Abstract: The Partnership for Los Angeles Schools (Partnership), in collaboration with Student Achievement Partners (SAP) and EdLight, proposes a 13-month research initiative to examine how a structured, continuous improvement cycle grounded in formative assessment informs educator instructional decision-making and supports asset-based mathematics teaching. Building on a district-wide professional learning initiative launched in 2024–25, this project will engage teachers across three Partnership schools in a structured improvement cycle. The cycle pairs the EdLight platform, which analyzes handwritten student work using AI, with the California Math Framework Learning Walk tool* to collect classroom-level instructional data. The study addresses a priority question for the Partnership: How do teachers interpret and use formative assessment data, made visible through student work and technology, to inform instructional decision-making in ways that center student mathematical thinking — particularly for historically marginalized learners?
Principal Investigators:
Dr. Alfonso Romero is the Senior Director of Secondary Math at the Partnership for Los Angeles Schools.
Abstract: Seeing Student Thinking investigates how EdLight’s AI-assisted formative assessment platform supports grades 6–8 mathematics teachers in interpreting student thinking, addressing misconceptions, and improving instruction in the School District of Philadelphia. Led by ImpactSTATS Inc., a national research and evaluation nonprofit, the mixed-methods study will examine the implementation of the platform and its influence on teacher decision-making, instructional practices, student engagement, and mathematics persistence.
Participating educators will engage in Professional Learning Communities (PLCs) with fellow math teachers to collaboratively analyze student work, identify instructional strategies, and reflect on patterns in student thinking. The study will explore how AI can be responsibly integrated into mathematics classrooms to better support educators, improve student learning, expand access to high-quality instruction, and improve outcomes for historically underserved students.
Principal Investigators:
Melodie Baker, Ph.D. is the Founder and Executive Director of ImpactSTATS Inc., a nonprofit research and policy organization that uses data, research, and evidence-based strategies to advance equitable education systems.
Kristian Monet is a Project Coordinator with ImpactSTATS Inc. and a junior Computer Science major at Howard University with a focus on data science.
Dr. Claire E. Cameron teaches mixed methods, doctoral student writing, and child development online.
Chris Rates has a PhD in Science Education from the University of Virginia, where he gained expertise in complex systems.
Abstract: Every school evaluating EdLight has been asked to judge it through whatever camera happened to be in the room, usually a Chromebook tilted at the wrong angle under bad light. That’s like testing a precision audio tool on speakers built for something else and concluding the tool is weak. KIPP NorCal proposes a two-year study to fix the question itself: under what hardware configurations and implementation conditions does EdLight actually maximize its impact on formative assessment and student math outcomes, especially for students with IEPs, English Learners, and students reading below grade level?
Using KIPP NorCal’s three-tier validation model (Regional AI Task Force to micro-pilot to Catalysts), Year 1 runs a controlled micro-pilot at one school, with trained teachers testing rapid scanners, document cameras, and Chromebook cameras side by side while we measure AI diagnostic accuracy, teacher adoption, and student growth across conditions and subgroups. Year 2 scales the winning configuration through 17 Catalyst teacher-leaders. The deliverable is an implementation playbook (the EdLight Launch Kit) that any similarly structured network can deploy, plus openly licensed findings, making this the first study to treat hardware as an independent variable in AI-assisted formative assessment.
Principal Investigators:
Jennie Dougherty is the Director of Strategic Initiatives at KIPP NorCal.
Poorvi Goradia Iyengar is the Associate Director of Academics at KIPP Public Schools Northern California and supports 5-8 Math and 5-12 Science implementation across the region.
Abstract: Early warning systems in digital learning platforms typically rely on behavioral engagement indicators such as assignment completion, time on task, and click patterns. While these measures detect disengagement, they overlook students who remain behaviorally compliant while accumulating conceptual misconceptions that predict later mathematics failure. This limitation has instructional and equity implications, particularly for students whose persistence masks fragile understanding. This study tests the predictive value of feature-engineered cognitive error patterns derived from EdLight’s longitudinal, AI-annotated corpus of handwritten middle school mathematics work. Using secondary data from grades 6-8, we will engineer temporal features from misconception tags and rubric scores, including error-type slopes, rates of conceptual versus procedural error accumulation, profile volatility, and transitions across misconception categories. These features capture the dynamic structure of student understanding rather than static misconception rates or aggregate performance. Through nested predictive modeling and early-warning timing analyses, we will evaluate whether engineered trajectory features improve prediction of end-of-year mathematics outcomes beyond static cognitive indicators and behavioral engagement metrics. Subgroup calibration and fairness analyses will assess performance across English learners, students with IEPs, and racial or ethnic groups. Findings will inform practitioner-facing guidance that integrates trajectory-based signals into formative assessment routines within EdLight.
Principal Investigators
Nicholas A. Vest, Ph.D., is a Postdoctoral Researcher in the School of Teaching and Learning at the University of Florida.
Avery Closser, Ph.D., is an Assistant Professor of Educational Technology at the University of Florida and a member of the Institute for Advanced Learning Technologies.
Xintian Gao is a second-year Ph.D. student in Educational Technology at the University of Florida.
Abstract: Teachers are increasingly expected to use student work to drive instructional decision-making, yet many lack the pedagogical content knowledge necessary to accurately diagnose misconceptions and design targeted instructional responses. Consequently, analysis of student work often results in broad re-teaching rather than precise interventions that address students’ underlying misunderstandings. This mixed-methods practitioner research study investigates whether AI-supported analysis of student work can strengthen both teachers’ diagnostic expertise and their pedagogical content knowledge in middle school mathematics. Conducted across grades 6–8 at East Harlem Scholars Academies, the study examines how integrating EdLight’s AI-powered platform into professional learning communities, coaching, and instructional planning influences teachers’ ability to identify misconceptions, develop standards-aligned instructional responses, and improve student learning outcomes. Rather than positioning artificial intelligence as a replacement for teacher expertise, this research explores AI as a catalyst for deeper teacher learning by anchoring professional development in authentic examples of student thinking. Findings from this study aim to inform emerging models of AI-enhanced professional learning that simultaneously build teacher capacity and improve instructional precision in mathematics classrooms.
Principal Investigators
Martin Palamore is the Chief Schools Officer at East Harlem Scholars Academies, where he leads academic strategy and school leadership development across a network of Pre-K–12 schools.
Abstract: Leap Educational Consulting will partner with secondary schools to study how EdLight supports instructional leaders and teachers in identifying student misconceptions, strengthening feedback cycles, and making data-informed instructional decisions in mathematics.
Principal Investigators
Jerry Silva is one of the founders of Leap and, prior to founding Leap, was a co-Senior Director of Achievement First’s Navigator Middle School Math team.
Jen Caruso is one of the founders and leaders of Leap. Prior to starting Leap, she served as Co-Senior Director of the Middle School Math Team for Navigator.
roject Abstract: New Visions proposes to integrate EdLight tools into the collection and analysis of data from interim assessments aligned with high-stakes end-of-year testing. We will then research how well this integration supports teachers shifting their attention from data capture to data analysis that better supports student success on those high-stake assessments.
Among the challenges of HQIM implementation, teachers often face a gap between effective classroom formative assessment and high-stakes end-of-year assessments. To bridge this gap, New Visions has provided annual “Mock Regents” where practice exams were supported with data analysis protocols and instructional guidance. We recently added “Interim Assessments,” starting with Algebra I, which provided shorter, more frequent assessments aligned both with specific curricula and end-of-year assessment expectations. We will look deeply at participating schools’ workflows to measure how well the addition of EdLight tools to interim assessment analysis demands less time for scoring and affords more time for instructional planning, To support effective use of the afforded time, we will test several existing data protocols to determine how they can best support team and teacher analysis of the data as part of their planning for year-end student success.
Principal Investigator
Russell West Jr. supports the Curriculum & Instruction team at New Visions. Additionally, Russell directs the Astor Center for Public School Libraries and provides legal counsel to the organization’s management team.
Abstract: Foxborough Regional Charter School(FRCS) is partnering with EdLight to strengthen instructional leadership and improve the quality and consistency of classroom feedback across our K–12 district. Building upon our district’s Instructional Vision, the project focuses on developing a shared understanding of high-quality instruction through aligned classroom observations, evidence-based feedback, and coaching cycles. School and district leaders will use the EdLight platform to calibrate observations, identify instructional trends, and leverage real time data to inform professional learning and continuous improvement.
Through this work, the district aims to build leadership capacity, create greater coherence across all three schools, and ensure that instructional feedback consistently supports teacher growth and improved student outcomes. The project will establish sustainable systems for monitoring implementation, measuring progress, and using instructional data to drive decision making while advancing equitable learning opportunities for every student.
Principal Investigator
Kathleen Foley serves as the Director of Teaching and Learning at Foxborough Regional Charter School (FRCS).
Abstract: Digital learning platforms (DLPs) are widely used to provide personalized math instruction, yet little is known about how students should structure their engagement. This study examines whether within-lesson engagement patterns—such as sustained attention, pacing, idle time, and lesson completion—predict middle school students’ math mastery and growth on Curriculum Associates’ i-Ready Personalized Instruction. Drawing on research on cognitive load, sustained attention, and skill acquisition, we examine whether fragmented or rushed engagement reduces learning, even when total time-on-task is the same.
Using data from all 17 Washoe County, Nevada, middle schools during the 2025–26 school year, we will analyze lesson logs, clickstream data, and assessment outcomes alongside teacher interviews from five schools. Findings will inform digital learning platform design, usage recommendations, and instructional practices to help maximize student learning and promote equitable math outcomes.
Principal Investigators
Amy Auletto is an education policy researcher and the Assistant Director of the Northwestern Collaborative for Applied Research in Education (NCARE).
Paul Goren serves as Executive Director of the Northwestern Center for Applied Research in Education (NCARE) at the Northwestern School of Education and Social Policy.
Abstract: Guided by the framework of data-based decision making, this study analyzes the 2024-2025 i-Ready educator report to identify distinct report-use strategies and examine how they evolve across fall, winter, and spring diagnostic cycles. School characteristics will be incorporated to investigate how contextual factors shape strategy prevalence and transitions. Finally, the predictive effect of report-use strategies on student growth will be examined at the school level. By moving beyond simple usage counts, this study conceptualizes report-use through multidimensional engagement indicators. Findings will provide actionable insights for learning platform designs, informing professional learning supports, and strengthening the alignment between diagnostic reporting and instructional decision-making.
Principal Investigator
Dr. Yuxi Qiu is an Assistant Professor of Research Methodology at Florida International University.
Abstract: This project leverages de-identified student log data from middle school mathematics classrooms using Khan Academy to integrate theory-informed indicators of struggle quality into knowledge tracing models. Using time-stamped records of performance, hint usage, retries, and time-on-task, we will develop extended models that distinguish productive from unproductive struggle states and examine how these states predict persistence, mastery acquisition, and disengagement. We will also investigate whether these relationships vary across content modalities (e.g., practice exercises and quizzes). By embedding theoretically grounded measures of struggle into mastery modeling, this study advances understanding of persistence in digital mathematics environments while generating actionable insights to improve detection and support of student struggle at scale.
Principal Investigator
Yang Shi is an Assistant Professor at Utah State University.
Abstract: This project examines how teacher engagement in online professional learning can be measured and how distinct engagement patterns relate to teacher learning outcomes in the Math Matrix micro-credential platform. Using existing platform, gradebook, assessment, and survey data, the project will construct interpretable engagement indicators that capture dimensions such as content coverage, activity intensity, pacing, persistence, and course completion. These indicators will be used to identify teacher engagement profiles and examine their impacts on outcomes including post-assessment performance, learning gains, mastery, completion, and survey-reported transfer to practice. By combining theory-guided feature engineering, exploratory data review, latent profile analyses, and causal-inference approaches for multi-valued engagement profiles, the project aims to move beyond descriptive platform metrics and generate decision-ready evidence about which forms of engagement appear most productive for supporting teacher learning.
Principal Investigators
Wei Li is an Associate Professor in the Research and Evaluation Methodology Program at the University of Florida.
Katherine Strickland, Ph.D., is a Postdoctoral Researcher in the Research and Evaluation Methodology program at the University of Florida.
Abstract: Teacher disengagement in digital professional development programs is poorly understood and rarely detected until it is too late to address. This study uses the Math Matrix Micro-Credential, an asynchronous competency-based PD platform developed by the Lastinger Center for Learning, to examine this problem directly. Drawing on behavioral trace data, competency-based assessment records, and teacher background information from the Math Matrix platform, we pursue three aims. We first map engagement trajectory patterns and their relationship to mastery outcomes, then identify early behavioral indicators that predict disengagement or mastery failure within the first two to three weeks of participation, and finally examine whether predicted risk profiles differ systematically across teacher characteristics, including certification pathway, years of experience, grade band, and role. We use trajectory modeling to identify engagement patterns over time and machine learning models to detect early warning signals. Findings will provide actionable evidence for designing timely interventions within the Math Matrix platform and contribute broader insight into how digital professional development systems can identify and support teachers at risk of disengagement.
Principal Investigators
Anna Kye, PhD, is a Postdoctoral Scholar jointly appointed in the Postsecondary Education Research Institute (PERI) and the Division of Teaching Excellence and Innovation (DTEI) at the University of California, Irvine.
Abstract: This study proposes a cognitive – engagement joint modeling framework for Math Matrix Micro-Credential data that integrates evidence of teachers’ conceptual understanding and misconceptions with behavioral engagement patterns in a unified analytical pipeline. Rather than treating platform use and teacher learning as separate outcomes, the project examines how assessment performance, assignment attempts, available rubric or response data, cumulative content visits, time spent on course pages, survey responses, and teacher background characteristics jointly relate to course mastery and micro-credential completion. Analyses combine engagement profile clustering, item- and assignment-level performance analysis, graph-based representation of teacher-concept-misconception relationships, and mixed-effects predictive modeling. Where open-ended data are available, human-guided coding and validated language-model-assisted analysis will identify recurring themes in teacher conceptions. The novelty of the project lies in connecting teacher cognition and engagement behavior within an interpretable, data-feasible modeling framework. Findings will provide the Lastinger Center with actionable evidence for improving course design, identifying differentiated teacher supports, and advancing research on asynchronous professional learning at scale.
Principal Investigators
Dr. Bo Pei is an Assistant Professor in the Instructional Technology Program, Department of Educational and Psychological Studies, College of Education, at the University of South Florida.
Abstract: Our project, “Uncovering Teacher Engagement and Learning in the Math Matrix Micro-Credential”, investigates how mathematics teachers engage with a competency-based digital professional learning platform and how their learning develops over time. The goal is to generate actionable insights that improve the platform, including better scaffolding of difficult concepts and more personalized support for different groups of teachers. Ultimately, this work will support the design of more effective, adaptive professional learning systems for mathematics educators.
Using data from the Math Matrix Micro-Credential, we examine how teachers progress through targeted skills and identify factors that influence engagement and learning. Our analysis focuses on three areas: patterns of teacher engagement, common mathematical misconceptions, and growth in mastery of key competencies. We also explore how these patterns vary by teacher characteristics such as experience level, grade level taught, and engagement level.
Principal Investigators
Dr. Hailey Kuang is an Assistant Professor of Measurement and Statistics at Florida State University.
Dr. Secil Caskurlu is an Assistant Professor of Instructional Systems and Learning Technologies at Florida State University.
Abstract: This study will investigate whether teacher characteristics can predict teachers’ learning gains and systematic patterns of misconception persistence in the Math Matrix Micro-credential. Furthermore, AI-powered recommendation models will be trained and validated to identify content areas with a high risk of persistent misconceptions. The study will use teacher characteristics to build theoretically grounded teacher profiles and examine them with assessment and end-of-course datasets to determine which teacher profiles are most vulnerable to misconception persistence and whether risk patterns vary systematically across content areas and courses. Findings will advance Math Matrix’s personalization strategy by predicting which teacher profiles show the greatest knowledge growth and which topics pose a greater risk of persistent misconceptions. This study will also contribute to the broader conversation about how asynchronous Digital Learning Platforms (DLPs) can be designed to serve heterogeneous teacher populations.
Principal Investigators
Shristi Shrestha is a Ph.D. student in Human Computer Interaction at Iowa State University with research interests in AI-augmented learning environments.
Jiyeong Yi is an Associate Professor at Iowa State University, where she develops instructional frameworks to support Emergent Bilinguals in rigorous mathematics.
Abstract: This study examines how variations in video engagement, including timing, duration, sequencing, tutor selection, and content choices, are associated with mathematics learning. Grounded in a scaffolding-oriented perspective, instructional video is conceptualized as a form of digital support through which students access and interact with mathematical content. Leveraging large-scale activity log data, this study will identify distinct patterns of video engagement and examine how these patterns relate to assessment performance. In addition, the study will explore which aspects of engagement are most strongly associated with learning and whether early engagement behaviors are indicative of later performance. Findings will provide actionable insights into how students use instructional video within a digital learning platform and which patterns of engagement are most supportive of mathematics learning. Results will inform the ongoing development of Math Nation and contribute to broader efforts to improve the design and effectiveness of digital learning environments in mathematics education.
Principal Investigators
Dr. Qingli Lei is an Assistant Professor in the Department of Special Education at the University of Illinois Chicago.
Abstract: Math Nation’s (MN) documented achievement gains in Florida, including a 2.9-point increase in FAST math scores and a 2.6-percentage-point increase in Level 5 proficiency in 2022-23, raise a natural next question: which patterns of student engagement with the platform’s features, especially videos, drive those gains? This project uses event-level video log data from ~140,000 middle school students in grades 6–8 to investigate how students engage with instructional videos over time and how these engagement patterns relate to their adaptive engagement and mathematics performance. The project will use latent class analysis to identify distinct engagement profiles from students’ video-use behaviors and sequential pattern mining to characterize common engagement patterns over time, including language selection in video viewing.
Principal Investigators
Sarah Clerjuste is a Postdoctoral Research Associate in the RISE Program at the University of Wisconsin–Madison’s Wisconsin Center for Education Research.
Dr. Ana Stephens is a researcher at the Wisconsin Center for Education Research at the University of Wisconsin–Madison.
Martha Wagner Alibali is the Susan Goldin-Meadow Professor of Psychology and an affiliate of the Wisconsin Center for Education Research at the University of Wisconsin–Madison.
Abstract: This study will investigate how variations in curriculum navigation and behavioral engagement relate to short-term learning gains, and to build machine learning models that predict immediate learning outcomes. We detail a two-phase approach: first, identifying and quantifying different learning pathway profiles among participants; second, training and evaluating predictive models to forecast post-assessment performance. This project will yield insights into which engagement behaviors and content navigation strategies correlate with better short-term learning in the Math Matrix program, and demonstrate how AI techniques can enhance educational outcomes by early identification of at-risk learners.
Principal Investigators:
Shuman Wang is a Ph.D. student in curriculum and teacher education at Stanford University.
Ari Jiayu An is a master’s student in education data science at Stanford University.
Abstract: This study employs Machine Learning (ML) techniques to predict math achievement trajectories for striving learners by analyzing engagement and persistence patterns within the i-Ready platform. Despite the potential of personalized instructional platforms to accelerate academic growth, significant variability exists in outcomes among users, some students make remarkable gains while others stagnate. By developing predictive models based on early-stage engagement indicators and persistence behaviors, this research distinguishes students likely to accelerate from those at risk of stagnation. The findings might provide educators with actionable insights for timely, targeted interventions and support Curriculum Associates in enhancing i-Ready’s responsiveness through improved early-warning tools. This work addresses both practical and methodological gaps in educational research, demonstrating how predictive analytics can transform support for striving learners when intervention matters most.
Principal Investigators:
Eter Mjavanadze is a doctoral student at George Mason University.
Angela Miller is an associate professor in research methods and educational psychology at George Mason University.
Abstract: This study investigates teacher engagement in the Math Matrix Digital Learning Platform (DLP) to understand factors that drive or hinder motivation. Situated expectancy-value theory (SEVT) is a motivational theory suggesting that an individual’s perceived competence and values predict decision-making and outcomes. Thus, we will use SEVT to examine teachers’ takeaways and challenges of using the DLP and how engagement and teacher characteristics (i.e., grade level, gender, race) predict competence beliefs, perceived usefulness, instructional strategy implementation, and ability to motivate their students. Findings will inform instructional design of the Math Matrix DLP to ensure effective teaching practices and student learning. This study will contribute to the broader conversation about the effectiveness of DLPS as research infrastructure. Leveraging platform-generated data will provide insights into the relation between teacher engagement with the platform and teacher motivation and whether patterns vary based on important teacher characteristics (e.g., grade level taught, experience level, etc.). These findings will not only inform the refinement of the Math Matrix DLP but also aligns with the goals of the DLP to offer guidance for the broader design and implementation of DLPs in mathematics education. The findings from this research will offer valuable insights into how DLPs can be optimized to improve mathematics instruction through enhancing teacher motivation, ultimately supporting student learning outcomes.
Principal Investigators:
Dr. Patrick Beymer is an assistant professor of psychology at the University of Cincinnati.
Dr. Jessica Gladstone is an assistant professor of educational psychology at the University of Illinois, Urbana-Champaign.
Abstract: The project aims to develop methodology and algorithms that optimize learning paths for improved efficiency. It will integrate robust measurement and knowledge tracing models to estimate students’ abilities along with their associated measurement errors. A Deep Q-Network-based recommendation system will guide learning by balancing skill improvement with reductions in measurement uncertainty, resulting in highly personalized learning paths. In addition, the proposed methodology will help us understand transfer of learning to new topics and estimate rates of acquisition and forgetting over time. This work directly supports PI’s (Yikai EK Lu’s) trajectory as an early-career researcher transitioning into a tenure-track assistant professor role.
Principal Investigators:
Yikai “EK” Lu is a doctoral candidate at the University of Notre Dame.
Dr. Ying (“Alison”) Cheng is professor of psychology at University of Notre Dame.
Abstract: Knowledge tracing (KT) predicts learning performance by analyzing past behaviors to enable adaptive instruction and precision education, particularly within digital platforms such as Math Matrix Micro-Credential. Traditional KT models primarily rely on sequential patterns (e.g., temporal sequence of assessment responses), often downplaying valuable semantic insights from textual data, such as problem similarity, embedded concepts, and prerequisite relationships. Meanwhile, prior KT methods have largely prioritized predictive accuracy, offering limited transparency into the underlying decision-making processes and leaving educators with few actionable insights. Leveraging the rich assessment data, open-ended responses, and user behaviors from Math Matrix Micro-Credential, we propose TRACELLMs: an explainable KT framework that fuses sequential interaction data, assessment text, and teachers’ historical learning records to evaluate conceptual understanding and deliver actionable feedback. We will benchmark TRACE-LLMs against existing KT methods to demonstrate its feasibility, robustness, and scalability. Successful validation of TRACE-LLMs will enable its integration into Math Matrix Micro-Credential for precise tracking of teacher competency growth, AI-powered mentoring, and personalized learning pathways, ultimately advancing high-quality, adaptive, and scalable professional learning experiences.
Principal Investigator:
Dr. Chenglu Li is an assistant professor of learning sciences at the University of Utah
Abstract: Carnegie Mellon University researchers are developing a new approach to enhance student engagement and persistence in mathematics using the i-Ready adaptive learning platform. Led by PhD student Conrad Borchers in collaboration with Co-PIs Vincent Aleven, Ken Koedinger, and Danielle Thomas, the project explores how regular, personalized goals and feedback can help middle school students stay motivated and on track toward long-term proficiency growth targets. Building on prior success from the PLUS tutoring project, where goal setting increased engagement by 25% and skill mastery by 40%, this initiative seeks to design scalable goal-setting tools integrated into i-Ready’s existing features.
The planning grant will support the creation of persistence analytics and adaptive goal recommendations using i-Ready’s lesson and diagnostic data. In partnership with Curriculum Associates and a school district to be selected, the project aims to empower teachers to guide students in setting and achieving goals, including through i-Ready’s “Data Chats” and growth tracking tools. Ultimately, the project advances research on student motivation and self-regulated learning while aligning with AIMS EduData’s mission to generate actionable insights from digital learning platforms.
Principal Investigator:
Conrad Borchers is a Ph.D. student at the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University’s School of Computer Science
Abstract: KIPP Team and Family, in partnership with KIPP New Jersey (Camden), will conduct a research-practice partnership with Khan Academy to evaluate AI-powered math interventions for underperforming students in high-variance classrooms. The partnership will investigate how Khanmigo can be leveraged alongside I-Ready diagnostic data to create targeted interventions that address this extreme variance, particularly for students performing below grade level within the Illustrative Mathematics curriculum, among other research questions.
Principal Investigator:
Kevin Shaw is the director of AI, innovation, and strategy at KIPP Team and Family
Abstract: This project explores how teachers interact with and benefit from online professional learning (PL), particularly in mathematics education. As more educators enter teaching through various pathways, ongoing PL becomes essential for ensuring high-quality instruction. Our project focuses on understanding teachers’ engagement, how they participate, think about, and value their learning experiences, and its impact on their teaching effectiveness. We’ll use survey and online activity data to identify patterns of engagement and connect them to teachers’ learning outcomes and perceptions. Insights from this project will help create better, more impactful online PL experiences that enhance mathematics teaching and student success.
Principal Investigators:
Dr. John Chukwunonso Ojeogwu is a STEM education postdoctoral researcher at the Desert Research Institute (DRI) and the University of Nevada Las Vegas.
Erin Smith is an associate professor of mathematics education at University of Nevada, Las Vegas.
