MINERVA VOICES

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

by Head of College of Social Sciences Katie McAllister

December 21, 2021

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, Forum(TM) 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.

Quick Facts

Name
Country
Class
Major

Social Sciences & Business

Business & Computational Sciences

Business and Social Sciences

Social Sciences and Business

Computational Sciences & Social Sciences

Computer Science & Arts and Humanities

Business and Computational Sciences

Business and Social Sciences

Natural Sciences

Arts and Humanities

Business, Social Sciences

Business & Arts and Humanities

Computational Sciences

Natural Sciences, Computer Science

Computational Sciences

Arts & Humanities

Computational Sciences, Social Sciences

Computational Sciences

Computational Sciences

Natural Sciences, Social Sciences

Social Sciences, Natural Sciences

Data Science, Statistics

Computational Sciences

Business

Computational Sciences, Data Science

Social Sciences

Natural Sciences

Business, Natural Sciences

Business, Social Sciences

Computational Sciences

Arts & Humanities, Social Sciences

Social Sciences

Computational Sciences, Natural Sciences

Natural Sciences

Computational Sciences, Social Sciences

Business, Social Sciences

Computational Sciences

Natural Sciences, Social Sciences

Social Sciences

Arts & Humanities, Social Sciences

Arts & Humanities, Social Science

Social Sciences, Business

Arts & Humanities

Computational Sciences, Social Science

Natural Sciences, Computer Science

Computational Science, Statistic Natural Sciences

Business & Social Sciences

Computational Science, Social Sciences

Social Sciences and Business

Business

Arts and Humanities

Computational Sciences

Social Sciences

Social Sciences and Computational Sciences

Social Sciences & Computational Sciences

Social Sciences & Arts and Humanities

Computational Science

Minor

Computational Science & Business

Economics

Social Sciences

Concentration

Computer Science and Artificial Intelligence & Cognition, Brain, and Behavior

Designing Societies & New Ventures

Strategic Finance & Data Science and Statistics

Brand Management and Designing Societies

Data Science & Economics

Machine Learning

Cells, Organisms, Data Science, Statistics

Arts & Literature and Historical Forces

Artificial Intelligence & Computer Science

Cells and Organisms, Mind and Emotion

Economics, Physics

Managing Operational Complexity and Strategic Finance

Global Development Studies and Brain, Cognition, and Behavior

Scalable Growth, Designing Societies

Business

Drug Discovery Research, Designing and Implementing Policies

Historical Forces, Cognition, Brain, and Behavior

Artificial Intelligence, Psychology

Designing Solutions, Data Science and Statistics

Data Science and Statistic, Theoretical Foundations of Natural Science

Strategic Finance, Politics, Government, and Society

Data Analysis, Cognition

Brand Management

Data Science and Statistics & Economics

Cognitive Science & Economics

Data Science and Statistics and Contemporary Knowledge Discovery

Internship
Higia Technologies
Project Development and Marketing Analyst Intern at VIVITA, a Mistletoe company
Business Development Intern, DoSomething.org
Business Analyst, Clean Energy Associates (CEA)

Conversation

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, Forum(TM) 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.