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1. Mapping the “long tail” of research funding: A topic analysis of NSF grant proposals in the division of astronomical sciences

   https://doi.org/https://doi.org/10.1002/pra2.276  

   Stahlman, Gretchen; Heidorn, P. Bryan (October 2020, Proceedings of the Association for Information Science and Technology)

   null (Ed.)

   “Long tail” data are considered to be smaller, heterogeneous, researcher‐held data, which present unique data management and scholarly communication challenges. These data are presumably concentrated within relatively lower‐funded projects due to insufficient resources for curation. To better understand the nature and distribution of long tail data, we examine National Science Foundation (NSF) funding patterns using Latent Dirichlet Allocation (LDA) and bibliographic data. We also introduce the concept of “Topic Investment” to capture differences in topics across funding levels and to illuminate the distribution of funding across topics. This study uses the discipline of astronomy as a case study, overall exploring possible associations between topic, funding level and research output, with implications for research policy and practice. We find that while different topics demonstrate different funding levels and publication patterns, dynamics predicted by the “long tail” theoretical framework presented here can be observed within NSF‐funded topics in astronomy. 

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2. Evaluating the Coverage and Depth of Latent Dirichlet Allocation Topic Model in Comparison with Human Coding of Qualitative Data: The Case of Education Research

   https://doi.org/10.3390/make5020029  

   Nanda, Gaurav; Jaiswal, Aparajita; Castellanos, Hugo; Zhou, Yuzhe; Choi, Alex; Magana, Alejandra J. (June 2023, Machine Learning and Knowledge Extraction)

   Fields in the social sciences, such as education research, have started to expand the use of computer-based research methods to supplement traditional research approaches. Natural language processing techniques, such as topic modeling, may support qualitative data analysis by providing early categories that researchers may interpret and refine. This study contributes to this body of research and answers the following research questions: (RQ1) What is the relative coverage of the latent Dirichlet allocation (LDA) topic model and human coding in terms of the breadth of the topics/themes extracted from the text collection? (RQ2) What is the relative depth or level of detail among identified topics using LDA topic models and human coding approaches? A dataset of student reflections was qualitatively analyzed using LDA topic modeling and human coding approaches, and the results were compared. The findings suggest that topic models can provide reliable coverage and depth of themes present in a textual collection comparable to human coding but require manual interpretation of topics. The breadth and depth of human coding output is heavily dependent on the expertise of coders and the size of the collection; these factors are better handled in the topic modeling approach. 

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3. Designing an Introductory HRI Course

   https://doi.org/10.1145/3610978.3638165  

   Admoni, Henny; Johal, Wafa; Szafir, Daniel; Sandygulova, Anara (March 2024, Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction)

   Human-robot interaction is now an established discipline. Dozens of HRI courses exist at universities worldwide, and some institutions even offer degrees in HRI. However, although many students are being taught HRI, there is no agreed-upon curriculum for an introductory HRI course. In this workshop, we aim to reach community consensus on what should be covered in such a course. Through interactive activities like panels, breakout discussions, and syllabus design, workshop participants will explore the many topics and pedagogical approaches for teaching HRI. They will then distill their findings into a single example introductory HRI curriculum. Output from this workshop will include a short paper explaining this curriculum and an example syllabus that can be used and adapted by HRI educators. 

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4. The Koa Ecosystem: Supporting Data Intensive Computing and Education in Hawaii

   https://doi.org/10.1145/3626203.3670611  

   Schanzenbach, David; Merrill, Ronald; Cleveland, Sean B (July 2024, ACM)

   The Koa ecosystem is a comprehensive computational ecosystem built on open source software and developed by the University of Hawai‘i to enhance data-intensive research across its campuses. The Koa ecosystem addresses critical needs for computational resources, high-performance storage, and sustainable infrastructure to support multiple scientific disciplines. Over two years, the Koa ecosystem has significantly boosted research output, as evidenced by numerous publications acknowledging it and the research community’s continued investment in expanding the resource. 

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5. ISPeL: A topic dependency-driven system for personalized learning

   https://doi.org/10.1109/ICSC50631.2021.00081  

   Gudivada, Venkat N; Herndon, Nic; Rao, Dhana (January 2021, 2021 IEEE 15th International Conference on Semantic Computing (ICSC))

   In this paper, we describe the design and development of ISPeL - an Interactive System for Personalization of Learning. Central to ISPeL is topic-based authoring. A topic is a small, self-contained, reusable, and context-free content unit. Learners may study a topic provided that they have met its prerequisite dependencies. Pre- and post-tests are associated with topics. Furthermore, topics feature several practice problems to enhance student learning. A pilot implementation of three undergraduate computer science courses currently in ISPeL is also presented. 

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