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LASER Institute

The Learning Analytics in STEM Education Research (LASER) Institute is a professional development program for early and mid-career researchers and funded by the National Science Foundation (DRL-2025090 and DRL-2321128).

Program Goals

As the use of digital teaching and learning resources continues to expand, the volume and variety of data available to researchers presents new opportunities for understanding and improving STEM education. The LASER Institute aims to increase the capacity of early and mid-career scholars to leverage new data sources and apply computational methods (e.g., network analysis, text mining and machine learning) to support their existing research and develop new lines of inquiry. Located at the Friday Institute for Educational Innovation, the LASER Institute is a collaborative effort between North Carolina State University, University of Pennsylvania, University of Florida and the University of Tennessee, Knoxville.

The LASER Institute focuses on building the capacity of scholars to conduct high-quality research in three primary domains:

  • Disciplinary Knowledge: Scholars will deepen their understanding of LA methodologies, literature, applications and ethical issues as they relate to STEM education and equity.
  • Technical Skills: Scholars will develop proficiency with R, Python, Quarto, GitHub and other tools used for collaboration, reproducible research and computational analyses.
  • Social Capital: Scholars will expand their professional networks, connecting with researchers and experts in LA related fields, as well as other scholars focused on STEM education.

Institute Details

Watch LASER Advisory Board Member Dr. Alyssa Wise deliver our first Summer Workshop keynote.

The LASER Institute is a year-long program consisting of two core components:

  1. Summer Workshop: The Friday Institute will host an intensive 5-day program consisting of learning labs, planning sessions, and community-building activities.
  2. Online Community: An online community of practice for ongoing networking and support throughout the year.

The summer workshop for the 2024 cohort will run from July 22-26 and be hosted in Raleigh, NC, on the campus of North Carolina State University. A reimbursement allowance for $1500 will be provided for travel, lodging, and evening meals (breakfast and lunch will be provided each day) to support participation.

For more information about the LASER Institute including eligibility and how to apply, see below:

Program activities are designed to prepare researchers with the knowledge, skills and resources necessary for more advanced study of LA and for collaborating with researchers and practitioners from different backgrounds, especially those from advanced data analytics. By the end of the program, participants will be able to:

  • Describe STEM education questions and issues that can be addressed by LA and associated analytical approaches/applications;
  • Identify relevant and appropriate STEM educational data sources appropriate for computational analyses;
  • Apply computational techniques (e.g. machine learning and text mining) using R or Python to prepare, explore and model STEM education data;
  • Evaluate both the technical feasibility and ethical issues in using analytics to support STEM teaching and learning, and school and district-level decision-making; and
  • Develop a research and/or teaching agenda that seeks to address challenges in STEM education from a Learning Analytics lens.

The LASER Institute curriculum covers a broad range of both introductory and advanced research methods frequently leveraged by LA researchers. Each method area described below consists of four carefully scaffolded learning modules designed to prepare participants for collaborative, data-intensive research, and to lower the barriers faced by scholars with limited programming experience or research backgrounds in advanced methods. Introductory learning modules focus on basic proficiency with software tools commonly employed in LA and data science more broadly (i.e., R, Python, GitHub, APIs) and focus on topics pertaining to data-intensive research workflows. Modules addressing advanced methods focus on a range of exploratory data analysis and modeling techniques.

  1. The Learning Analytics Workflow is designed to provide participants an overview of the field of Learning Analytics and prepare them to wrangle, explore, model and communicate data using R, Python, and Quarto.
  2. Predictive Analytics introduces scholars to applications of supervised machine learning in STEM educational settings and prepare them to conceptualize educational problems, build and evaluate models, and work with a wide range of algorithms and methods to address those problems.
  3. Structure Discovery introduces scholars to applications of unsupervised machine learning in STEM educational settings and prepare them to conceptualize educational problems, build and evaluate models, and work with a wide range of algorithms and methods to address those problems.
  4. Text Mining provides an introduction to text mining concepts, applications in STEM Ed contexts, and applied experience with widely adopted tools and techniques such as tf-idf and sentiment analysis, topic modeling, text classification, and large language models.
  5. Social Network Analysis introduces scholars to social network theory and how network analysis can be applied in online and blended learning environments. Students will learn to calculate network statistics, visualize network properties and use modeling to discover underlying structures and factors impacting their development.
  6. Knowledge Inference prepares scholars to leverage techniques that model the knowledge of a student at a specific point in time as they interact with coursework and assessment activities. Techniques introduced in these modules include Bayesian, Logistic, and Deep Knowledge Tracing.
  7. Relationship Mining supports scholars in discovering relationships between variables in a dataset with a large number of variables. Scholars will learn how to identify variables strongly associated with a single variable of particular interest and discover which relationships between any two variables are strongest. Modules in this area cover mining basics, association rule mining, correlation mining, and causal data mining.

LASER instructional modules consist of carefully scaffolded activities designed to prepare participants for collaborative, data-intensive research, and to lower the barriers faced by scholars with little programming experience or research backgrounds in advanced methods. These activities provide opportunities for participants to explore key topics in-depth and gain hands-on experience using analytic tools like R and Python to carry out essential data science workflow processes, including advanced methods for machine learning and text mining. In each module, participants will also explore how these methods have been applied by researchers in STEM education contexts and work with corresponding real-world datasets from a wide range of sources such as MOOCs, student information systems, and log data from digital learning platforms.

  1. Interactive Presentations. Each module contains slide decks for two interactive presentation that provide an overview of key concepts, software packages and functions for data analysis. The first presentation focuses on a conceptual overview of key terminology, techniques, and applications (see example). The second presentation provides a short but highly structured code-along activity that demonstrates key packages and functions required for specific data analysis techniques highlighted in each unit and an exemplary research study (see example). Both presentations include prompts for discussion to check participant understanding and connect content with their personal and professional research interests. 
  2. Coding Case Studies. Case study assignments developed by the project team are interactive coding experiences that can be completed by learners independently or in small groups (see example). These activities demonstrate how key data-intensive research workflow processes (i.e., wrangling, visualizing, summarizing, modeling, and communicating data) featured in exemplary STEM education research studies are implemented in R or Python. Coding case studies also provide a holistic setting to explore important foundational LA topics integral to data analysis such as reproducible research, use of APIs, student privacy, ethical consideration, and diversity and inclusion in STEM education.
  3. Readings and Discussion. Essential readings are curated for participants to help them dive deeper into LA concepts, techniques, and applications introduced in presentation and case studies (see example). Each module includes an exemplary research article that illustrates how LA applications and/or techniques highlighted in each module (e.g., data visualization, topic modeling) have been used in STEM education contexts. These articles are also used to guide coding case studies and help connect technical skills required for advanced methods with authentic research applications. Instructors will be provided guiding questions to help them facilitate discussion among learners and assess their understanding of module content.
  4. Software Tutorials. Openly accessible software tutorials are curated for each module and are intended to help learners develop technical proficiency with essential software packages, functions, and programming syntax introduced during conceptual overviews, code-alongs, and case studies. Tutorials include, but are not limited to, available on Posit Cloud and Python and intelligent-tutor based assignments developed by UPenn that scaffold students in learning to use learning analytics methods (Aleven et al., 2017; Zhou et al., 2021).
  5. Badges & Microcredentials. Each module includes a summative assessment activity designed to help learners reflect on how the concepts and techniques introduced in each lab might apply to their own STEM education research, where they can demonstrate their technical proficiency with the analytical techniques and methods addressed in each unit. Instructors are provided with digital badges to award students upon successful completion of assessments (see example). At the instructor’s discretion, badges can be sequenced into microcredentials that can be used to certify learners’ successful demonstration and/or application of LA methods. Microcredential certificates will also be created for learners who have demonstrated their ability to consider the potential applications and challenges of learning analytics in society. Badging activities and corresponding badges developed for the LASER Institute will be expanded to include new modules created from UPenn materials.

During the Summer Workshop, broader topics related to disciplinary knowledge will be addressed at the end of each day through presentations, guest speakers and panel discussions. Speakers will consist of institute instructors, invited guests, advisory board members and past participants with topics including, but are not limited to:

  • Digital Data in Education will introduce participants to three types of digital data that frame the analytical approaches addressed by this Institute, as well as three types of educational technologies in which these data are captured and stored. Specifically, this presentation will cover structured data, unstructured text data, and network data obtained from digital learning environments, administrative data systems, and sensors and recording devices.
  • Frameworks and Workflows will introduce participants to general approaches to conceptualizing processes associated with LA, including data collection, storage, cleaning, exploring, and modeling. These frameworks and workflows will help illustrate LA’s emphasis on actionable insight to better target instructional, curricular and support resources and interventions.
  • Researcher-Practitioner Partnerships will highlight the value of interdisciplinary collaborations with educational organizations to help them learn from their own data and identify new ways to support students. This presentation will include examples from the field and discuss the conditions necessary for developing and sustaining these partnerships.
  • Legal and Ethical Issues will address considerations for researchers that are unique to working with data in these new types of STEM learning environments. Topics will include issues such as explicit and implicit bias embedded in big data and algorithms, adequately protecting data, and appropriately addressing privacy concerns.

A core component of the LASER Institute are in-person community building activities during the summer workshop and an online community of practice to provide continued professional learning, mentoring, and networking opportunities throughout the year. Ongoing activities include:

  • Facilitated discussions take place during the summer workshop and through our Slack workspace. Facilitated discussions will focus on shared problems of practice along with online community forums for topic areas such as R/Python-related Help, general announcements, and specific methods.
  • Zoom Webinars will be led by instructors, guest speakers and past participants designed to reteach and extend topics introduced at the Summer Workshop, address critical issues raised in the community, and provide deeper dives into learning analytics methods.
  • Peer review activities will be coordinated by the project team so participants can receive timely formative feedback on research and teaching products (e.g., code, analyses, presentations, manuscripts or proposals) before a more formal review by the broader academic community.
  • A resource repository consisting of both instructor and member-generated content will be hosted on GitHub and a newly developed website, which will contain all the materials necessary to both teach with, and learn from, the LASER Institute Curriculum.
  • Dr. Tiffany Barnes is a Professor of Computer Science at NC State University. Dr. Barnes has served as chair and board member of the International Educational Data Mining Society and received an NSF CAREER Award for her novel work using educational data mining to add intelligence to STEM learning environments. Dr. Barnes is co-Director for the STARS Computing Corps, a consortium of universities that engage college students in outreach, research, and service to broaden participation in computing.
  • Dr. Gregory Downing is an Assistant Professor in STEM Education at North Carolina Central University, an HBCU. Dr. Downing’s research explores equity and diversity issues within STEM education, specifically how current teaching and learning practices within the K-16 system (dis/en)able students of color and other marginalized students to/from entering STEM careers.
  • William Finzer has been developing educational software for over 30 years. He is a skilled software designer and programmer with considerable experience in classroom teaching, teacher professional development, game design, curriculum development, and research. As Senior Scientist and project lead for the Common Online Data Analysis Platform (CODAP) project at Concord Consortium, he leads design and development of a free, open source, browser-based data analysis and exploration environment adaptable to a wide variety of educational settings.
  • Nancy Rausch is a senior manager and data scientist at SAS. Nancy has been involved for many years in the design and development of SAS’s data warehouse and data management products, working closely with customers and authoring a number of papers on SAS data management products and best practice design principles for data management solutions.
  • Dr. Alyssa Wise is an Associate Professor of Learning Sciences and Educational Technology in the Steinhardt School of Culture, Education, and Human Development and the Director of LEARN, NYU’s university-wide Learning Analytics Research Network. Dr. Wise directs LEARN with the aim of making NYU a leader in data-informed teaching and learning while also generating new knowledge about how LA can promote equitable and effective education.

Applicants for the 2024 institute must have completed the requirements for a Ph.D. or Ed.D. degree by December 2021. Early-career scholars are typically under seven years after obtaining a doctoral degree; mid-career scholars are typically within their first 15 years of academic or other research-related employment.

In support of the broader goals of the Building Capacity in STEM Education Research (BCSER) program, the LASER Institute will prioritize early and mid-career scholars from underrepresented groups and faculty at minority-serving institutions. Prospective LASER Scholars will have a primary job responsibility or specific aspect of their research and teaching agenda that would benefit from participation in the LASER program. As part of the application process, prospective participants will need to articulate this connection.

Participants who will benefit most from the LASER Institute are scholars who:

  • Are currently engaged in research in STEM education contexts;
  • Need guidance on how new data sources and computational techniques can support their research;
  • Have access to a dataset or study population of interest in which they can apply learned skills;
  • Are interested in and able to teach a webinar, workshop, or course using LASER Institute curriculum materials; 
  • Can dedicate time throughout the year to continue their skill development and implement a teaching plan at their home institution; 
  • Have a basic understanding of probability and statistical analysis;
  • Have experience using statistical software programs for data cleaning and analysis (e.g. R, Python, Stata, SAS).

Participants in the LASER Institute are expected to commit to the following:

  • Attend the in-person Summer Institute from July 22-26 (virtual options are not available);
  • Attend the virtual monthly check-ins that will be the third Thursday of each month from August – December; and
  • Develop and implement an instruction plan for teaching colleagues or students at their home institutions using LASER Institute curriculum materials.

2023 Cohort

NameRoleInstitution
Megan AthaAssistant ProfessorFlorida Gulf Coast University
Yu BaoAssistant ProfessorJames Madison University
Le Shornn BenjaminAmerican Society of Engineering Education Post Doctoral ResearcherUniversity of Houston/American Society of Engineering Education
Rogers BhalalusesaLecturerThe Open University of Tanzania
Emily BonemAssistant Director, Scholarship of Teaching & LearningPurdue University
Daniela Castellanos ReyesIncoming Assistant ProfessorNorth Carolina State University
Xiaowen ChenAssistant ProfessorWestern Kentucky University
Deborah CockerhamClinical Assistant ProfessorUniversity of North Texas
Liliana Donchik BelkinSenior Lecturer in EducationUniversity of Roehampton
Suzhen DuanAssistant ProfessorTowson University
AJ EdsonResearch Assistant Professor of Mathematics EducationMichigan State University
Fei GaoProfessorBowling Green State University
Taren GoingPostdoctoral Research AssociateMichigan State University
Angela HemingwayEducation AdvisorT-Mobile
Jianlin HouSpecialistThe School District of Palm Beach County
Itauma ItaumaDivision Chair & Assistant ProfessorNorthwood University
Hyeon-Ah KangAssistant ProfessorUniversity of Texas at Austin
Victor LawAssociate Professor and Program DirectorUniversity Of New Mexico
Jin LeeAssistant ProfessorUniversity of Louisiana at Lafayette
Seung LeeAssistant Professor of EducationPepperdine University
Alfredo LeonAssistant ProfessorMiami Dade College
Cynthia LimaAssistant Professor of STEM EducationUniversity of Texas at San Antonio
Jin LiuClinical Associate ProfessorUniversity of South Carolina
Peng LuAssistant ProfessorUniversity of Georgia
Catherine ManlyPostdoctoral ResearcherCity University of New York
Praveen MeduriAssistant ProfessorCalifornia State University
Nadia MillsAssociate Professor of MathematicsUniversity of the Virgin Islands
Matthew MorenoPostdoctoral ResearcherMcGill University
Ceren OcakAssistant Professor of Instructional TechnologyGeorgia Southern University
Erin OttmarAssociate Professor of Learning SciencesWorcester Polytechnic Institute
Eunsung ParkAssistant ProfessorTennessee Tech University
Fabio Andres Parra MartinezPostdoctoral Research FellowUniversity of Arkansas
Yingxiao QianClinical Assistant ProfessorUniversity of South Carolina
Tacey RodgersDirector, Assessment, Research, and EvaluationSolano County Office of Education
Arthur SikoraAssistant Professor of ChemistryNova Southeastern University
Vipin VermaAssistant Research ScientistArizona State University
Ning WangResearch AssociateThe University of Texas at Dallas
Korah WileyLearning ScientistDigital Promise
Mia WilliamsAssistant ProfessorUniversity of Wyoming
Fan XuSenior Learning DesignerThe Ohio State University
Zhen XuPostdoc Research AssociateUniversity of North Carolina at Chapel Hill
Clement G. YedjouAssociate Professor of BiologyFlorida A & M University
Ji Hyun YuAssistant ProfessorUniversity of North Texas
Dake ZhangAssociate ProfessorRutgers, the State University of New Jersey
Enyu ZhouSenior Research AnalystCouncil of Graduate Schools

2022 Cohort

NameJob TitleInstitution
Brittany AndersonAssistant Professor, Urban EducationUniversity of North Carolina at Charlotte
Alexandria ArdissoneAssistant ScientistUniversity of Florida
Tracy ArnerPostdoctoral Research ScholarArizona State University
Catherine BlatAssistant Dean for Student ExperiencesEngineering/UNC Charlotte
Irina CainAssociate LecturerUniversity of Massachusetts Boston
Deborah CockerhamClinical Assistant ProfessorUniversity of North Texas
Michael DaleyAssociate Professor of EducationUniversity of Rochester
Kristi DonaldsonPartner Relations ManagerThe Learning Partnership
Krista DulanyResearch Assistant ScientistUniversity of Florida
Mona EmaraResearch Fellow, Lecturer of Edu. PsychologyUniversity of Vienna, Austria. Damanhour University, Egypt
Lori FootePostdoctoral ResearcherUniversity of Cincinnati
Liz FrechetteSenior Research and Policy AssociateUniversity of Oklahoma
Peng HePostdoctoral Research AssociateMichigan State University
Susan HibbardSenior Director of Learning Science and PsychometricsBlueprint Test Preparation
Ahmed IbrahimSenior Education Research ConsultantJohns Hopkins University
Justina Rodriguez JacksonResearch ScientistGeorgia Institute of Technology
Jillian LauerPostdoctoral FellowNew York University
Mark LaVeniaData StrategistEdReports
Sungwoong LeeAssistant ProfessorUniversity of West Georgia
Kathryn LeechAssistant ProfessorUniversity of North Carolina at Chapel Hill
Alex LishinskiResearcherUniversity of Tennessee-Knoxville
Kathryn McCarthyAssistant ProfessorGeorgia State University
Veronica MinayaSenior Research AssociateTeachers College at Columbia University
Nadun Kulasekera MudiyanselageAssistant ProfessorAppalachian State University
Jennifer OsterhageAssistant Professor of BiologyUniversity of Kentucky
Tom PennistonCoordinator of Learning Analytics
University of Maryland, Baltimore County
Shalaunda ReevesAssistant Professor in STEM EducationUniversity of Tennessee
Lisa RidgleyResearch AssociateJacobs Institute for Innovation in Education/University of San Diego
Margarita SafronovaAssociate Director, Academic CoordinatorUniversity of California, Santa Barbara
Guan SawAssociate ProfessorClaremont Graduate University
Celia ScottAssistant Dean of Assessment and Associate ProfessorUniversity of North Texas Health Science Center
Jung Mi ScoulasAssistant ProfessorUniversity of Illinois Chicago
Jenay SermonSenior Director Applied Learning Science / Education PT FacultyKenzie Academy from Southern New Hampshire University / Florida A&M University
Damji StrattonE-Learning Research & Data Analyst SpecialistMissouri Online, University of Missouri System
Robert TalbertProfessor of Mathematics and Presidential Fellow for the Advancement of LearningGrand Valley State University
Ashley VaughnAssociate Director/Assistant Professor of PracticeNorthern Kentucky University
Emily WeigelSenior Academic ProfessionalGeorgia Institute of Technology
Melinda WhitfordResearch AnalystUniversity at Buffalo
Rachel WongAssistant Professor of Educational PsychologyTexas A&M University-Commerce
Kim WrightAssistant Research ScientistTexas A&M University
Cristina ZepedaPostdoctoral Research AssociateWashington University in St. Louis
Ya Zhang
Assistant Professor

Western Michigan University
Meina Zhu
Assistant Professor
Wayne State University

2021 Cohort

NameRoleInstitution
Mete AkcaogluAssociate ProfessorGeorgia Southern University
Zina AlaswadAssistant Professor of Interior DesignTexas State University, School of Family and Consumer Sciences
Tawannah G. AllenAssociate Professor of Educational LeadershipStout School of Education, High Point University
Rebecca Y. BayeckCLIR Postdoctoral FellowSchomburg Center for Research in Black Culture
Laurie O. CampbellAssistant ProfessorUniversity of Central Florida
Jacqueline G. CavazosPostdoctoral ScholarUniversity of California, Irvine
Shonn Sheng-Lun ChengAssistant ProfessorSam Houston State University
MeganClaire CoglianoPostdoctoral FellowUniversity of Nevada Las Vegas
Yvonne EarnshawAssistant Professor and Program Coordinator of Instructional Design and DevelopmentUniversity of Alabama at Birmingham
Carlton J. FongAssistant ProfessorTexas State University
Hoda Harti
Instructor, Educational Technology
Northern Arizona Univesity
Yu-Ping HsuAssistant ProfessorWestern Illinois University
Diane IgocheAssistant ProfessorRobert Morris University
Carrie JonesScience TeacherWake County Schools
Yeo-eun KimPostdoctoral FellowWashington University in St. Louis
T.K. KuykendallAdjunct/Coordinator of DataCleveland State University/Lakewood City Schools
Yanju LiData Administrator Lead
Georgia State University
Lin LinProfessorUniversity of North Texas
Peggy LisenbeeAssociate Professor of Early Childhood Education
College of Professional Education, Texas Woman’s University
Nikki G. LobczowskiPostdoctoral AssociateUniversity of Pittsburgh
Chrishele MarshallProgram Associate I, Implementation and Training (Assessment)Detroit Public Schools Community District
Tara MasonAssisant Professor of Inclusive EducationWestern Colorado University
Becky Matz
Research Scientist, Center for Academic Innovation
University of Michigan
T.J. McKennaLecturerBoston University
Vida MingoSenior LecturerColumbia College (SC)
Angela MurilloAssistant ProfessorSchool of Informatics and Computing, Indiana University-Purdue University Indianapolis
Jeffrey T. OlimpoAssistant Professor in Biological SciencesThe University of Texas at El Paso
Patricia Ortega-ChasiAssistant ProfessorUniversidad del Azuay
Mihwa ParkAssistant ProfessorTexas Tech University
Kim Pinckney-LewisHR StrategistNational Security Agency
Tiffany RomanAssistant Professor of Instructional TechnologySchool of Instructional Technology and Innovation, Kennesaw State University
Teomara (Teya) RutherfordAssistant Professor, Learning SciencesUniversity of Delaware
Jaime SabelAssistant ProfessorUniversity of Memphis
Justice T. WalkerAssistant Professor of STEM EducationThe University of Texas at El Paso
Nadia Monrose MillsAssistant Professor of MathematicsUniversity of the Virgin Islands

Applications for the LASER Institute will be available soon. To be placed on our mailing list and notified when applications are open, please complete the following form:

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LASER Institute Builds Community of Scholars

Beyond the many positive effects that have come out of this institute, from helping scholars to seek and secure grant funding to facilitating collaboration on journal articles, LASER Institute scholars have gained connections that have significantly impacted their professional lives. 

Field trip to NC State Dairy Fram for some Howling Cow ice cream

For me, it kind of changed my identity. Before I felt, ‘I don’t do learning analytics,’ and I couldn’t participate in these conversations and think that way. It’s helped me identify more as a scholar in that area, which is the area I wanted to go in.

Dr. Nikki Lobczowski

Assistant Professor of Learning Sciences at McGill University 

Funders

National Science Foundation

Partners

Current Project Team

Past Team Members