Teaching Problem Solving using Data - The Classroom MINI GUIDE

Recently, I have been putting together a collection of class activities and worksheets to help me teach problem-solving skills in details to my students.

Aside from the happiness that the 3-day problem-solving sprint conducted for the SS3 students went awesomely well, I wasn’t satisfied that I get to critically teach my students how to solve real-life social and business problems in such little days. Schools are adamant to bend the curriculum these days.

So, I decided to make it a classroom fun. This is because identifying and understanding the problem you want to solve is the first and best step to solving it. 

According to Maria Kampen, creating a data-driven classroom is more of the teachers' task. It’s essential for us as it will build learners that will never dwell on problems but actively look for solutions through communication, collaboration, critical thinking and creativity. 

In this article, I share my 4 favourite steps for creating a data-driven classroom.

Determine data standard

Every research has proven that data laced decision making is far useful and accurate than mere intuition-based decision making. However, there has to be a standard for the data, otherwise making sense out the data would be a torso. 

Data usage in the classroom needs as much care as any other data. Student’s sensitive data like academic history or performance and behavioural data must not be used for class consumption. I employed formative and observational data like sport preferences and the number of family members.

Also, I’d ask the student to signify if they’re through with note-taking, then collect the data and analyse to indicate the number of students that finished on time. This helps build speed and accuracy.

Have fun with the analysis

This exercise keeps raises my students' curiosity and it helps us do maths unknowingly. I printed on an A3 cardboard paper, the graph type used in the problem-solving sprint and glue it up in my classroom. I’d sometimes look at the image and nod in affirmation — this is to tag the charts as important in my students' minds.

Each time we get a dataset, we brainstorm on the chart type to communicate the data in visualisation and ask ourselves, the additional data we could collect to make more sense and probably an advanced visualisation. We’ve gone beyond column, line, bar and pie charts. Now we’re making treemaps, stacked bar charts, bubble and waterfall charts.

Canva, Venngage and Google Flourish are my favourite tools for data visualisation. The drag and drop features make it easy for my students to play around. Creating data visualisation is simply a divide and conquer task — break the data into pieces and visualise. Colour combination must be guided using the colour wheel.

Reflect on the findings

Sometimes I create a line chart to explore the trends of my students' academic performance. This guides me to study their wellbeing, competencies and also work on their mindsets. Individually, I would invite students that lag in a particular subject to discuss how we can improve on his performance.

When we reflect on the findings, it makes us take data seriously. With Taoheed, we analysed his performance data to figure out the minimum score needed in Data Processing practicals if he wants to represent the school in the National Computer Science Competition. Knowing his target score helped him out-score the threshold. 

This system alone has helped me create a detailed feedback loop with my students. Now, we have a shared knowledge base about issues that affect individual students and a collaborative discussion about actionable solutions.

Look beyond the classroom

One of my students, Abdul Muu’min brought the idea that we look beyond the classroom to solve problems in the community. Immediately, I keyed into the great vision. When students realise they understand their problems and can find solutions to it, they want to do more.

We started with a community-based project in Idi Apa — a school in the rural area of Kwara State. The school has long been without the needed infrastructures. We conducted our research and found that it was a road problem. Vehicles bringing materials couldn’t use the road so they diverted the resources elsewhere. We donated a few chairs to the under-tree classes and looked for means to get help from the government. We employed Google forms in the data collection and sheets for analysis in that phase.

Since the beginning of this term, we’ve worked on two community projects and we hope to do more. To me, creating a data-driven classroom is more to teaching the 21C skills, it develops the students to become responsible citizens — which is the ultimate of being educated.