Using Theory-Informed Learning Analytics to Understand How Homework Behavior Predicts Achievement

Educators, families, and students continue to debate whether homework promotes academic achievement. A resolution to this debate has proven elusive, given the often-mixed findings of the relationship between homework behavior, typically measured with often-unreliable student self-reports and achievement. We argue better estimates of these relationships require (a) changes to what data are collected to measure homework behavior and (b) more theory-informed ways to model those data. Thus, in this article, we pursued what Marsh and Hau (2007) called substantive-methodological synergy. We grounded our substantive investigation in Trautwein et al.’s (2006) Homework Model, wherein student characteristics and motivation predict homework behaviors (i.e., homework effort, homework time), which in turn predict achievement. To better understand students’ homework behavior, we used digital tools that produced trace data that could be understood and modeled via theory-informed learning analytics. We collected homework behavior data and subsequent achievements from 507 German academic-track school students who used an intelligent tutoring system to learn English as a foreign language. Our initial analyses showed that theory-aligned digital trace data captured unique information beyond self-report data. Then, we found homework effort, as conceptualized in the Homework Model and captured via theory-informed learning analytics, predicted academic performance, whereas homework time did not. Overall, behavioral trace measures of homework effort were more predictive than self-reports. These findings help to clarify the mixed findings in the homework literature and illustrate the benefits of substantive-methodological synergy between theory and learning analytic methods.