Mention big data in a grade level or team meeting of teachers, and you’ll often hear a cacophony of complaints about the test scores that the state collects to rank and judge schools. The chatter beyond the classroom, however, continues to be about how the rise of big data will be a disruptive force in the ways teaching and learning are taking place.
Little data, in contrast, keeps teachers agile and flexible. It allows them to read the room and move the students to the center of the learning, while maintaining the ability to orchestrate curriculum mastery, instructional fluidity, and assessment validity and reliability. Master teachers often struggle to articulate how artfully they use little data, but it can be the difference maker between classrooms with successful engagement and deeper learning and those that miss the mark.
What can those of us supporting classrooms—district leaders, university partners, education technology partners, voices in the education space—do to grow and scale the conversations around a new generation of data to support learning? It seems to begin with understanding the quantitative, qualitative, and gut-level data points that educators are using hour to hour and day by day to make decisions around planning, mid-lesson shifts, tone, and pace. Seeing the impact of little data in action can help create a bridge to conversations about the potential power that big data holds for learners.
Below are four potential bridges between little data and big data:
1. From Attendance to Engagement
Attendance is an essential element of many big data projects that use predictive analytics to measure the risk of students dropping out or graduating without post-high school opportunities; but it isn’t the focus of most classroom educators. Even though teachers take attendance daily and sometimes hourly, attendance as impactful data remains at a compliance level for most educators. In contrast, effective classroom teachers do have a significant amount of data in their working and long-term memories about classroom engagement and focus. This includes how students are responding to questions, how quickly they move between collaboration groups, and the amount of “learning noise” that each student is producing.
Ask any teacher to rank the focus and engagement of their students, and even if the validity of the data is skewed a bit, he or she would have an almost instant response. Teachers know that it is focus on and engagement with meaningful real-world tasks that push students outside of their comfort zones and into new frames of learning. Pairing big attendance data with little data about focus and engagement allows teachers to bring more voices into classroom conversations and build a robust learning community that reaches all students.
2. Interactions, Not Infractions
Another central element of big data surrounds discipline, including data for both in-school and out-of-school suspension. In addition, data surrounding chronic minor offenses in areas like disrespect and insubordination are now factoring into the predictive analytics that schools use. Though teachers care about those numbers, they are more focused on how individual students are reacting to and interacting with their peers and teachers during their time together.
This little data looks at things like:
- how or if a student responds to his or her peers’ incorrect answers
- how students work with others to complete a project
- how students ask questions and request permission in the classroom
Teachers know that these factors deeply impact learning as they create a rhythm, flow, and energy that maximize both teaching and learning in class. How can other conversations mix both big and little data of discipline to support students and create optimal learning environments?
3. The Glass Half Full
The third element of most big data projects is assessment. Though the use of assessment data to draw conclusions about student achievement creates conversational potholes, it remains an important element of big data. Until we grow the analytics foundation for education to be more individualized, robust, and comprehensive, educators will have to play in a half-full pool of data.
The larger problem in this space, though, is that most assessments of learning continue to feel deeply compliance based for both students and teachers. It is only the assessments for learning—i.e exit tickets and checks for understanding—that truly guide instructional practices in the classroom. This little data generates the essential micro-shifts in teaching before, during, and after a lesson. This is where learning that is adaptive, competency-based, and personalized will truly come to life; it is the teacher’s role to constantly fine tune these systems to maximize success.
4. A Culture of Joy
The final type of little data that needs to merge into the larger data conversation has to do with whether our work in the classroom is growing children that are happy and joyful. Certainly there are climate and culture surveys that gather big data around student satisfaction. But for a teacher, the little data of joy and happiness manifests when students:
- share smiles
- display energy for learning
- ask questions of inquiry
- talk about the lesson after class in the hallway
- enthusiastically showcase their learning through presentations
It is during these joyful moments that teachers collect the data about their effectiveness and impact, and also when they grow more determined to plan, invest time, and give of themselves. Currently, big data is oblivious to the emotional needs of teachers; it lacks the nuance necessary to speak to the daily work of educators.
The conversation shifts necessary for big data to impact daily learning have, in most places, not even started. All of us invested in edtech need to change that. It may be only with this inclusion of little data that the power of big data can come of age to impact change in the learning spaces across the country and beyond.