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 (ECR: BCSER).

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 Florida and the University of Tennessee, Knoxville.

Goals

  • 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, RStudio, GitHub and other tools used for collaboration, reproducible research and computational analysis.
  • 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

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, research planning sessions, and guest speakers.
  2. Online Community: An online community of practice for ongoing networking and support throughout the year.

The weeklong intensive program will be conducted at North Carolina State University in Raleigh, North Carolina.

The summer workshop will consist of four half-day sessions each week, with one day each week reserved for planning, mentoring, and independent work. Participants will receive a $1,500 stipend for their participation in institute activities.

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/issues addressed by LA and associated analytical approaches/applications;
  • Identify relevant and appropriate STEM educational data sources for computational analyses;
  • Apply computational techniques (e.g. machine learning and text mining) using R and RStudio 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 collaborative research agenda in STEM education that seeks to address challenges in STEM education from a Learning Analytics lens.

Learning Labs during the summer program are designed to provide participants hands-on experience with R to apply learning techniques, including text mining, data visualization, social network analysis and machine learning. Students will gain hands-on experience in analyzing educational data from STEM contexts, preparing them to solve practical problems in cutting-edge STEM education research and practice. Data for learning labs will come from Friday Institute Online Professional Learning courses (in particular Technology and Math Educators), social media posts, ASSISTments math practice tool, the CODAP data exploration tool, and other digital teaching and learning platforms used in STEM education. Curriculum for the LASER Learning Labs will address the following areas:

  1. Introduction to Learning Analytics is designed to provide participants an overview of the field of Learning Analytics and prepare students for using tidy data principles and producing replicable research using R, RStudio and GitHub.
  2. Visualizing STEM Learning will focus on the use of R packages such as ggplot2 and shiny for plotting learner data, creating attractive and informative charts, and developing interactive web apps and dashboards.
  3. Machine Learning in STEM Ed will introduce researchers to applications of 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 in Education will provide 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 and classification.
  5. Analyzing Learning Networks will introduce students 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.

In support of the NSF’s broad goal of building individuals’ capacity to conduct high-quality STEM education research, participants will receive daily support during the Summer Session in developing a research topic that could be investigated further. To maximize time, presentations and panels will be combined with a working lunch. To help provide a personalized learning experience, participants will be initially put into one of the two following groups based on their responses to the needs assessment administered prior to the Institute:

  1. Technical Assistance will be aimed at participants who have a solid research idea but need support with specific techniques or packages in R. Technical assistance needs may have commonalities between participants or may be participant specific. To determine the most effective response to participant needs, the participants will respond to an online poll each day that allows for up/down voting. In real-time, participants will see technical issues that others are having and may vote issues up/down. This will allow for subgrouping of participants and also identify common problems that can be addressed.
  2. Research Planning is for participants that need help developing a research idea. For this group, we will make use of low stakes writing activities (Bean, 2011) followed by peer sharing. Our goal is for these participants to have a research idea and plan of action by the end of the week.

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 will be an online community of practice to provide follow-up support to the Summer Workshop and continued professional learning, mentoring, and networking opportunities throughout the year. The LASER online community will include monthly activities and regularly updated resources.

  • Facilitated discussions will be hosted on our learning and social media platforms. Facilitated discussions will focus on shared problems of practice such as reproducible research along with community forums for topic areas such as R-related Help, general announcements, specific methods.
  • Zoom Webinars will be led by instructors, guest speakers and past participants designed to extend topics introduced at the Summer , 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 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 in our Professional Learning and Collaborative Environment (PLACE) platform and accessible through modern tools such as GitHub to model and support participant engagement with modeling best practices in learning analytics research.

  • 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.

2022 Cohort

Name Job Title Institution
Brittany Anderson Assistant Professor, Urban Education University of North Carolina at Charlotte
Alexandria Ardissone Assistant Scientist University of Florida
Tracy Arner Postdoctoral Research Scholar Arizona State University
Catherine Blat Assistant Dean for Student Experiences Engineering/UNC Charlotte
Irina Cain Associate Lecturer University of Massachusetts Boston
Deborah Cockerham Clinical Assistant Professor University of North Texas
Michael Daley Associate Professor of Education University of Rochester
Kristi Donaldson Partner Relations Manager The Learning Partnership
Krista Dulany Research Assistant Scientist University of Florida
Mona Emara Research Fellow, Lecturer of Edu. Psychology University of Vienna, Austria. Damanhour University, Egypt
Lori Foote Postdoctoral Researcher University of Cincinnati
Liz Frechette Senior Research and Policy Associate University of Oklahoma
Peng He Postdoctoral Research Associate Michigan State University
Susan Hibbard Senior Director of Learning Science and Psychometrics Blueprint Test Preparation
Ahmed Ibrahim Senior Education Research Consultant Johns Hopkins University
Justina Rodriguez Jackson Research Scientist Georgia Institute of Technology
Jillian Lauer Postdoctoral Fellow New York University
Mark LaVenia Data Strategist EdReports
Sungwoong Lee Assistant Professor University of West Georgia
Kathryn Leech Assistant Professor University of North Carolina at Chapel Hill
Alex Lishinski Researcher University of Tennessee-Knoxville
Kathryn McCarthy Assistant Professor Georgia State University
Veronica Minaya Senior Research Associate Teachers College at Columbia University
Nadun Kulasekera Mudiyanselage Assistant Professor Appalachian State University
Jennifer Osterhage Assistant Professor of Biology University of Kentucky
Tom Penniston Coordinator of Learning Analytics University of Maryland, Baltimore County
Shalaunda Reeves Assistant Professor in STEM Education University of Tennessee
Lisa Ridgley Research Associate Jacobs Institute for Innovation in Education/University of San Diego
Margarita Safronova Associate Director, Academic Coordinator University of California, Santa Barbara
Guan Saw Associate Professor Claremont Graduate University
Celia Scott Assistant Dean of Assessment and Associate Professor University of North Texas Health Science Center
Jung Mi Scoulas Assistant Professor University of Illinois Chicago
Jenay Sermon Senior Director Applied Learning Science / Education PT Faculty Kenzie Academy from Southern New Hampshire University / Florida A&M University
Damji Stratton E-Learning Research & Data Analyst Specialist Missouri Online, University of Missouri System
Robert Talbert Professor of Mathematics and Presidential Fellow for the Advancement of Learning Grand Valley State University
Ashley Vaughn Associate Director/Assistant Professor of Practice Northern Kentucky University
Emily Weigel Senior Academic Professional Georgia Institute of Technology
Melinda Whitford Research Analyst University at Buffalo
Rachel Wong Assistant Professor of Educational Psychology Texas A&M University-Commerce
Kim Wright Assistant Research Scientist Texas A&M University
Cristina Zepeda Postdoctoral Research Associate Washington University in St. Louis
Ya Zhang Assistant Professor Western Michigan University
Meina Zhu Assistant Professor Wayne State University

2021 Cohort

Name Job Title Institution
Mete Akcaoglu Associate Professor Georgia Southern University
Zina Alaswad Assistant Professor of Interior Design Texas State University, School of Family and Consumer Sciences
Tawannah G. Allen Associate Professor of Educational Leadership Stout School of Education, High Point University
Rebecca Y. Bayeck CLIR Postdoctoral Fellow Schomburg Center for Research in Black Culture
Laurie O. Campbell Assistant Professor University of Central Florida
Jacqueline G. Cavazos Postdoctoral Scholar University of California, Irvine
Shonn Sheng-Lun Cheng Assistant Professor Sam Houston State University
MeganClaire Cogliano Postdoctoral Fellow University of Nevada Las Vegas
Yvonne Earnshaw Assistant Professor and Program Coordinator of Instructional Design and Development University of Alabama at Birmingham
Carlton J. Fong Assistant Professor Texas State University
Hoda Harti Instructor, Educational Technology Northern Arizona Univesity
Yu-Ping Hsu Assistant Professor Western Illinois University
Diane Igoche Associate Professor Robert Morris University
Carrie Jones Science Teacher Wake County Schools
Yeo-eun Kim Postdoctoral Fellow Washington University in St. Louis
T.K. Kuykendall Adjunct/Coordinator of Data Cleveland State University/Lakewood City Schools
Yanju Li Data Administrator Lead Georgia State University
Lin Lin Professor University of North Texas
Peggy Lisenbee Associate Professor of Early Childhood Education College of Professional Education, Texas Woman’s University
Nikki G. Lobczowski Postdoctoral Associate University of Pittsburgh
Chrishele Marshall Program Associate I, Implementation and Training (Assessment) Detroit Public Schools Community District
Tara Mason Assisant Professor of Inclusive Education Western Colorado University
Becky Matz Research Scientist, Center for Academic Innovation University of Michigan
T.J. McKenna Lecturer Boston University
Vida Mingo Senior Lecturer Columbia College(SC)
Angela Murillo Assistant Professor School of Informatics and Computing, Indiana University-Purdue University Indianapolis
Jeffrey T. Olimpo Assistant Professor in Biological Sciences The University of Texas at El Paso
Patricia Ortega-Chasi Associate Professor Universidad del Azuay
Mihwa Park Assistant Professor Texas Tech University
Kim Pinckney-Lewis HR Strategist National Security Agency
Tiffany Roman Assistant Professor of Instructional Technology School of Instructional Technology and Innovation, Kennesaw State University
Teomara (Teya) Rutherford Assistant Professor, Learning Sciences University of Delaware
Jaime Sabel Assistant Professor University of Memphis
Justice T. Walker Assistant Professor of STEM Education The University of Texas at El Paso
Nadia Monrose Mills Assistant Professor of Mathematics University of the Virgin Islands

Eligibility

Applicants for the 2022 institute must have completed the requirements for a Ph.D. or Ed.D. degree by December 2019. 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.

Applications

The LASER Institute will kick off with a week-long summer workshop that will run from July 11-15 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.

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

  • Are currently engaged in research in STEM education contexts;
  • Are relatively new to data sources and techniques used in Learning Analytics research;
  • Have a STEM Ed research question or topic in mind;
  • Need guidance on how new data sources and techniques can support their research;
  • Have access to a dataset or study population of interest;
  • Have a basic understanding of probability and statistical analysis;
  • Have experience using statistical software programs for data cleaning and analysis (e.g. SPSS, Stata, SAS, R);
  • Have the time necessary to devote to developing their skills and supporting a research effort in the upcoming year (e.g. manuscript submission, grant submission, conference presentation).

Applications for the 2022 LASER Institute Cohort are now closed. If you would like to be notified when 2023 applications begin, please sign up for the mailing list via the following form.

LASER Institute Interest Form