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, DRL-2321128, and DRL-2321129). LASER curriculum materials and instructional resources can be found on our companion site at: go.ncsu.edu/laser-beam.
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.
Learn with LASER!
Click below to stay updated on LASER Institute news and ways you can participate in future events, webinars, and programs.
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:
Summer Workshop: An intensive 5-day program consisting of learning labs, guest speakers, planning sessions, and community-building activities.
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:
The primary goal of the LASER Institute is to increase the number of education researchers capable of leveraging advanced research methods to understand and improve student learning. To accomplish this goal, participants in the LASER Institute will:
Learn from knowledgeable instructors with deep expertise in Learning Analytics and associated research methods.
Teach colleagues or students at their home institutions using curriculum materials developed for the LASER Institute.
Program activities and LASER Institute curriculum materials are designed to prepare scholars with the knowledge, skills and resources necessary to apply collaborative, data-intensive research methods to understand and improve student learning and the contexts in which learning occurs. By the end of the program, participants will be able to:
Describe STEM education research 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 Summer Workshop is an intensive 5-day summer training program taught by faculty and staff from NC State, Penn, UT-Knoxville and includes invited presentations from Advisory Board members. Each workshop takes place in July with pre-workshop preparation for participants beginning earlier in the spring. To attend to the needs of participants with varying degrees of expertise in LA, the workshop provides both a beginner and an advanced track to support faculty development, with approximately half of the participants in each track.
Pre-Institute Preparation. Prior to the Summer Institute, participants will complete a needs assessment to identify their teaching interests, experience with software packages, and skills and knowledge in relation to LA and advanced methods. This assessment will be used to help guide the content, structure and sequence of their 5-day professional development and assign tutorials to completed prior to the workshop.
Module Sessions. Module sessions are conducted in small groups and differentiated for participants in the beginner and the advanced track based upon the needs assessment administered prior to the Summer Institute. These sessions will focus on the instructional materials from which participants will both learn from and use to train learners at their home institutions. Additionally, module sessions will be offered in an online format through workshops in the fall.
Pedagogy Sessions. These whole group sessions designed to support participants in adapting materials for use in their own instructional programs. Sessions focus on the nuts and bolts of teaching the LASER curriculum, including topics such as tools and approaches for facilitating in-person and online discussion, assessment and grading of assignments, and logistics for instructional delivery.
Design Sessions. Each day includes whole and small group activities facilitated by the project to assist participants in designing a customized instructional plan at their home institutions. These session focus on how to 1) incorporate curriculum resources into their own contexts, 2) select relevant modules and activities, effective teaching methods (pedagogy), and suitable technology, and 3) tailor instruction to address the unique needs and preferences of their learners.
Community Building. Each day includes identity-affirming activities to help participants learn more about each other and create a sense of community. By sharing their backgrounds, interests, and experiences, participants identify personal and professional commonalities and differences. This will create a space for participants to learn from each other and build on each other’s strengths.
Ongoing support is provided to participants during the academic year to continue their professional learning and ensure they can successfully carry out instructional plans at their home institutions. The project team provides a range of activities designed to support participants and inform curriculum refinement throughout the year. These following are guided by findings from our prior research found to be strongly associated with successful online communities:
Monthly Check-Ins. Throughout fall and spring, the project team will facilitate formal monthly check-ins with participants on progress made towards implementing instructional plans developed duringthe Summer Workshop. The check-ins will also be used to gather feedback on curricular modules used by instructors. Guest speakers from our advisory board and other invited guests will also lead sessions on LA topics during check-ins. These sessions will be informed by the community, as well as more specialized topics in advanced methods.
Virtual Module Sessions. These session are offered several times each month so participants can learn about research methods they were unable to experience during the Summer Institute, as well to model instructional use of the modules in a fully online context. The workshops are led by members of the project team as well as by past LASER Scholars.
Asynchronous Activities. Facilitated discussion channels and informal Q&As are hosted on our Slack workspace, which includes both current and past participants from prior LASER Institute cohorts. Discussions focus on shared problems of practice such as adapting instructional modules to local contexts or working with students who have limited programming experience, as well as topics related to module content such as R packages, conceptual overviews, and essential readings.
Resource Repositories. A key deliverable of this grant is a freely available website that houses all the curriculum materials needed to teach, and learn from, the LASER curriculum. These materials consist of both project team and member-generated content hosted on GitHub and a new curriculum website currently under development. These website include materials for each module as well as supporting materials for instructors such as pedagogical tips, information on computing infrastructure, technology stack, and logistics for set up.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
Dr. Collin Lynch, Associate Professor in the Department of Computer Science at North Carolina State University, develops robust intelligent tutoring systems for ill-defined domains such as scientific writing, law, and software development.
Dr. Xavier Ochoa, Assistant Professor of Learning Analytics at New York University, uses recent advances from learning analytics and smart sensors to build and study tools that augment the awareness, self-reflection, sense- and decision-making of students and instructors.
Dr. JuliaRutledge, Director of the MS in Educational Psychology – Learning Analytics program at University of Wisconsin-Madison, oversees course development in her program and teaches the introductory course. Her research interests include social and emotional learning (SEL) measurement and personalized learning environments.
Dr. George Siemens, Professor and Director of the Centre for Change and Complexity in Learning at the University of South Australia, is founding President of the Society for Learning Analytics Research and pioneer of the massive open online courses (MOOC) movement.
Dr. Yianna Vovides, Professor and Director of Learning Design and Research at the Center for New Designs in Learning and Scholarship (CNDLS), oversees her university’s online learning development efforts.
2021-2023
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 June 2023. 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 virtual monthly check-ins that will be the third Thursday of each month from August – December; and
Develop and implement an instructional plan for teachingstudents or colleagues at their home institutions using LASER Institute curriculum materials.
Magliano, J. P., Flynn, L., Feller, D. P., McCarthy, K. S., McNamara, D. S., & Allen, L. K. (2022). Leveraging a multidimensional linguistic analysis of constructed responses produced by college readers. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2022.936162
McCarthy, K. S. & Yan, E. F. (under review). Supporting reading comprehension and learning: Policy considerations in the age of AI. Submitted to Policy Insights from the Brain and Behavioral Sciences.
Moore, R. L., Jiang, S., & Abramowitz, B. (2023). What would the matrix do?: A systematic review of K-12 AI learning contexts and learner-interface interactions. Journal of research on Technology in Education, 55(1), 7-20.
Penniston, T. (2023). Toward a New Paradigm: Learning Analytics 2.0. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2023. Lecture Notes in Computer Science, vol 14044. Springer, Cham. https://doi.org/10.1007/978-3-031-34735-1_11
Qian, Y. (under review). Understanding college students’ cognitive engagement in online STEM courses: From the ICAP perspectives, International Journal of STEM Education.
Wang, C. & Zhu, M., (under revision) Trends and patterns in K-12 computer science education: Data analysis from Twitter. Educational Media International.
Williams, M. K. & Mercier, A. (2024). Social networks of early childhood learners engaged in collaborative problem-solving and creative design during children’s engineering. [Manuscript in Progress].
Williams, M. K. (2024). Creating productive academic writers through a fellowship program for new faculty. [Manuscript in Progress.]
Yu, J.H. (in preparation). Decoding dialogue dynamics: A topic modeling and sentiment analysis of MOOC discussion forums. To be submitted to the Journal of Research on Technology in Education.
Yu, J.H. (in preparation). Emotional landscapes of learning: Sentiment dynamics in MOOC course feedback across time. To be submitted to the Internet and Higher Education.
Zhu, M., & Wang, C (under review). A Systematic Review of Artificial intelligence for Language Learning: Leveraging data mining. Computer Assisted Language Learning.
Book Chapters
Fiacco, J, Jiang, S., Adamson, D., & Rosé, C. (in press). Learning Analytics. In International encyclopedia of education (4th ed.).
Hibbard, S., McClure, J., & Kellogg, S. (2024). Embracing Learning Analytics in Health Professions Education. New Directions in Education. https://doi.org/10.1002/tl.20597
Grants
Gholson, D., Borron, A., Lu, P., (Co-PI) et al. (05/31/2024 – 06/01/2029) Advancing irrigation technology adoption across the Southeast through multi-layer training, farmer incentives, and better understanding of farmer’s needs. The proposal has submitted to USDA Conservation Innovation Grants ($5,000,000)
McCarthy, K. S. (PI) & Jaeger-Berena, A. J. (FY2023, revising for FY2024). Toward Individualized Learning: Capturing the Complexity of Generative Learning Strategy Supports Using a Digital Platform. Submitted to IES.
P. Lu (PI), A. Lamm, A. Borron, J.Y. Park, J. Gratzek, H.J. Ye, J. Deustsch. (2024/01/01-2025/06/30). Reimagining Sustainability: Pioneering Upcycled Foods in Circular Food Systems. The proposal has submitted to University of Georgia 2023 Presidential Interdisciplinary Seed Grant ($ 60,000)
Zhang, D. (PI), M. Li, D. Dong. Automated Classification of Student Problem-Solving Style in Representing Fractions with a Number Line. Funding provided by ASSISTments.
Conference Presentations and Proceedings
Akcaoglu, M., & Ocak, C. (Accepted). Analyzing text data using computational methods. Proposal accepted for presentation at the Georgia Southern University COE Mini-Conference, December 5, 2023, Statesboro, Georgia.
Banawan, M., McCarthy, K. S., Allen, L. K., Magliano, J. P., & McNamara, D. S. (2023, Apr. 14). Sourcing Strategy Use in Constructed Responses within Multiple Document Integrated Reading and Writing Tasks. [Poster Presentation]. Annual Meeting of the American Educational Research Association (AERA). Chicago, IL.
Cockerham, D., & Kaplan-Rakowski, R. (2024, October 19-23). Exploring the Role of Sound in Virtual Reality. [Submitted]. Association for Educational Communications and Technology Conference. Kansas City, MO.
Flynn, L., Magliano, J. P., Allen, L. K., McCarthy, K. S., & McNamara, D. S. (2023, Apr. 14). Relations between Cohesion in Constructed Responses and Individual Differences in Reading Literacy Skills. [Poster Presentation]. Annual Meeting of the American Educational Research Association (AERA). Chicago, IL.
Han, S. & Castellanos-Reyes, D. (2024) Tracing The Reviewers: A cluster analysis of MOOC engagement patterns using sequential pattern mining. LAK Short Paper.
Hinze, S. R. & McCarthy, K. S. (2023, Nov. 18). Effects of Self-Explanation and Explanatory Retrieval Practice on Immediate Test Performance. [Poster presentation]. Annual Meeting of the Psychonomics Society, San Francisco, CA.
Hong, D., Feng, C., Paul, K., Zou, X., Hmelo-Silver, C., Lee, S., Wang, T., Farnsworth, K. Glazewski, K., Mott, B. & Lester, J. (2024, March 18-22). Orchestration Assistant: A Real-time Teacher Guidance Tool.
Itauma, I., Roberston, A., Fuda-Daddio, J., Komaroff, E. (2024). Factors Predicting Mathematics and Science Motivation among Minority Female High School Students. American Educational Research Association (AERA) Annual Meeting, 2024 (Accepted).
Itauma, I. (2024). Unveiling the Voices of Students: A Text Analysis of Machine Learning Course Feedback. Buffalo, New York, USA: International Society of the Learning Sciences. https://2024.isls.org/ (Submitted).
Iwatani, E., Leones, T., & Wiley, K. (2024, April 11-14). Essential Components of Discussion-Based Lessons in World History: Emergence of a New Framework [Paper Presentation]. AERA 2024 Annual Meeting, Philadelphia, PA.
McCarthy, K. S., Hinze, S. R., Dahl, A. C., Phillips, A., & Malloy, J. (2023, Aug. 4). Combining Learning Strategies: Effects of Explanation on Retrieval and Comprehension. [Poster presentation]. Annual Meeting of the American Psychological Association, Washington, D. C.
McClure, J., Mushi, D, Jiang, S. & Kellogg, S. (2023). The state of teaching about algorithmic bias and fairness in Learning Analytics programs. In proceedings of the 13th International Learning Analytics and Knowledge Conference, March 13–17, 2023, Arlington, TX.
Ocak, C., Akcaoglu, M., & Caskurlu, S. (Under Review). Exploring in-service teachers’ attitudes toward computational thinking in online learning settings: A sentiment analysis. Proceeding under review for publication in the Proceedings of the 18th International Conference of the Learning Sciences – ICLS 2024, Buffalo, New York.
Ocak, C., & Akcaoglu, M. (In Preparation). In-service teachers’ motivations to teach computer science: Topic modeling. Manuscript in preparation for the 29th annual ACM conference on Innovation and Technology in Computer Science Education (ITiCSE).
Pradham, S., Gurung, A., & Ottmar, E. (submitted). Gamification and Deadending: Unpacking Performance Impacts in Algebraic Learning. The 14th International Learning Analytics and Knowledge Conference. Kyoto, Japan.
Pradham, S., Lee, J., Egorova, A., Jerusal, J., & Ottmar, E. (submitted). An Application of Data Mining Methods on In-Game Behaviors: A Replication-Extension Study. ISLS Annual Meeting., Buffalo, NY.
Qian, Y. (under review). Understanding college students’ cognitive engagement in online STEM courses: From the ICAP perspectives, International Journal of STEM Education.
Rutherford, T., & Fong C. J. (2024, August). Flocking together? Twitter networks of educational psychologists versus learning scientists. [Poster presentation]. Annual Convention of the American Psychological Association, Seattle, WA.
Yu, H. & Bohlig, E. M. (2023, November). Examining the Relationship between Basic Needs Insecurity and Student Engagement at Community Colleges. Association for the Study of Higher Education. Minneapolis, MN. [Accepted and Presented]
Yu, H. & Bohlig, E. M. (2023, April). The Longitudinal Association between Institutional Expenditure and Credential Completion at Public Two-Year Colleges. Division J – Section 2a: College Student Access, Trajectories, and Transitions. American Education Research Association.
Yu, J.H. (2023). A systematic review of natural language processing applications in personalized learning: Using latent Dirichlet allocation techniques. Paper presented at the AECT 2023 Conference.
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.
