LASER Institute

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:  

1. Learn from knowledgeable instructors with deep expertise in Learning Analytics and associated research methods. 
2. 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 during the 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.

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 inclusive practices 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.

• 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.
• 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.
• 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.
• 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.

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:

2024-2026

• 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. 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. Julia Rutledge, 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 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.
• 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.
• 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.

2024 Cohort

2023 Cohort

2022 Cohort

2021 Cohort

Applicants for the 2025 institute must have completed the requirements for a Ph.D. or Ed.D. degree by June 2024. 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 14-18 (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 teaching students or colleagues at their home institutions using LASER Institute curriculum materials.

Conference Presentations and Proceedings (Accepted, Presented, or Submitted)

American Educational Research Association (AERA) Presentations (2023–2025):

1. Banawan, M., McCarthy, K. S., Allen, L. K., Magliano, J. P., & McNamara, D. S. (2023, April 14). Sourcing strategy use in constructed responses within multiple document integrated reading and writing tasks. Annual Meeting of AERA, Chicago, IL.
2. Flynn, L., Magliano, J. P., Allen, L. K., McCarthy, K. S., & McNamara, D. S. (2023, April 14). Relations between cohesion in constructed responses and individual differences in reading literacy skills. Annual Meeting of AERA, Chicago, IL.
3. Greene, M. D., Broda, M., Chen, C-C., Chang, C.-N., Wang, T.-W., Joy, J., & Xia, Y. (2025, April-forthcoming). Zero-shot learning and support vector machine methods for automated abstract screening in scoping reviews. Conference presentation at the 2025 American Educational Research Association (AERA) Annual Conference, Denver, CO
4. Itauma, I., Roberston, A., Fuda-Daddio, J., & Komaroff, E. (2024). Factors predicting mathematics and science motivation among minority female high school students. Annual Meeting of AERA, Philadelphia, PA.
5. Iwatani, E., Leones, T., & Wiley, K. (2024, April 11–14). Essential components of discussion-based lessons in world history: Emergence of a new framework. AERA Annual Meeting, Philadelphia, PA.
6. Kellogg, S., McClure, J., Bodong, C., Poquet, S. (2023). An Introduction to Social Network Analysis and Education Research: Core Concepts and Applications with R. American Education Research Association Virtual Research Learning Series. 
7. Liu, J., Chezan, L., Zhao, Y., & Hood, S. (2024). The QOLASD-C3 for children with autism: Utilizing network analysis to identify core items of the quality of life. Annual Meeting of AERA, Philadelphia, PA.
8. Olesova, L., Sadaf, A., & Choi, H. (2024, April 11-14). Visualizing interaction patterns within Practical Inquiry Model (PIM) through Social Network Analysis [Poster presentation]. AERA Annual Conference, Philadelphia, PA.
9. Zhu, M., Xu, L., & Ericson, B. (2025, April). Large language models for computer programming education: A systematic review of empirical study. AERA Annual Meeting, Denver, CO.
10. Zhu, M., & Sari, A. (2025, April). A systematic review of empirical research on artificial intelligence for self-regulated learning. AERA Annual Meeting, Denver, CO.

Association for Educational Communications and Technology (AECT):

11. Cockerham, D. & Kaplan-Rakowski, R. (2024, October). Exploring the role of sound in virtual reality. AECT Conference, Kansas City, MO.
12. Yu, J.H. (2023). A systematic review of natural language processing applications in personalized learning: Using latent Dirichlet allocation techniques. AECT 2023 Conference.

Association for Psychological Science (APS), Psychonomics Society, APA:

13. Hinze, S. R. & McCarthy, K. S. (2023, Nov. 18). Effects of self-explanation and explanatory retrieval practice on immediate test performance. Annual Meeting of the Psychonomics Society, San Francisco, CA.
14. McCarthy, K. S., Hinze, S. R., Dahl, A. C., Phillips, A., & Malloy, J. (2023, August 4). Combining learning strategies: Effects of explanation on retrieval and comprehension. Annual Meeting of the American Psychological Association, Washington, D.C.
15. Rutherford, T., & Fong, C. J. (2024, August). Flocking together? Twitter networks of educational psychologists versus learning scientists. Annual Convention of the American Psychological Association, Seattle, WA.

International Conference of the Learning Sciences (ICLS):

16. Gao, J., Huang, X., Dubé, A. K., & Lobczowski, N. G. (2024). Exploring Duolingo user’s learning experience through text mining. In Proceedings of the 18th International Conference of the Learning Sciences (ICLS 2024), Buffalo, NY.
17. Ocak, C., Akcaoglu, M., & Caskurlu, S. (Under review). Exploring in-service teachers’ attitudes toward computational thinking in online learning settings: A sentiment analysis. ICLS 2024, Buffalo, NY.

LAK Conference (Learning Analytics and Knowledge):

18. Han, S. & Castellanos-Reyes, D. (2024). Tracing the reviewers: A cluster analysis of MOOC engagement patterns using sequential pattern mining. LAK Short Paper.
19. McClure, J., Mushi, D, Jiang, S., & Kellogg, S. (2023). The state of teaching about algorithmic bias and fairness in Learning Analytics programs. Proceedings of the 23rd International Learning Analytics and Knowledge Conference. Arlington, TX.
20. 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.

Other Conferences:

21. Akcaoglu, M., & Ocak, C. (2023, December 5). Analyzing text data using computational methods. Georgia Southern University COE Mini-Conference, Statesboro, GA.
22. Atha, M. L., Katz-Buonincontro, J. & Tyagi, S. (2024, August 8). New ways to analyze divergent thinking responses to improve authentic assessment.  Annual Meeting of the American Psychological Association, Seattle, Washington.
23. Amresh, A. & Reeves, T. D. (2024, August). Basic text mining in R/RStudio. Northern Illinois University Graduate Student Association Coffee Hour, DeKalb, IL.
24. Greene, M.D., & Chang, C. (2024, November). Advancing educational research through machine learning and text mining. 2024 VCU Joint Research Symposium, Virtual.
25. Park, E. (2024). Analyzing graduate students’ online discussion interactions and topics: Social network analysis and structural topic modeling. AECT Annual Meeting, Kansas City, MO.
26. Penniston, T. (2023, July 26). Toward a new paradigm: Learning Analytics 2.0. Adaptive Instructional Systems. HCII 2023. Copenhagen, Denmark.
27. 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.
28. Rutherford, T., & Fong, C. (2024, August). Flocking together? Twitter networks of educational psychologists vs. learning scientists.Presentation at the annual meeting of the American Psychological Association, Seattle, WA, United States.
29. Salman, A. N., Wang, N., Martinez-Lucas, L. M., Vidal, A., & Busso, C. (2024, November). MSP-GEO Corpus: A Multimodal Database for Understanding Video-Learning Experience. In Proceedings of the 26th International Conference on Multimodal Interaction (pp. 488-497).
30. Whitford, M. (2024, May 28–31). Examining curricular enrollment patterns for first-generation female STEM students. Association for Institutional Research Annual Forum, Denver, CO.
31. 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.

Journal Publications (Published or In Press)

1. Amresh, A., Verma, V., & Zandieh, M. (2024). An in-depth evaluation of educational burst games in relation to learner proficiency. Multimodal Technologies and Interaction, 8(10), 88.
2. Chang, C. N., Lin, S., Kwok, O., & Saw, G. K. (2023). Predicting STEM major choice: A machine learning classification and regression tree approach. Journal for STEM Education Research, 6(2), 358-374.
3. Leech, K. A. (2024). Family science capital moderates gender differences in parent–child scientific conversation. Journal of Experimental Child Psychology, 247, 106020.
4. 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
5. 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.
6. Olesova, L., Sadaf, A., Choi, H., Castellanos-Reyes, D., Gant, S., & Cabrales, L. (under review). Integrating network data literacy to explore meaningful interactions in asynchronous online discussions: A case study applying the i-SUN framework. In C. Schumacher & D. Ifenthaler (Eds.), International Perspectives on Educational Data Literacy: Frameworks, Contexts, and Practices. Routledge.
7. Qian, Y. (under review). Understanding college students’ cognitive engagement in online STEM courses: From the ICAP perspectives. International Journal of STEM Education.
8. Rahimi, S., Walker, J. T., Lin-Lipsmeyer, L., & Shin, J. (2024). Toward defining and assessing creativity in sandbox games. Creativity Research Journal, 36(2), 194-212. https://doi.org/10.1080/10400419.2022.2156477
9. Sadaf, A., Olesova, L., & Choi, H. (2024). Empowering learning networks: Insights from Social Network Analysis in inquiry-based discussions. Online Learning Journal, 28(4), 200-227. https://doi.org/10.24059/olj.v28i4.4635
10. Tang, J., Zhou, X., Wan, X., Daley, M., & Bai, Z. (2023). ML4STEM professional development program: Enriching K-12 STEM teaching with machine learning. International Journal of Artificial Intelligence in Education, 33(1), 185-224.
11. Verma, V., Amresh, A., Baron, T., & Bansal, A. (2023). Software engineering for dynamic game adaptation in educational games. In Software Engineering for Games in Serious Contexts: Theories, Methods, Tools, and Experiences (pp. 43-62). Springer.
12. Wang, C., & Zhu, M. (2024). Trends and patterns in K-12 computer science education: Data analysis from Twitter. Educational Media International, 1-22. https://doi.org/10.1080/09523987.2024.2434978
13. Yu, J. H. (2024). Integrating actionable analytics into learning design for MOOCs: A design-based research. Journal of Computing in Higher Education, 1-39. https://doi.org/10.1007/s12528-024-09413-5
14. Yu, J. H., & Chauhan, D. (2024). Trends in NLP for personalized learning: LDA and sentiment analysis insights. Education and Information Technologies, 1-42. https://doi.org/10.1007/s10639-024-12988-2
15. Yu, J. H., Chauhan, D., Iqbal, R. A., & Yeoh, E. (2024). Mapping academic perspectives on AI in education: Trends, challenges, and sentiments in educational research (2018–2024). Educational Technology Research and Development, 1-29. https://doi.org/10.1007/s11423-024-10425-2
16. Yedjou, C. G., Tchakoua, C. T., Latinwo, L., Tchounwou, M., Eidahl, K., Alo, A. A., & Liu, H. (2024). Recent advances in artificial intelligence (AI) in education, ethical concerns and implications. International Journal of Science Academic Research, 05(2), 7027-7030.
17. Yedjou, C. G., Webster, S., Osborne, D., Liu, J., Balagurunathan, Y., Odewumi, C., Latinwo, L., Ngnepiepa, P., Alo, R., & Tchounwou, P. B. (2023). Health Promotion and Racial Disparity in COVID-19 Mortality Among African American Populations. Reports on Global Health Research, 6(3), ISSN: 2690-9480.
18. Zhu, M., & Wang, C. (in press). A systematic review of artificial intelligence in language education from 2013 to 2023: Current status and future implications. Language Learning & Technology, 29(1), 1-29.

Book Chapters and Books (Contracted or Published)

1. Fiacco, J., Jiang, S., Adamson, D., & Rosé, C. (in press). Learning analytics. In International Encyclopedia of Education (4th ed.).
2. 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
3. Wang, W., Akcaoglu, M., Rosenberg, J. M., & Kellogg, S. (under contract). Computational social science cookbook with R: A practical guide. CRC Press.

Manuscripts in Progress or Under Review

1. McCarthy, K. S. & Yan, E. F. (under review). Supporting reading comprehension and learning: Policy considerations in the age of AI.
2. Park, E., Gleasman, C., & Ogbomo, Q. (In preparation). Enhancing instructional design through analysis of interaction patterns and cognitive themes in graduate online discussions.
3. Moreno, M., Grewal, K., Cutumisu, M., & Harley, J.M. (Revise and Resubmit). Employing a machine learning approach to predict self-regulation and multimodal responses of medical learners in simulation environments. Educational Psychology Review.
4. Ocak, C., & Akcaoglu, M. (In preparation). In-service teachers’ motivations to teach computer science: Topic modeling. To be submitted to ITiCSE.
5. Ocak, C., Akcaoglu, M., & Caskurlu, S. (Under Review). Exploring in-service teachers’ attitudes toward computational thinking in online learning settings: A sentiment analysis.
6. Park, E., Gleasman, C., & Ogbomo, Q. (In preparation). Enhancing instructional design through analysis of interaction patterns and cognitive themes in graduate online discussions.
7. Qian, Y. (under review). Understanding college students’ cognitive engagement in online STEM courses from the ICAP perspective.
8. 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].
9. Williams, M. K. (2024). Creating productive academic writers through a fellowship program for new faculty. [Manuscript in progress].
10. Yu, J.H. (in preparation). Decoding dialogue dynamics: A topic modeling and sentiment analysis of MOOC discussion forums.
11. Yu, J.H. (in preparation). Emotional landscapes of learning: Sentiment dynamics in MOOC course feedback across time.
12. Zhu, M., & Wang, C. (under review). A systematic review of artificial intelligence for language learning: Leveraging data mining. Computer Assisted Language Learning.

Funded/Awarded Grants

1. U.S. Department of Agriculture (USDA). ($749,991). Nonformal Training of Michigan Youth on Intersection of Agriculture and Data Science.
   Co-PI: Meina Zhu (Wayne State University)
2. National Science Foundation (NSF) (2024). RCN-UBE: HBCU Faculty Collaborative Network for the Integration of Artificial Intelligence and Machine Learning into Teaching General Biology (Award No. 2417643)
   PI: Clement Yedjou (Florida A&M University) and collaborators
3. NSF CAREER Grant (2024). Award No. 2339516
   PI: Katie Leech (University of North Carolina at Chapel Hill)
4. NSF BCSER IID Grant (Award No. 2422544)
   PI: Praveen Meduri (California State University, Sacramento)
5. EAGER: Cultivating Scientific Mindsets in the Machine Learning Era, NSF Award #2225227
   Supported by work from Michael Daley (University of Rochester) and colleagues (Tang, J., Zhou, X., Wan, X., Bai, Z.)
6. Faculty Academy Grant (University-level)
   Awarded to Susan Hibbard (American Board of Anesthesiology) for developing a graduate course integrating 30-40% learning analytics content.
7. Non-disclosed funder ($350,000)
   PI: Veronica Minaya (Columbia University) – Project on predictive models for post-college outcomes.
8. University of Georgia Presidential Interdisciplinary Seed Grant ($60,000)
   Reimagining Sustainability: Pioneering Upcycled Foods in Circular Food Systems.
   PI: Peng Lu (University of Georgia), Co-PIs: A. Lamm, A. Borron, J.Y. Park, J. Gratzek, H.J. Ye, J. Deutsch
9. ASSISTments Funding
   PI: D. Zhang, Co-PIs: M. Li, D. Dong. Automated Classification of Student Problem-Solving Style in Representing Fractions with a Number Line.

Proposed/Submitted Grants

1. Toward Individualized Learning: Capturing the Complexity of Generative Learning Strategy Supports Using a Digital Platform.
   Submitted to IES, PI: Katie McCarthy (Georgia State University), Co-PI: A. Jaeger-Berena
2. Exploring Leaderboard Design for Gamified Learning
   Submitted to IES, PI: Fei Gao (Bowling Green State University) (with Hyeon Ah Kang as a collaborator)
3. 3X3 Science Curriculum Enhancement Program: Supporting the Science Success of Students in Primary-Secondary Transition.
   Submitted to IES, PI: Mia Williams (University of Wyoming), Co-PIs Megan Atha, Shaun Kellogg, Ning Wang
4. Cultivating Inclusive Networks of Creative Coders: Effects of Need Supportive Coaching and Peer Mentoring on a Robotics Competition
   Submitted to NSF’s AISL program, PI: Megan Atha (Florida Gulf Coast University), Co-PI:  Jennifer Katz-Buonincontro (Drexel University), Derek Lura (FGCU) and Anna Koufakou (FGCU)
5. Tool Competition 2025 (in Phase 2). HomeWorld Real-Time Video Learning Assessment Tool. Ning Wang (University of Texas at Dallas)
6. Advancing Irrigation Technology Adoption across the Southeast.
   Submitted to USDA Conservation Innovation Grants, Co-PI: Peng Lu (University of Georgia), et al.
7. Canadian Institutes of Health Research Fellowship (2024)
   PI: Matthew Moreno (McGill University)

Workshops, Symposia, and Additional Scholarly Activities

1. Arastoopour Irgens, G., Misiejuk, K., Kaliisa, R., Kellogg, S., Sciana, J., Eagan, B., Wang, Y., & Tan, Y. ( (2025). Foundational ideas and advanced methods in Quantitative Enthnography [Workshop]. Proceedings of the 25th International Conference on Learning Analytics and Knowledge (LAK25). Dublin, Ireland.
2. Symposium Organizing Committee for AAAI (2024). “Association for the Advancement of Artificial Intelligence (AAAI)” Symposium at the University of Stanford, California, March 25–27, 2024. Chaired by Clement Yedjou (Florida A&M University).
3. Kellogg, S.B., Moore, R., Jiang, S., Rosenberg, J.M., & Houchins, J. (2021, August 11). PEERS Workshop: A LASER Focus on Understanding and Improving STEM Education. AERA-ICPSR PEERS Data Hub.
4. Kellogg, S., Baker, R., Yu, R., Rutledge, J., Bernanki, M., Rosenberg, J. (2023). Graduate Programs in Learning Analytics Workshop: Core Competencies, Curriculum, and Instruction [Symposium]. Proceedings of the Annual Meeting of the American Education Research Association (AERA). Chicago, IL.
5. Kellogg, S., McClure, J., Houchins, J., Jiang, S., Mushi, D. (2023). An Introduction to Learning Analytics with R [Workshop]. Proceedings of the Annual Meeting of the American Education Research Association (AERA). Chicago, IL.
6. Kellogg, S., McClure, J., Smyslova, D., Jiang, S., Mushi, D. (2024). Social Network Analysis for Newbies: Theory, Applications, and Analysis [Workshop]. Proceedings of the 24th International Conference on Learning Analytics and Knowledge (LAK24). Kyoto, Japan.
7. Kellogg, S., McClure, J., Smyslova, D., Jiang, S., Mushi, D., Baker, R. (2024). Learning to Teach with (and Learn from!) the LASER Curriculum [Workshop]. Proceedings of the 24th International Conference on Learning Analytics and Knowledge (LAK24). Kyoto, Japan.
8. CREATE Lab Text Mining Workshop Series (Code Mapping, Crowdsourcing, Text-Mining Basics, and Sentiment Analysis). September–November, 2023. McGill University: Montreal, CA. (Facilitator: N. G. Lobczowski et al.)
9. Workshop presentations by LASER team members at AERA, Duke University’s Pandemic Pedagogy Research Symposium, and AERA Virtual Research Learning Series on topics such as learning analytics with RStudio, social network analysis, and text mining.
10. The creation of a Data Analyst position in a K-12 local educational agency due to LASER-informed requirements: Tacey Rodgers (Solano County Office of Education) credited LASER training for specifying R-Studio and analytics skills.
11. Development of a graduate course integrating 30-40% learning analytics content (Supported by Faculty Academy Grant, University not specified).
12. Pre-Proposal to the FGCU Provost Office for Submission to the Florida State Board of Governors for establishing a Master of Science in Learning Sciences & Analytics degree (anticipated start in Fall 2026). – Incorporating curriculum developed through LASER
13. Invited to be an Affiliated Researcher with the Florida BOG-approved Institute for AI & Data Science (call DENDRITIC) https://www.fgcu.edu/eng/dendritic-institute/#AffiliateMembers