The science of learning has generated a wealth of research and theory on how people think and learn. One product of this research is what are known as 'principles of learning' - teaching and learning strategies that have garnered empirical support for their effectiveness at enhancing learning across a range of learners and domains in rigorous lab and classroom tests. For example, the principle of interleaved practice involves mixing problems that can be solved using different solutions -- to support discrimination learning - and spacing problems that can be solved using the same solution over time -- to support long-term memory retention [1]. In the present work, we describe an on-going cluster randomized trial where we attempt to test the efficacy of interleaved practice across multiple schools and geographic regions in a nationally representative U.S. sample. Despite strong evidence that interleaved practice leads to greater math learning than blocking problems [2], blocked practice predominates math curricula [3]. This finding follows a general trend that learning research is underutilized in educational contexts [4]. One reason for this disconnect is that learning takes place within a dynamic system that is highly contextual [5]. What works in one context may not work the same way in another. As a result, scaling learning research remains a major challenge. Although interleaving is seemingly straightforward for application, we describe several challenges we faced in our own work when attempting to scale interleaving in this large-scale study. To address these challenges we outline a process guided by a working implementation model for translating interleaving to concrete materials. We also describe early results of our first (of three) data collection years assessing the impact of a mostly interleaving intervention relative to a mostly blocked intervention on students' math achievement. Although our work is ongoing, the lessons learned even at this early stage can have important implications for scaling interleaved practice as well as learning principles more broadly.

Solving mathematical problems is cognitively complex, involving strategy formulation, solution development, and the application of learned concepts. However, gaps in students' knowledge or weakly grasped concepts can lead to errors. Teachers play a crucial role in predicting and addressing these difficulties, which directly influence learning outcomes. However, preemptively identifying misconceptions leading to errors can be challenging. This study leverages historical data to assist teachers in recognizing common errors and addressing gaps in knowledge through feedback. We present a longitudinal analysis of incorrect answers from the 2015-2020 academic years on two curricula, Illustrative Math and EngageNY, for grades 6, 7, and 8. We find consistent errors across 5 years despite varying student and teacher populations. Based on these Common Wrong Answers (CWAs), we designed a crowdsourcing platform for teachers to provide Common Wrong Answer Feedback (CWAF). This paper reports on an in vivo randomized study testing the effectiveness of CWAFs in two scenarios: next-problem-correctness within-skill and next-problem-correctness within-assignment, regardless of the skill. We find that receiving CWAF leads to a significant increase in correctness for consecutive problems within-skill. However, the effect was not significant for all consecutive problems within-assignment, irrespective of the associated skill. This paper investigates the potential of scalable approaches in identifying Common Wrong Answers (CWAs) and how the use of crowdsourced CWAFs can enhance student learning through remediation.

Advances in the learning analytics community have created opportunities to deliver early warnings that alert teachers and instructors when a student is at risk of not meeting academic goals [6], [71]. Alert systems have also been developed for school district leaders [33] and for academic advisors in higher education [39], but other professionals in the K-12 system, namely guidance counselors, have not been widely served by these systems. In this study, we use college enrollment models created for the ASSISTments learning system [55] to develop reports that target the needs of these professionals, who often work directly with students, but usually not in classroom settings. These reports are designed to facilitate guidance counselors' efforts to help students to set long term academic and career goals. As such, they provide the calculated likelihood that a student will attend college (the ASSISTments College Prediction Model or ACPM), alongside student engagement and learning measures. Using design principles from risk communication research and student feedback theories to inform a co-design process, we developed reports that can inform guidance counselor efforts to support student achievement.

The well-studied Baker et al., affect detectors on boredom, frustration, confusion and engagement concentration with ASSISTments dataset were used to predict state tests scores, college enrollment, and even whether a student majored in a STEM field. In this paper, we present three attempts to improve upon current affect detectors. The first attempt analyzed the effect of missing skill tags in the dataset to the accuracy of the affect detectors. The results show a small improvement after correctly tagging the missing skill values. The second attempt added four features related to student classes for feature selection. The third attempt added two features that described information about student common wrong answers for feature selection. Result showed that two out of the four detectors were improved by adding the new features.