A Teacher’s Guide: How to Use Data to Measure Student Engagement


The evidence in favor of active student engagement is overwhelming: learning outcomes are higher in active versus passive learning environments. Many instructors use active learning techniques; however, the actual level of each student’s engagement throughout a class session isn’t readily apparent. The data collection required to verify student engagement has traditionally been a time-consuming task with little standardization: manual coding of videos, questionnaires or pre- post-surveys, quizzes, and interviews.

Online learning represents a tremendous departure from some of these limitations: platforms can automatically record classes, generate transcripts, and log instances of students speaking, editing documents, and contributing to text chat. These new opportunities to quantify engagement challenge us to consider what we are measuring and how we should use this information.

At Minerva University, for example, a classroom on its digital platform, ForumTM has multifaceted engagement, with verbal, written, and visual elements in the main classroom and breakouts. Active learning is further supported with interactive learning resources, including collaborative workbooks, whiteboards, and polls.

After completing a class session, Forum includes metrics for overall student-instructor talk time, reactions (emojis), hand-raises, chats, and individual student talk-time and talk-time history in breakouts and the main classroom. A single class session includes hundreds of measurements of student engagement. While some platforms include similar metrics, many others focus on providing transcripts, poll responses, message boards, and click-through tracking of engagement with course materials. Minerva instructors use metrics of in-class engagement as a powerful tool to identify students who may be struggling to participate and examine how we include all our students in active learning.

Dos and Don’ts when using classroom metrics

Metrics offer instructors a unique record of what is happening in their classroom, capturing details otherwise lost in the moment. However, metrics typically represent what is readily measurable; instructors must carefully consider the broader context of the learning environment when interpreting classroom data. These strengths and limitations suggest specific dos and don’ts.

1. Do use data to identify blind spots and challenge assumptions. The overall balance of instructor-student talk-time draws our attention to how we make space for students to talk and engage with each other in discussion. The individual talk-time and engagement history challenges us to consider how we spread opportunities to participate around the classroom and foster verbal and non-verbal contributions.

In my own classroom, the ability to see data that demonstrated how specific students could appear less engaged in the main classroom but have significant talk-time in breakouts challenged me to explore new avenues to help students find their voice in the larger group.

2. Do share metrics across different courses or teaching staff to reveal critical questions about pedagogy otherwise invisible to an individual instructor. What is happening in the classroom with more hand-raising or emoji reactions or frequent use of chat? What drives low/high amounts of instructor talk-time relative to students? A teaching team can use the data to orient towards a topic, and an individual can compare their practices to their peers. Comparing metrics across courses also reveals discipline-specific approaches to active learning.

In one instance, a teaching team identified one instructor with comparatively limited student talk-time. Instead of an overly-talkative instructor, follow-up found that the students happened to have a generally lower level of proficiency with the language of instruction. The instructor addressed this mismatch with the assumed proficiency in the lesson plan, and student engagement increased.

3. Don’t assume all classes should have the same metrics. Some specific data patterns may be “good” or “bad,” but many differences are just that: differences in instructor style and classroom dynamic. Metrics require significant context to interpret, and there aren’t one-size-fits-all ways of describing an ideal class session.

While one instructor’s class may include flurries of emoji reactions and proactive hand-raising, another’s engagement may focus on thinking, writing, and invitations to share or critique.

4. Don’t rely on one metric when making pedagogical decisions. Determining if/when to intervene requires a nuanced understanding that student engagement is more than just a specific number. It is also far easier to have a single measure capture the quantity of engagement rather than examine its quality.

A single metric represents just one facet of engagement: a student who experiences significant anxiety in speaking up could appear “disengaged” if measured by talk-time alone, yet frequently affirm other students’ ideas with emoji reactions and offer evaluation and extension in text chat. Here, nonverbal engagement offers significant insight into the student’s motivation, which is important context for any intervention.

It can be as important to understand what we are measuring as what we are not measuring: “There are many students, and I am one of them, who may appear to be disengaged, even catatonic, when they are in fact silently but vigorously grappling with a concept or problem” (Hopper, 2003 p. 25).

Meaningful engagement requires a deep belonging that relies on trust, student agency, and a positive classroom dynamic with opportunities for meaningful participation. Metrics offer a starting point for more objective and data-informed conversations about cultivating active and engaged students. Understanding when and how our students are engaged enables instructors to foster a greater sense of community, increase belonging, and build rich learning environments.

Hopper, K. B. (2003). In Defense of the Solitary Learner: A response to collaborative, constructivist education. Educational Technology, 43(2), 24-29.

McAllister, K. A. L. (2021). Beyond the Lecture: Interacting with students and shaping the classroom dynamic. Rowman & Littlefield.

Katie McAllister, Ph.D., is a professor and Head of the College of Social Sciences at Minerva University.

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