Guide To Actionable Student Data Part 2 — How Could EdTech Companies Provide Actionable Insights For Teachers
With the growth of data and the advancement of AI technologies, “student data analysis” has become a buzzword in the EdTech industry. The past decade has witnessed a growing number of EdTech companies that focus on using data analytics to make teachers’ lives easier. As the competition gets fiercer, how to make your company stand out from the crowd by providing teachers with the most relevant and helpful insights?
In Part 1 of “Actionable Student Data”, we looked at the benefits of actionable data and concluded that actionable data stand as a good solution for teachers because they free up more time for addressing educational needs, provide more transparent student academic information, and offer solid evidence to support actions taken.
Since actionable student data have a multitude of benefits for teachers, they definitely constitute a tool that EdTech companies would like to acquire. But what kind of data can be considered actionable? What’s the difference between actionable data and actionable insights, which are sometimes used interchangeably?
Well, actually the term “actionable data” might be problematic. According to Brent Dykes, there is a hierarchical relationship between data, information, and insights. While the dataset forms the base of the pyramid with a large amount of raw and unprocessed facts, one needs to look at the top of the pyramid for insights, which are generated by analyzing information and drawing conclusions. However, not every insight is created equal: some can be used to drive changes or make decisions, while others can only answer questions and are not actionable. Therefore, actionable insights sit at the apex of the pyramid.
With this logic, every single piece of data could potentially be “actionable”, if it is analyzed thoroughly for generating insights and actions. Therefore, for any business to achieve data-driven success, it should seek to maximize effective actionable insights.
Now that we have clarified the terminologies, we are ready to answer a more practical question:
What do actionable insights look like in the educational field?
Here, I have summarized three characteristics of actionable insights that distinguish them from non-actionable ones: proper context, right timespan, and meaningful application. With these characteristics, you could easily examine and improve the data insights from your company.
Proper context
Actionable insights compare single data points with a broader context, such as similar groups of students or period of time, etc. Such context gives an insight a benchmark, to understand why it is important. Without proper context, an insight can end up causing more confusion than calling for actions.
Meaningful application
Actionable insights should be able to draw a connection between relevant data and apply meaningful applications between multiple data points to depict a clearer picture.
Appropriate timespan
Actionable insights analyze data within a time frame that is reasonably set for answering a particular educational question. If the time span is too long or too short for that question, no significant actionable insights are driven from data. For example, to look into student progress of a class, you wouldn’t want to investigate their scores only once every academic year, which would be too infrequent.
Another good way of identifying and providing actionable insights is by looking at the questions that they could answer. Typical questions that actionable student insights can help answer include:
- How likely would a particular student fail in the final exam or even drop out, based on his/her past performance?
- Where are the gaps in the course materials that cause confusion for students?
- Does a student need remedial support for particular knowledge and skills in a subject area?
- What are the classes or groups of students that are frequently lagging behind in the course pacing?
- What is the progress of a certain group of students within a regular period?
There are two principles specific to the educational industry that you should keep in mind.
First, data should always be analyzed with the educational KPIs. Define your key educational goals before looking into data for insights. Key educational goals help you understand the priority and urgency of different metrics so that you could sift out unnecessary information and react to the key metrics when a significant increase or decrease is noticed. Typical educational goals include student engagement, performance scores, student progress, etc.
Second, too many insights confuse rather than inspire people. With so much competing data and information to digest, the fewer insights you provide to teachers, the more likely they can be acted on. Suggest to teachers only those that are urgent, novel and highly relevant to the educational KPIs. Always keep in mind that less is more, or as Karen Martin said in her book The Outstanding Organization: Generate Business Results by Eliminating Chaos and Building the Foundation for Everyday Excellence, “if everything is a priority, nothing is a priority”.
In conclusion, with the introduction of EdTech products, teachers and school leaders don’t need to manually collect data, and could focus their time and energy on connecting with individual students. However, without the right insights presented or the right questions answered, EdTech companies cannot guarantee a supply of quality, actionable student insights for schools.
Now since we have defined actionable insights, let us get down to the “how” part of this question:
How can EdTech companies provide actionable student insights for teachers?
After analyzing 100+ EdTech products in the market, I have come up with the following 4 common attributes that feature an actionable educational data solution:
1) All in one place
We understand that student data can come from various types of sources and in different forms: attendance, diagnostic tests, quizzes, final exams, etc. However, teachers don’t have enough capacity to gather scattered information and put them in one place for analysis. Therefore, it is the responsibility of EdTech companies to provide a one-stop-shop for all possible data analysis.
Though it is possible to split them into several different interfaces, the key to data analytics design remains for teachers to access the most relevant information on their class(es) with a minimum number of clicks.
2) The simpler, the better
One rule that everyone should keep in mind is that not every teacher has a degree in statistics and can understand complex charts. When designing visualizations, always go with the simplest, most straightforward options. Complex chart types will more likely confuse your audiences instead of helping them save time or make better decisions.
Some commonly used visualizations include:
- Simple texts
- Bar charts (horizontal or vertical)
- Line graphs
- Scatter plots
On the contrary, think twice about
- Box and whiskers plot
- 3D charts
- Line graphs with more than 3 lines
3) Different granularities for different audiences
When you design a data analytics page, take into account more than one group of audiences: teachers, parents, subject leaders, principals, teacher trainers, etc., and keep various design options for different needs. For example, to quickly identify the classes that are lagging behind and need attention, principals would like to see the pacing of multiple classes and their overall performance, but wouldn’t need to know the performance of individual students. While for teachers, in addition to comparing several classes that they teach, they would also prefer the transparency of individual student’s progress, scores, interactivity with and time spent on the platform, etc, so that the teacher could single out the students who need urgent attention or guidance.
Hence, conduct thorough user research to understand user’s needs and the questions they are keen on. Design with different audiences in mind, and draft out different product journeys for different users if possible. Finally, show your designs to your audiences and investigate whether those designs are addressing the real needs of them.
4) Make it easy to export
In addition to using data analytics during class, teachers also need the visualization to communicate with school leaders, to make comparisons across multiple classes or periods, or simply to keep a record for themselves. For these purposes, teachers do not need the analytics that sit on their screen, but the ones that can be exported, printed and stored.
Make sure that all data analysis can be either exported or downloaded in both raw data and visualization (chart, graph, etc.) format. Plus, if some information shows up once the mouse hovers over the visualization, it should also be included when exported, so no information would be missing in the transfer.
All things considered…
There are two fundamental elements for EdTech companies to provide their clients with straightforward data insights. One element is a thorough understanding of their audiences. What is their regular day like? What questions are they trying to solve? What actions would they take? Another element is a strong skill set in data analytics and visualization. What is the most efficient way of delivering certain information? How to guide your audience quickly to the answers they’re looking for? How to cater to the needs of different groups of audiences?
At Alef, we are experimenting with two approaches to student data analysis:
First, we make sure that all data analysis and insights are visible on a single analytics page on our platform instead of being scattered on different pages. For any teacher or principal in any grade and class, they could find the right level of information within 3 clicks, by selecting the subject, grade, and class that they are interested in.
Second, Alef platform is designed with consideration of multiple levels of audiences, including school principals, teachers, students, and school admins. Each type of audience has its own login access to the product, and the platform features and data analytics are catered to their needs.
With the advancement of data analytics, different EdTech companies can have various approaches to providing actionable insights to their educational clients. How does your company approach learning analytics? Do you know any good examples in EdTech? What has been proved and disproved? What are some success stories that you would like to share? Share your experience with the community by commenting below.
