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1. A 3D Printer Build for Students with Visual Impairments

   Madura, T; Christian, C; O'Beollain, S; Wild, T; Silberman, K (May 2025, Journal on technology and persons with disabilities)

   Miesenberger, K; Robles, A; Ruiz, S (Ed.)

   While 3D printing may be a promising tool for making Science, Technology, Engineering, and Mathematics (STEM) education more accessible for students with visual impairments, most research centers on creating and using tactile models and braille, rather than direct student use of 3D printing technologies. This study observed 121 high school studentswith visual impairments across twelve states, examining whether and how students with visual impairments engage in scientific and engineering practices during their assembling of a 3D printer. We found that students exhibited all eight of the science and engineering behaviors defined in the National Research Council's A Framework for K-12 Science Education:Practices, Crosscutting Concepts, and Core Ideas. This study builds upon the work of Hilson and Wild and shows that students with visual impairments, when given the opportunity, can demonstrate scientific and engineering process skills just as their sighted peers do. This is the largest sample of students with visual impairments to date to be observed to document their work and behaviors in this area of STEM research. However, further research is needed to examine science and engineering behaviors of students with visual impairments in other STEM areas and while completing other complex STEM tasks. 

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2. Modeling Complementarity in Behavior Data with Multi-Type Itemset Embedding

   https://doi.org/10.1145/3458724  

   Wang, Daheng; Zeng, Qingkai; Chawla, Nitesh V.; Jiang, Meng (June 2021, ACM Transactions on Intelligent Systems and Technology)

   null (Ed.)

   People are looking for complementary contexts, such as team members of complementary skills for project team building and/or reading materials of complementary knowledge for effective student learning, to make their behaviors more likely to be successful. Complementarity has been revealed by behavioral sciences as one of the most important factors in decision making. Existing computational models that learn low-dimensional context representations from behavior data have poor scalability and recent network embedding methods only focus on preserving the similarity between the contexts. In this work, we formulate a behavior entry as a set of context items and propose a novel representation learning method, Multi-type Itemset Embedding , to learn the context representations preserving the itemset structures. We propose a measurement of complementarity between context items in the embedding space. Experiments demonstrate both effectiveness and efficiency of the proposed method over the state-of-the-art methods on behavior prediction and context recommendation. We discover that the complementary contexts and similar contexts are significantly different in human behaviors. 

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3. Work in Progress: Assessment of Reflective Thinking in Graduate Engineering Students: Human and Machine Methods

   Taraban, R.; Isserman, M.; Pittman, J.C.; Yeo, N.; Campbell, R.C.; Kim, J.H.; Reible, D.D. (July 2021, 2021 ASEE Annual Conference Proceedings)

   Engineering education is increasingly looking to the liberal arts to broaden and diversify preparation of students for professional careers. The present study involves an elective graduate environmental engineering course that incorporated the arts and humanities. The goal of the course was to develop engineers and technical professionals who would become both more appreciative of and better equipped to address technical, ethical, social, and cultural challenges in engineering through the development of critical and reflective thinking skills and reflective practice in their professional work. A reflective writing assignment was submitted by students following each of fourteen course topics in response to the following question: Reflect on how you might want to apply what you learned to your development as a professional and/or to your daily life. Student responses were classified by human coders using qualitative text analytic methods and their classifications were attempted to be learned by a simple machine classifier. The goal of this analysis was to identify and quantify students’ reflections on prospective behaviors that emerged through participation in the course. The analysis indicated that the primary focus of students’ responses was self-improvement, with additional themes involving reflection, teamwork, and improving the world. The results provide a glimpse into how broadening and diversifying the curriculum might shape students’ thinking in directions that are more considerate of their contributions to their profession and society. In the discussion, we consider the findings from the human and machine assessments and suggest how incorporating AI machine methods into engineering provides new possibilities for engineering pedagogy. 

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4. Student Behaviors and Interactions Influence Group Discussions in an Introductory Biology Lab Setting

   https://doi.org/10.1187/cbe.20-03-0054  

   Paine, Alex R.; Knight, Jennifer K. (December 2020, CBE—Life Sciences Education)

   Gardner, Grant Ean (Ed.)

   Past research on group work has primarily focused on promoting change through implementation of interventions designed to increase performance. Recently, however, education researchers have called for more descriptive analyses of group interactions. Through detailed qualitative analysis of recorded discussions, we studied the natural interactions of students during group work in the context of a biology laboratory course. We analyzed multiple interactions of 30 different groups as well as data from each of the 91 individual participants to characterize the ways students engage in discussion and how group dynamics promote or prevent meaningful discussion. Using a set of codes describing 15 unique behaviors, we determined that the most common behavior seen in student dialogue was analyzing data, followed by recalling information and repeating ideas. We also classified students into one of 10 different roles for each discussion, determined by their most common behaviors. We found that, although students cooperated with one another by exchanging information, they less frequently fully collaborated to explain their conclusions through the exchange of reasoning. Within this context, these findings show that students working in groups generally choose specific roles during discussions and focus on data analysis rather than constructing logical reasoning chains to explain their conclusions. 

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5. 2021, April. Understanding health and behavioral trends of successful students through machine learning models. In International Conference on Human Interaction and Emerging Technologies (pp. 516-525). Springer, Cham.

   https://doi.org/10.1007/978-3-030-74009  

   Kim, A.; Nikseresht, F.; Dutcher, J.M.; Tumminia, M.; Villalba, D.; Cohen, S.; Creswell, K.; Creswell, D.; Dey, A.K.; Mankoff, J.; et al (April 2021, International Conference on Human Interaction and Emerging Technologies)

   This study analyzes patterns of physical, mental, lifestyle, and personality factors in college students in different periods over the course of a semester and models their relationships with students’ academic performance. The data analyzed was collected through smartphones and Fitbit. The use of machine learning models derived from the gathered data was employed to observe the extent of students’ behavior associated with their GPA, lifestyle, physical health, mental health, and personality attributes. A mutual agreement method was used in which rather than looking at the accuracy of results, the model parameters and weights of features were used to find common behavioral trends. From the results of the model creation, it was determined that the most significant indicator of academic success defined as a higher GPA, was the places a student spent their time. Lifestyle and personality factors were deemed more significant than mental and physical factors. This study will provide insight into the impact of different factors and the timing of those factors on students’ academic performance . 

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