Background/Context: Math education remains a critical focus for national education improvement. As a solution, districts in the U.S. are investing in math education technologies. Research has demonstrated the potential of these technologies to close achievement gaps (e.g., Pape et al., 2012; Roschelle et al., 2016). Student math achievement is particularly important during the middle school years. 64% of fifth-grade students and 58% of sixth-grade students did not meet expectations in math (MDE, 2022). Notable losses in student motivation, engagement (Eccles et al., 1993; Eccles & Midgley, 1990), and self-concept around math (Royer & Walles, 2007) occur during the transition to middle school. As schools adopt digital programs, it is essential to attend to student affect while rigorously testing the efficacy of these platforms in improving students' achievement. Purpose/Research Question: We conducted a multi-year, large-scale randomized controlled trial of the MathSpring program during the 2020-23 school years. MathSpring is a technology-based learning environment that offers personalized content, remedial tutoring, and affective support for students, along with learning analytics reports for teachers. We investigated the extent to which the program supports math learning, growth mindset, and dispositions towards math, as well as teachers' practices. The study was guided by four research questions: Does MathSpring improve students' math achievement? Does MathSpring impact students' growth mindsets and disposition towards mathematics? What factors moderate the effects of MathSpring? What is the fidelity of implementation and the effects of implementation on learning? Setting/Participants: The study was conducted in Massachusetts in 47 public schools from 41 districts with diverse backgrounds. Sixty-four fifth- or sixth-grade math teachers participated over three cohorts. On average, 69% of the participating students were White, 7% were Black, and 15% were Hispanic. 56% of students didn't meet expectations of standards. Intervention: MathSpring is a web-based intelligent tutor [citations blinded for review] designed to support students' achievement, affect, and engagement with math. An effort-based tutoring algorithm considers a student's recent performance and level of effort to provide an adaptive selection of problems, keeping students within their zone of proximal development (Vygotsky, 1978). The platform also provides students with learning support (immediate feedback, hints, worked examples in videos) and a learning companion avatar who provides supportive statements (Fig. 1a). MathSpring features a student progress page where students can visualize their progress via a plant that blooms and grows peppers as students master more topics (Fig. 1b). Additionally, it provides student- and class-level analytics data to teachers to inform adjustments to instruction and pacing. Teachers can view effort data for individual students and the class (Fig. 1c) and use the data to determine whether students are putting in effort during problem-solving (i.e., not skipping or guessing). Research Design: The study employed a multisite, clustered randomized experimental design in which teachers were first blocked by school characteristics and then, within each block, randomly assigned to the treatment (n = 34) or control condition (n = 30). Treatment teachers were expected to use MathSpring to support math instruction at least once every other week and received professional development, ongoing support, and access to self-paced online modules. Control teachers continued using existing instructional practices and supplemental technologies and were offered training and access to the platform after the study. The study was approved by the WestEd Institutional Review Board and was pre-registered at Registry of Efficacy and Effectiveness Studies. Data Collection and Analysis: Student mathematics achievement was measured with the Massachusetts Comprehensive Assessment System (MCAS) end-of-year state standardized math assessment. The online Mathematics Diagnostic Testing Project Math Readiness Test (MRT) (Anthony, 2005) was administered as a supplemental math outcome measure. Previously validated SEL survey scales were used to measure students' growth mindset, learning strategies, and dispositions toward mathematics, including the Goal Orientations and Learning Strategies Survey (GOALS-S) (Dowson & McInerney, 2004), mindset scale (Dweck, 2006), Grit Scale (Duckworth & Quinn, 2009), and scales measuring attitudes towards mathematics (PISA 2012 Student Questionnaire). To understand the nature of implementation, we collected teacher pre- and post-surveys and instructional logs, interviews, and classroom observations. Two-level hierarchical linear models were applied to estimate MathSpring's impact on student math learning and SEL survey scales. Researchers coded the qualitative data (35 teacher interviews, 11 classroom observations, 10 observation follow-up interviews) according to four SEL sub-categories: 1) the importance of effort, 2) problem-solving strategies, 3) goals, and 4) emotions. The backend system data, which tracked student actions and teacher usage of reports, were used to analyze implementation fidelity. Findings/Results: The analytic sample included 53 teachers (30 treatment, 23 control; overall cluster-level attrition = 17.19%, differential attrition = 11.57%). Analyses of the MCAS test scores are ongoing and will be shared at the conference. Here, we report initial results from the MRT and the SEL survey scales with 1,757 students (832 treatment, 925 control; overall student-level attrition = 12.28%, differential attrition = 6.22%). Findings from the teacher interviews and observations indicated that teachers in the treatment group mentioned each coded SEL sub-category significantly more often than teachers in the control group (Table 1). Teachers used MathSpring's analytics to spark class discussions around effort and problem-solving behaviors to help students develop self-regulated learning strategies. Yet, the impact analysis results indicated that the students in the treatment group did not outperform those the control group on the MRT, and the groups did not differ on the SEL survey scales either. Conclusions: There are numerous visions for leveraging technology to enhance math outcomes. This study investigated the effectiveness of an educational technology intervention that addresses math learning and attitudes towards math. The results indicate that using MathSpring during math time influenced teaching practices; treatment teachers incorporated SEL topics more frequently with their students than control teachers. However, initial analyses did not reveal conclusive evidence of the intervention's impact on student math learning. Ongoing analyses are examining the MCAS outcome measure, fidelity of implementation, the relation between fidelity and student learning, the service contrast between conditions, and a moderation analysis of student subgroups. These findings will contribute to our understanding of the interventions that support students' math learning and SEL outcomes.