Decomposition is a foundational computational thinking construct that is often introduced early as students are learning computer science in the elementary grades. Although decomposition is often described in early computational activities, little research exists about how to teach and assess students’ understanding of decomposition. In this mixed-methods research study, 173 third-grade students from eight elementary school classrooms in the Midwest were taught eight lessons that integrated decomposition as well as other computational thinking practices into their mathematics instruction. They completed a computational thinking assessment after the first four lessons and again after the second four lessons. Analyses included the distribution of correct decomposition item responses, confirmatory factor analysis, and item-level error analysis. Results indicate wide variability in students’ performance on the decomposition assessment items as well as in performance on items contextualized within mathematics. This study highlights the need for additional considerations about assessing computational understanding, implications for assessment within integrated contexts, and the use of paper-and-pencil tests compared to embedded assessments.

This study presents a novel video recommendation system for an algebra virtual learning environment (VLE) that leverages ideas and methods from engagement measurement, item response theory, and reinforcement learning. Following Vygotsky's Zone of Proximal Development (ZPD) theory, but considering low affect and high affect students separately, we developed a system of five categories of video recommendations: 1) Watch new video; 2) Review current topic video with a new tutor; 3) Review segment of current video with current tutor; 4) Review segment of current video with a new tutor; 5) Watch next video in curriculum sequence. The category of recommendation was determined by student scores on a quiz and a sensor-free engagement detection model. New video recommendations (i.e., category 1) were selected based on a novel reinforcement learning algorithm that takes input from an item response theory model. The recommendation system was evaluated in a large field experiment, both before and after school closures due to the COVID-19 pandemic. The results show evidence of effectiveness of the video recommendation algorithm during the period of normal school operations, but the effect disappears after school closures. Implications for teacher orchestration of technology for normal classroom use and periods of school closure are discussed.

Students using online learning environments need to effectively self-regulate their learning. However, with an absence of teacher-provided structure, students often resort to less effective, passive learning strategies versus constructive ones. We consider the potential benefits of interventions that promote retrieval practice – retrieving learned content from memory – which is an effective strategy for learning and retention. The goal is to nudge students towards completing short, formative quizzes when they are likely to succeed on those assessments. Towards this goal, we developed a machine-learning model using data from 32,685 students who used an online mathematics platform over an entire school year to prospectively predict scores on three-item assessments (N = 210,020) from interaction patterns up to 9 minutes before the assessment as well as Item Response Theory (IRT) estimates of student ability and quiz difficulty. These models achieved a student-independent correlation of 0.55 between predicted and actual scores on the assessments and outperformed IRT-only predictions (r = 0.34). Model performance was largely independent of the length of the analyzed window preceding a quiz. We discuss potential for future applications of the models to trigger dynamic interventions that aim to encourage students to engage with formative assessments rather than more passive learning strategies.