Teachers use all sorts of methods to distinguish between their students and the ways in which they learn. The majority do this without documenting their findings, and in most cases very few teaching staff actually collaborate to share these revealing attributes about students with their colleagues.

Last term my colleagues and I agreed that a Personal Learning Card, available about each student in the school, should be a priority. This finely curated digital card would give teachers an ‘at a glance’ overview of a learner. When a teacher begins to truly understand a child’s learning preference, then the goal of Differentiation (a teacher’s proactive response to a learners’ needs) has a chance of being realised.

In Education Today Term 1 issue, I discussed my findings of Carol Ann Tomlinson’s work on Differentiation, and I presented some methods for surveying your students to capture data. In this article I will cover how you can make this data meaningful, and more importantly how it can be accessed and processed by your teaching colleagues.

Personal Learning Cards
A Personal Learning Card is a singular location that houses details concerning a student. I chose to start with the ideal outcome of the project: what information needs to be included, and how should it be displayed?

Diagram 1 illustrates how I envisioned the information that the learning card would contain. I have started with Tomlinson’s three suggested categories that vary a student, they are: Readiness, Interest and Learning Profile. The field of Readiness can be achieved through a combination of official testing such as NAPLAN results, but also individual teacher surveying and notes throughout the year. In my school’s case we have been using ACER’s PAT testing, it is this type of data that will provide teachers with a starting point to understanding a student’s Readiness.

The two remaining fields of Interest and Learning Profile are something which can be ascertained by way of a survey or quiz to be taken periodically, for example at the beginning of each year.

As a result of Tomlinson’s work I developed a survey using Google Forms, which was split into three different sections, developed to ascertain the following:

1 Interests: including favourite subject and least favourite subject at school. Aspirations: what the student hopes to become; who the student admires. Media influences: favourite TV shows and websites.
2 Self-perception: how a student would describe himself or herself. Results from a multiple intelligence test (questions were derived from a reputable online source) would also be included, providing a teacher some insight into a student’s preferred learning style.
Kinaesthetic – Body Smart
Linguistic – Word Smart
Logical – Number Smart    
Interpersonal – People Smart
Intrapersonal – Myself Smart    
Musical – Music Smart
Visual/Spatial – Picture Smart    
Naturalistic – Nature Smart
After some deliberation it was decided that Naturalistic intelligence would be removed. This would not be applicable for the staff at our school.
3 I also chose: to include details about a student’s preferred learning environment, and how they function in different social situations.
The results of the Google Form come through in a spreadsheet, one student per row. By creating new sheets nested in the spreadsheet, I could begin to sort the data, one sheet for each student.

Making the data accessible
In order for any of the information to be useful it needs to be presented in a visual manner, so that a teacher can quickly reference and process it. The intelligence test data is displayed as a pie chart, so it is easy to establish the domineering intelligences.

The text based answers concerning interests and aspirations are probably the most revealing about a student. These are displayed simply in clear red text.

The most challenging formula to write into the spreadsheet was the one which reveals the learning and social environments of a student. Depending on the answers of 28 questions a student can have one of four outcomes for each category.

For example the outcome “I like to learn…” will result in an output of either:
“In a group where I’m lead”
“In a group where I’m the leader”
“Independently”
“Independently with some direction from a teacher or friend.”

In practice
The advantages of a Google Form is that I do not require each student to leave their name, we already know who they are because they are logged into their instance of Chrome on their device. Each student can spend as much time as they need on the survey and once they click ‘submit’ the answers are all collated for me. This may be dozens or even hundreds of results. The data is then processed in a largely automated fashion. The result is a webpage for each student that hosts their very own Personal Learning Card.

Google Forms and Sheets is an incredibly powerful tool, but the real benefit is in the option: ‘Publish to the web…’, here lies the redefining aspect of the GAFE suite, enabling us to create a webpage of finely tailored information (including graphs, charts and tables) for every single student.  The last part of the jigsaw is where to house the unique URL provided for every student.

In our case we have chosen to plant this unique hyperlink right inside our Student Information Panel inside our Learning Management System (SEQTA). That way, when a teacher takes the attendance, or reviews a student’s pastoral care records, they can also click straight through to the Personal Learning Card.

Make no mistake; the information provided is nothing more than a tiny slice of information that a teacher may take into consideration. It is by no means a method by which we pigeon hole students. Probably the most rewarding part of this process is that all staff will begin to reference some of the same terminologies (Readiness, Learning Profile etc), and all are starting to think about differentiated instruction.

I am currently working closely with my school’s leadership team to implement this digital solution, and I expect that we might be looking at another term before this idea is released in practice. My concerns lie in the way that the data is collected with regards to the questions. Because if we get this wrong, we could be providing false or largely inaccurate data to the whole teaching cohort. I expect we have some fine-tuning of the questions contained in the survey to do.

However, as with any innovative idea, this solution is a process that involves many visionaries at my school. I see my role as that of a digital enabler, harnessing the motivation behind a collection of great ideas and I expect that this project might indeed go through a few more big changes in the coming months. But one thing that can be agreed on is this: the focus remains squarely on the learner and we have a duty to excel the best students, and to stretch the weakest.