LASER Institute

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

2021 Cohort