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1. The Effects of Prior Knowledge on Learning with Low-Cost Desktop Learning Modules

   Watson, C. ; Van Wie, B. ; Dutta, P. ; Adesope, O. ; Gartner, J. ; Curtis, H ( August 2022 , Zone 1 Conference of the American Society for Engineering Education)

   Hands-on experiments using the Low-Cost Desktop Learning Modules (LCDLMs) have been implemented in dozens of classrooms to supplement student learning of heat transfer and fluid mechanics concepts with students of varying prior knowledge. The prior knowledge of students who encounter these LCDLMs in the classroom may impact the degree to which students learn from these interactive pedagogies. This paper reports on the differences in student cognitive learning between groups with low and high prior knowledge of the concepts that are tested. Student conceptual test results for venturi, hydraulic loss, and double pipe heat exchanger LCDLMs are analyzed by grouping the student data into two bins based on pre-test score, one for students scoring below 50% and another for those scoring above and comparing the improvement from pretest to posttest between the two groups. The analysis includes data from all implementations of each LCDLM for the 2020-2021 school year. Results from each of the three LCDLMs were analyzed separately to compare student performance on different fluid mechanics or heat exchanger concepts. Then, the overall pre- and posttest scores for all three LCDLMs were analyzed to examine how this interactive pedagogy impacts cognitive gains. Results showed statistically significant differences in improvement between low prior knowledge groups and high prior knowledge groups. Additional findings showed statistically significant results suggesting that the gaps in performance between low prior knowledge and high prior knowledge groups on pre-tests for the LCDLMs were decreased on the posttest. Findings showed that students with lower prior knowledge show a greater overall improvement in cognitive gains than those with higher prior knowledge on all three low-cost desktop learning modules. 

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2. The Effects of Prior Knowledge on Learning with Low-Cost Desktop Learning Modules

   Watson, Carah ; Gartner, Jacqueline ; Van Wie, Bernard ; Dutta, Prashanta ; Adesope, Olusola ; Curtis, Heidi ( January 2022 , ASEE annual conference)

   Hands-on experiments using the Low-Cost Desktop Learning Modules (LCDLMs) have been implemented in dozens of classrooms to supplement student learning of heat transfer and fluid mechanics concepts with students of varying prior knowledge. The prior knowledge of students who encounter these LCDLMs in the classroom may impact the degree to which students learn from these interactive pedagogies. This paper reports on the differences in student cognitive learning between groups with low and high prior knowledge of the concepts that are tested. Student conceptual test results for venturi, hydraulic loss, and double pipe heat exchanger LCDLMs are analyzed by grouping the student data into two bins based on pre-test score, one for students scoring below 50% and another for those scoring above and comparing the improvement from pretest to posttest between the two groups. The analysis includes data from all implementations of each LCDLM for the 2020-2021 school year. Results from each of the three LCDLMs were analyzed separately to compare student performance on different fluid mechanics or heat exchanger concepts. Then, the overall pre- and posttest scores for all three LCDLMs were analyzed to examine how this interactive pedagogy impacts cognitive gains. Results showed statistically significant differences in improvement between low prior knowledge groups and high prior knowledge groups. Additional findings showed statistically significant results suggesting that the gaps in performance between low prior knowledge and high prior knowledge groups on pre-tests for the LCDLMs were decreased on the posttest. Findings showed that students with lower prior knowledge show a greater overall improvement in cognitive gains than those with higher prior knowledge on all three low-cost desktop learning modules. 

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3. Instructor facilitation of STEM+CT discourse: engaging, prompting and guiding students’ computational modeling in physics

   Snyder, C. ( June 2022 , Computersupported collaborative learning)

   Chinn, C. (Ed.)

   The integration of computational modeling into instruction in science classrooms is complex in that it requires the synergistic application of students’ developing science and computational thinking knowledge. This is not only difficult for students, but teachers often find it hard to parse the science content from the computational constructs to guide students when they have difficulties. Leveraging past literature that highlights the beneficial impact instructors can have when they immerse themselves in group problem-solving discussions, this paper examines the instructors’ role in facilitating students’ construction of and problem-solving with computational models. We utilize a case study approach to analyze instructor-facilitated, synchronous group discussions during applications of synergistic learning processes to understand how instructors may elicit students’ knowledge, misunderstanding, and difficulties to help guide, prompt, and engage groups in this complex task for more productive integration in K-12 science classrooms. We hope that this will lead to better scaffolding of students' learning, and better support for teachers when they use such curricula in classrooms. 

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4. Modeling Knowledge Acquisition from Multiple Learning Resource Types

   Zhao, S. ; Wang, C. ; Sahebi, S. ( July 2020 , Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020))

   Students acquire knowledge as they interact with a variety of learning materials, such as video lectures, problems, and discussions. Modeling student knowledge at each point during their learning period and understanding the contribution of each learning material to student knowledge are essential for detecting students’ knowledge gaps and recommending learning materials to them. Current student knowledge modeling techniques mostly rely on one type of learning material, mainly problems, to model student knowledge growth. These approaches ignore the fact that students also learn from other types of material. In this paper, we propose a student knowledge model that can capture knowledge growth as a result of learning from a diverse set of learning resource types while unveiling the association between the learning materials of different types. Our multi-view knowledge model (MVKM) incorporates a flexible knowledge increase objective on top of a multi-view tensor factorization to capture occasional forgetting while representing student knowledge and learning material concepts in a lower-dimensional latent space. We evaluate our model in different experiments to show that it can accurately predict students’ future performance, differentiate between knowledge gain in different student groups and concepts, and unveil hidden similarities across learning materials of different types. 

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5. Returning to disrupting deficit narratives: Funds of knowledge beyond curriculum

   Adiredja, Aditya ; Civil, Marta ( October 2023 , University of Nevada, Reno)

   Lamberg, T ; Moss, D (Ed.)

   We examine the potential for instructor-led student interview in disrupting narratives about minoritized university students and their families. This approach was inspired by a critical component of the original Funds of Knowledge for Teaching project (FoK), the home visit, which involved ethnographic interviews with students and their family to learn about their lives. Study group reflections with project teachers and researchers led to the teachers’ examination of biases and disruption of deficit stories about students and their families (Tenery, 2005). Two frameworks guided the design and the analysis of the study. The anti-deficit framework for students from minoritized groups (Adiredja, 2019) prioritizes unpacking students’ existing understandings and resources for learning, and explicitly constructing counter-stories about students and their mathematics. The study also interprets the results of the study from a socioecological perspective (Louie & Zhan, 2022). Story construction is attributed to the individual, the learning community they are in, and the broader societal context. The data comes from a larger project focusing on engaging university instructors in community learning project focusing on race, gender, and mathematics (NSF DUE 2021313 IUSE). The project began in the summer and the interview occurred in the following Spring. Six participants interviewed a student of their choice, and four attended a follow-up debrief meeting. Results showed that participants gained novel insights into their students’ lives. The focal case, Zaynah shows that the interview can also change an instructor’s deficit story about a student and their mathematics into an anti-deficit story. Zaynah’s post-interview description of her student, Maria, went from someone who had “weak algebra skills” and was “just kind ofthere,” to a student who had a well-researched and articulated academic and life goals, and a “passion” that Zaynah “knew nothing about.” Zaynah accepted that math “wasn’t [Maria’s] thing,” and that “she can and will do the math” to achieve those goals. The anti-deficit framework and the socio-ecological perspective rejected a simple narrative of a deficit-oriented instructor who changed after the interview experience. Zaynah is not a deficit-oriented instructor. Her description of her other students in the workshop—all women of color— were positive, with no mention of deficits. Instead, it is a more complex story of the interplay of narratives about mathematics and students of color, and the impact of the interview with a student and the role of the community in shifting deficit stories. It is showing promise of applying FoK project’s core principles in undergraduate mathematics beyond curriculum design. 

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