletics, our ESSA-certified online math program for ages 5–16, delivers personalized learning through explicit and systematic instruction, engaging activities, gamified challenges and immediate feedback. But at First Baptist, it’s become much more than a supplement: it’s central to how they teach.

Bremner now oversees implementation across the entire school, ensuring every new teacher successfully integrates the program into their lessons.

His personal journey, from newcomer to passionate champion, reflects the school’s own evolution in digital math education.

From ‘just another resource’ to essential instruction

Six years ago, First Baptist had a clear goal: find a math platform with diverse resources that teachers could actually use to improve instruction through technology.

“While it started off as just a resource they wanted to use,” Bremner reflects, “is now an important part of our instruction.” What made the difference? “We find Mathletics to be very encompassing. It’s a very layered product,” Bremner explains, “from working through problem-solving questions to assessment to fun games.”

This comprehensive nature meant teachers weren’t juggling multiple tools: they had everything in one platform. The school committed to systematic implementation and real teacher training, instead of just handing teachers another digital tool and hoping for the best. Their approach? Start with the youngest learners and build up.

Students learn the platform progressively from Kindergarten through Grade 8, teachers get ongoing support (not just a one-time training session) and most importantly, Mathletics becomes woven into daily instruction rather than treated as an add-on.

“We’ve been intentional about using Mathletics,” Bremner explains, “and so from Kindergarten up to Grade 8, we’re really training our children how to understand the platform, how to use it and how they can benefit from the resources.

When learning becomes play (without students realizing it)

Ask Bremner about gamification and he lights up. He’s watched something remarkable happen in his classroom over the years: students so absorbed in math challenges they forget they’re actually learning.

“Making things in a gamified way allows them to enjoy their learning and learning almost becomes secondary,” he shares. “And so, through that gamification, they learn that they can progress, and they can make mistakes, but they can still improve.”

For today’s digital-native students, this isn’t just nice to have – it’s speaking their language.

And nowhere is this more evident than with Live Mathletics, the program’s real-time math competitions where students can compete with peers from around the world.

 

These live challenges test students’ math fluency skills and reflexes, allowing whole schools, classes and individual learners to go head-to-head.

For Bremner’s class at this international school, it means they’re not just competing with classmates: they’re facing off against students from India, Pakistan, South Africa and Canada.

“[They] find ways of how they can get their answers in quicker and how they can compete with children around the world,” Bremner observes. “And so, it grows a global mindset not just within the classroom but on the platform that we’re using.”

The competitive element has genuinely engaged students. They enjoy customizing their avatars, working to beat their personal bests in timed challenges and tracking their progress against peers worldwide.

The immediate feedback and global competition keep them motivated to practice more, proving that when learning feels like play, students naturally want to keep improving.

The teacher’s game-changer: Data that actually helps

The engagement is just the beginning. For Bremner, the real value comes from how Mathletics helps him meet every student where they are.

Using the platform’s reporting features, Bremner tracks how students perform on activities and quests, gauging their responses against the school’s grading scale. But he doesn’t stop at assessment.

“What we do is build from that,” he explains, “build their understanding and make sure they’re [not just] working on topics being taught in class at that moment… so it’s not just about current learning, it’s about identifying students that need to be challenged and also children that need to be supported.”

This means advanced learners get extension activities that push them beyond the current curriculum, while struggling students receive targeted support exactly where they need it.

The school reinforces this differentiated approach with Mathletics’ printable booklets in their Response to Intervention (RTI) program.

The result? Every student gets what they need (challenge or support) based on real data, not guesswork.

Leadership that values what works

At First Baptist, administrative support comes with accountability. The principal’s approach is clear: she’s committed to providing Mathletics but expects to see it actively used in classrooms.

“She wants to make sure the teachers are intentional in its use,” Bremner explains, “and that it’s not just another resource that we just add to the list.”

This results-focused leadership means the school maintains accountability through:

• Usage monitoring via administrative accounts
• Activity tracking to measure engagement levels
• Regular review of minutes used and activities assigned
• Data-driven decisions about program effectiveness.

“I have an admin account and I look at how many activities are being assigned and how many minutes are being used,” Bremner shares. “We make sure that it’s actually being used for its true value.”

From one teacher’s journey to school-wide success

When Bremner first arrived at First Baptist, integrating a new platform while adapting to a new country and school felt challenging. Yet his perspective shifted dramatically through experience:

“And over time of using it, I grew to love it!” he reflects.

His transformation, from newcomer to the teacher who now guides colleagues through Mathletics implementation, mirrors First Baptist’s own six-year evolution.

Through Bremner’s experience, we see the key elements that drive success:

• Comprehensive training that unlocks features teachers didn’t know existed
• Engaged students participating because they want to, not because they must
• Meaningful data that informs teaching decisions and student support
• Committed leadership that backs investment with accountability and clear usage expectations
• Systematic progression building platform fluency from kindergarten through eighth grade
• Sustained support offering professional development beyond one-time training sessions.

Today, Bremner monitors usage data and provides the support he once needed himself, embodying the long-term approach First Baptist has built.

“When it comes to Mathletics, because we’ve embedded it within our culture of numeracy in the school, it’s allowed our teachers to feel confident in how they use it and our students to feel comfortable and confident with the platform.”

Bremner’s six-year journey shows exactly how sustainable maths success can happen – one teacher, one classroom, one school at a time!

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Kristina Gobetti*

Have you ever struggled with teaching statistics? Do you and your students share a sense of apprehension when data lessons appear on the scheme of work? You’re not alone. Anecdotally, many teachers tell me that statistics is one of the topics they like teaching the least, and I am no exception to this myself. In my mathematics degree I took the minimum number of statistics-related courses allowed following a very poor diet of data at school, and carried this negative association into my teaching. Looking back on my career in the classroom, I did not do a good job of teaching statistics, but having had the luxury of spending many years at Cambridge Mathematics immersed in research from excellent statistics teachers and education academics I now understand why!

So now of course, the question has been posed. Why is statistics hard to teach well? In part, I believe that it stems from viewing statistics through a mathematical lens – understandably, given that we are delivering it alongside quadratic equations, Pythagoras’ theorem, fractions, decimals and percentages. But while statistical analysis would not exist without the mathematical concepts and techniques underpinning it, we have a tendency within curricula to make the mathematical techniques the whole point, and reduce the statistical analysis part to an afterthought or an added extra. Students find the more subjective analysis hard, so it is tempting to make sure everyone can manage the techniques and then focus on the interpretation as something only the most able have time to spend on (although, there is always the additional temptation to move on to other, more properly ‘maths-y’ topics as soon as possible).

This approach is at odds with how education researchers suggest students should encounter statistical ideas. In the early 1990s, George Cobbⁱ and other researchers recommended that statistics should

• emphasise statistical thinking,
• include more real data,
• encourage the exploration of genuine statistical problems, and
• reduce emphasis on calculations and techniques.

Since then, much subsequent research has refined these recommendations to account for new technology tools and new ideas, but the core principles have remained the same. In much of my reading of education research, three ways of seeing or interacting with data keep appearing:

• Data modelling – the idea that data can be used to create models of the world in order to pose and answer questions
• Informal inference – the idea that data can be used to make predictions about something outside of the data itself with some attempt made to describe how likely the prediction is to be true
• Exploratory data analysis – the idea that data can be explored, manipulated and represented to identify and make visible patterns and associations that can be interpreted

In the abstract, these ways of seeing, while distinct, have a degree of overlap and all students may benefit from multiple experiences of all three approaches to data work from their very earliest encounters with data through to advanced level study.

Imagine the following classroom activity that could be given to very young students (e.g., in primary school). A class of students is given a list of snacks and treats and the students are asked to rank them on a scale of one to five based on how much they like each item. How could this data be worked with through each of the three approaches?

Firstly, we will consider data modelling. Students could be asked to plan a class party with a limited budget. They can buy some but not all of the items listed, and must decide what they should buy so that the maximum number of students get to have things they like. In this activity, students must create a model from the data that identifies those things they should buy more of, and those things they should buy least of, along with how many of each thing they should get – perhaps considering these quantities proportionally. This activity uses the data as a model but inevitably requires some assumptions and the creation of some principles. Is the goal to ensure everyone gets the thing they like most? Or is it to minimise the inclusion of the things students like least? What if everyone gets their favourite thing except one student who gets nothing they like?

Secondly, we will think about this as an activity in informal inference. Imagine a new student is joining the class and the class wants to make a welcome pack of a few treats for this student, but they don’t know which treats the student likes. Can they use the data to decide which five items an unknown student is most likely to choose? What if they know some small details about the student; would that additional information allow them to decide based on ‘similar’ students in the class? While the second part of this activity must be handled with a degree of sensitivity, it is an excellent primer for how purchasing algorithms, which are common in online shops, work.

Finally, we turn to exploratory data analysis. In this approach students are encouraged to look for patterns in the data, perhaps by creating representations. This approach may come from asking questions – e.g., do students who like one type of chocolate snacks rate the other chocolate snacks highly too? Is a certain brand of snack popular with everyone in the class? What is the least popular snack? Alternatively, the analysis may generate questions from patterns that are spotted – e.g. why do students seem to rate a certain snack highly? What are the common characteristics of the three most popular snacks?

Each of these approaches could be engaged in as separate and isolated activities, but there is also the scope to combine them, and use the results of one approach to inform another. For example, exploratory data analysis may usefully contribute both to model building and inference making, and support students’ justifications for their decisions in those activities. Similarly, data modelling activities can be extended into inferential tasks very easily, simply by shifting the use of the model from the population of the data (e.g., the students in the class it was collected from) to some secondary population (e.g., another class in the school, or as in the example, a new student joining the class).

Looking back on my time in the classroom, I wish that my understanding of these approaches and their importance for developing statistical reasoning skills in my students had been better. While not made explicit as important in many curricula, there are ample opportunities to embed these approaches and make them a fundamental part of the statistics teacher’s pedagogy.

Do you currently use any of these approaches in your lessons? Can you see where you might use them in the future? And how might you adapt activities to allow your students opportunities to engage in data modelling, informal inference and exploratory data analysis?

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Darren Macey*