Ben Motz has held several titles within the last few years: Senior lecturer and research scientist in Psychological and Brain Sciences, faculty fellow for UITS Learning Analytics, and most recently director of the eLearning Research and Practice Lab. How did his CogSci perspective lead him to focus on advancing student learning? The through-line is his enduring interest in the relationships between cognitive theories of human learning, psychological theories of student engagement, and the realities of what goes on in college classes.
For Motz, developing tools that help motivate and sustain students' engagement in learning activities is not just an add-on but a core element of fulfilling our research and educational missions. While his research focuses on examining what motivates students to learn and how to adjust teaching practices to meet their needs, his practical experience as an instructor has led him to translate those insights into new evidence-based practices, interventions, and tools.
Motz and colleagues have created a series (a progression, really) of resources aimed at advancing student learning—and, as you might expect, all of them ultimately came out of his classes and attempts to address particular concerns within psychology and, more broadly, within higher education. In his words, "It's all because of science." Here's what he means:
When Motz first started teaching online, he struggled to find good models, so he looked to the science of what a good approach might be. In particular, he was concerned about how to do things in accordance with how CogSci research indicated online courses ought to happen. That was the germ of Quick Check, a tool for creating inline assessments in Canvas: A lot of practice. Incentives. Frequent low-stakes assessments.
Quick Check solved one problem but led to another.
Students gained a lot of ways to engage with their studies, which is great from a learning perspective, but they were feeling overwhelmed and missing assignments. They needed help tracking everything. An automated tool became necessary because of the volume of activities we were asking students to manage.
Motz explored potential solutions, going so far as to use bi-weekly mail merges to help students periodically monitor their own progress. That proved cumbersome, so he looked into customer relationship management software (Salesforce) to send assignment reminders. When integration with Canvas emerged as a necessity, he appealed to colleagues within learning technologies. Together they built a mobile app that integrates with IU's Canvas data to help students track upcoming assignments. Boost, as it's now known, lets students opt-in to receive push notifications on their devices as deadlines approach.
It comes as no surprise, then, that the mission of Motz's eLearning Research and Practice Lab (part of IU's Pervasive Technology Institute, with support from UITS Learning Technologies) is to foster a collaborative, interdisciplinary research community to expand what's available. He's currently in the thick of writing NSF grants that will enable more rigorous research methods and bigger, more collaborative science.
One step in this direction is ManyClasses, which compares practices across dozens of classes to see the variability of effects on student learning. Working with departmental colleagues in Psychological and Brain Sciences and across the Unizin consortium, Motz is already pursuing an open question for the science of learning: How generalizable are our studies and findings about teaching and learning? The common theme is rigorous controlled experiments with college students at larger scales that require collaboration across disciplines and institutions.
