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1. Incorporating Sentiment Analysis with Epistemic Network Analysis to Enhance Discourse Analysis of Twitter Data

   https://doi.org/https://doi.org/10.1007/978-3-030-67788-6_26  

   Misiejuk, Kamila; Scianna, Jennifer; Kaliisa, Rogers; Vachuska, Karl; Shaffer, David W. (January 2021, Advances in Quantitative Ethnography: Second International Conference, ICQE 2020, Malibu, CA, USA, February 1-3, 2021, Proceedings)

   Ruis, Andrew R.; Lee, Seung B. (Ed.)

   While there has been much growth in the use of microblogging platforms (e.g., Twitter) to share information on a range of topics, researchers struggle to analyze the large volumes of data produced on such platforms. Established methods such as Sentiment Analysis (SA) have been criticized over their inaccuracy and limited analytical depth. In this exploratory methodological paper, we propose a combination of SA with Epistemic Network Analysis (ENA) as an alternative approach for providing richer qualitative and quantitative insights into Twitter discourse. We illustrate the application and potential use of these approaches by visualizing the differences between tweets directed or discussing Democrats and Republicans after the COVID-19 Stimulus Package announcement in the US. SA was integrated into ENA models in two ways: as a part of the blocking variable and as a set of codes. Our results suggest that incorporating SA into ENA allowed for a better understanding of how groups viewed the components of the stimulus issue by splitting them by sentiment and enabled a meaningful inclusion of data with singular subject focus into the ENA models. 

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2. STEM Moments in the Family Context throughout Engineering Design Challenge Activities

   Kim, Jungsun; Simpson, Amber (January 2021, American Society for Engineering Education)

   null (Ed.)

   Research on interactions between caregivers and children have long been reported in science museum experiences. However, the interactions between caregivers and children in home environments are rarely investigated. By comparison, research on the experience of the engineering design challenge activities in a family context is even less. This case study aimed to examine interactions of two families in their home as they engaged with engineering design challenge kits that have the potential to support children’s foundational understanding of STEM concepts. Using social-cultural constructivism as a lens, about 370 minutes of video data was analyzed. Data coding revealed three types of interactions that facilitated children’s understanding of STEM concepts: teaching, build up, and synthesized moments. These three moments were interdependent but included different emphasis of caregivers’ and children’s engagement. Although there is a limitation of this study to generalize the findings, our results contribute to understand how caregivers and children play with the materials, tools, and their ideas in their home environments and how caregivers used different facilitation approaches without any training prior to engaging with the engineering kits. 

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3. STEM Moments in the Family Context throughout Engineering Design Challenge Activities (Fundamental)

   Kim, J.; Simpson, A. (July 2021, Zone 1 Conference of the American Society for Engineering Education)

   Research on interactions between caregivers and children have long been reported in science museum experiences. However, the interactions between caregivers and children in home environments are rarely investigated. By comparison, research on the experience of the engineering design challenge activities in a family context is even less. This case study aimed to examine interactions of two families in their home as they engaged with engineering design challenge kits that have the potential to support children’s foundational understanding of STEM concepts. Using social-cultural constructivism as a lens, about 370 minutes of video data was analyzed. Data coding revealed three types of interactions that facilitated children’s understanding of STEM concepts: teaching, build up, and synthesized moments. These three moments were interdependent but included different emphasis of caregivers’ and children’s engagement. Although there is a limitation of this study to generalize the findings, our results contribute to understand how caregivers and children play with the materials, tools, and their ideas in their home environments and how caregivers used different facilitation approaches without any training prior to engaging with the engineering kits. 

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4. At-home engineering: Caregivers’ support during problem-solving.

   Penney, L. (November 2023, Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023)

   Blikstein, P. (Ed.)

   The home environment is a critical context in which children engage in STEM activities. Caregivers serve as key influencers on their children’s engagement in these activities. This case study of four families explored how caregivers support their child(ren) during moments of problem-solving while completing engineering activities at home and illustrated the variation in caregiver support, caregiver participation/child agency, problem-solving strategies used, and integration of activities into the home. Our findings suggest we need to be purposeful in designing kit activities and supports for caregivers that will contribute to meaningful interactions during STEM activities that draw upon the unique relationship between caregivers and their children in the home. 

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5. Leveraging Machine Learning to Analyze Semantic User Interactions in Visual Analytics

   https://doi.org/10.3390/info15060351  

   Jeong, Dong Hyun; Jeong, Bong Keun; Ji, Soo Yeon (June 2024, Information)

   In the field of visualization, understanding users’ analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend this reasoning process. However, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that addresses the limitation by correlating semantic user interactions with analysis decisions using an interactive wire transaction analysis system and a visual state transition matrix, both designed as visual analytics applications. The system enables interactive analysis for evaluating financial fraud in wire transactions. It also allows mapping captured user interactions and analytical decisions back onto the visualization to reveal their decision differences. The visual state transition matrix further aids in understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to evaluate the effectiveness of our approach in understanding users’ analytical reasoning process by connecting the captured semantic user interactions to their decisions (i.e., suspicious, not suspicious, and inconclusive) on wire transactions. With the algorithms, an average of 72% accuracy is determined to classify the semantic user interactions. For classifying individual decisions, the average accuracy is 70%. Notably, the accuracy for classifying ‘inconclusive’ decisions is 83%. Overall, the proposed approach improves the understanding of users’ analytical decisions and provides a robust method for evaluating user interactions in visualization tools. 

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