@article{nocera_newton_jiang_2024, title={"They created segregation with the economy": Using AI for a student-driven inquiry into redlining in the social studies classroom}, ISSN={["2163-1654"]}, DOI={10.1080/00933104.2024.2331466}, abstractNote={This article investigates students' engagement with a historical inquiry into redlining—a practice of discriminatory lending that originated in the 1930s as part of the New Deal. The authors developed and implemented a week-long curricular intervention for high school sophomores using StoryQ—an Artificial Intelligence (AI) textual modeling platform designed for high school students without technical expertise—to examine hundreds of neighborhood descriptions produced for the Home Owners Loan Corporation's "residential security maps" in the late 1930s. In this article, we ask: What kinds of historical and present-day racial awareness do high school students demonstrate through instruction focused on AI-assisted analysis of patterns in redlining? Analyzing field notes, interviews, and student-generated digital work showed that many students were drawn to structural explanations of racism and worked to unpack the way primary sources presented Whiteness through "coded language." We argue that it is not only possible for teachers to construct historical inquiries that aim to identify patterns in a large set of primary sources with the aid of AI, but this approach to inquiry offers students an important avenue to engage with alternatives to individual conceptions of racial oppression.}, journal={THEORY AND RESEARCH IN SOCIAL EDUCATION}, author={Nocera, Amato and Newton, Victoria and Jiang, Shiyan}, year={2024}, month={Apr} }
 @article{tatar_jiang_rose_chao_2024, title={Exploring Teachers' Views and Confidence in the Integration of an Artificial Intelligence Curriculum into Their Classrooms: a Case Study of Curricular Co-Design Program}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-024-00404-2}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Tatar, Cansu and Jiang, Shiyan and Rose, Carolyn P. and Chao, Jie}, year={2024}, month={May} }
 @article{li_jiang_hu_feng_chen_ouyang_2024, title={Investigating the impact of structured knowledge feedback on collaborative academic writing}, ISSN={["1573-7608"]}, DOI={10.1007/s10639-024-12560-y}, journal={EDUCATION AND INFORMATION TECHNOLOGIES}, author={Li, Xu and Jiang, Shiyan and Hu, Yue and Feng, Xiaoxiao and Chen, Wenzhi and Ouyang, Fan}, year={2024}, month={Mar} }
 @article{kahn_jiang_2024, title={Leveraging epistemic data agency with data visualizations to bridge the gap between data trends and personal experiences}, ISSN={["2398-5356"]}, DOI={10.1108/ILS-03-2023-0024}, abstractNote={Purpose While designing personally meaningful activities with data technologies can support the development of data literacies, this paper aims to focuses on the overlooked aspect of how learners navigate tensions between personal experiences and data trends. Design/methodology/approach The authors report on an analysis of three student cases from a design study in which middle and high school youth assembled family migration stories using data visualization technologies with socioeconomic and demographic data. The authors used interaction analysis to examine how students responded to misalignments they encountered between their families’ experiences and data trends in their models, drawing on the theoretical construct of epistemic data agency. Findings This case analysis demonstrates ways in which students enacted epistemic data agency. Instructional support can help students deepen inquiry and avoid certain pitfalls, such as encoding data in unsound or misleading ways to support a particular story, while encouraging students to see themselves as an epistemic authority on par with data. This study opens pathways for future research that considers how data can shape personal narratives and how students can leverage their experiences in the stories they tell with data. Originality/value The authors introduce the construct of epistemic data agency to describe the conceptual and material practices that reveal and shape students’ relationships to the data. The descriptions of students enacting epistemic data agency in assembling data stories informs the understanding of how to better elevate and recognize students’ efforts in relation to disciplinary norms and support deeper, meaningful student learning with and about data.}, journal={INFORMATION AND LEARNING SCIENCES}, author={Kahn, Jennifer and Jiang, Shiyan}, year={2024}, month={Sep} }
 @article{jiang_mcclure_tatar_bickel_rose_chao_2024, title={Towards inclusivity in AI: A comparative study of cognitive engagement between marginalized female students and peers}, volume={4}, ISSN={["1467-8535"]}, url={https://doi.org/10.1111/bjet.13467}, DOI={10.1111/bjet.13467}, abstractNote={Abstract This study addresses the need for inclusive AI education by focusing on marginalized female students who historically lack access to learning opportunities in computing. It applies the theoretical framework of intersectionality to understand how gender, race and ethnicity intersect to shape these students' learning experiences and outcomes. Specifically, this study investigated 27 high‐school students' cognitive engagement in machine learning practices. We conducted the Wilcoxon–Mann–Whitney test to explore differences in cognitive engagement between marginalized female students and their peers, employed comparative content analysis to delve into significant differences and analysed interview data thematically to gain deeper insights into students' machine learning model development processes. The findings indicated that, when engaging in machine learning practices requiring drawing diverse cultural perspectives, marginalized female students demonstrated significantly higher performance compared to their peers. In particular, marginalized female students exhibited strengths in holistic language analysis, paying attention to writers' intentions and recognizing cultural nuances in language. This study suggests that integrating language analysis and machine learning across subjects has the potential to empower marginalized female students and amplify their perspectives. Furthermore, it calls for a strengths‐based approach to reshape the narrative of underrepresentation and promote equitable participation in machine learning and AI. Practitioner notes What is already known about this topic Female students, particularly those from underrepresented groups such as African American and Latina students, often experience low levels of cognitive engagement in computing. Marginalized female students possess unique strengths that, when nurtured, have the potential to not only transform their own learning experiences but also contribute to the advancement of the computing field. It is critical to empower marginalized female students in K‐12 AI (ie, a subfield of computing) education, seeking to bridge the gender and racial disparity in AI. What this paper adds Marginalized female students outperformed their peers in responding to machine learning questions related to feature analysis and feature distribution interpretation. When responding to these questions, they demonstrated a holistic approach to analysing language by considering interactions between features and writers' intentions. They drew on knowledge about how language was used to convey meaning in different cultural contexts. Implications for practice and/or policy Educators should design learning environments that encourage students to draw upon their cultural backgrounds, linguistic insights and diverse experiences to enhance their engagement and performance in AI‐related activities. Educators should strategically integrate language analysis and machine learning across different subjects to create interdisciplinary learning experiences that support students' exploration of the interplay among language, culture and AI. Educational institutions and policy initiatives should adopt a strengths‐based approach that focuses on empowering marginalized female students by acknowledging their inherent abilities and diverse backgrounds.}, journal={BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY}, author={Jiang, Shiyan and Mcclure, Jeanne and Tatar, Cansu and Bickel, Franziska and Rose, Carolyn P. and Chao, Jie}, year={2024}, month={Apr} }
 @article{jiang_wang_2023, title={Blurring the boundaries of current and future selves: students' STEM identity exploration in a multimodal composing learning environment}, ISSN={["1743-9892"]}, DOI={10.1080/17439884.2023.2225858}, abstractNote={ABSTRACT As multimodal composition is gradually integrated into STEM learning, research is needed to examine how to fully connect multimodal composing practices and STEM practices to support students’ STEM identity exploration. To fill in this gap, we conducted a design-based research project to investigate how students explored STEM identities in a multimodal composing environment. A total of 42 fifth- to eighth-grade students participated in the project in which they worked in groups to create multimodal science fiction stories. We identified two cross-cutting themes regarding how students presented STEM-related selves in multimodal artifacts: channeling current and imagined future life experiences into multiple characters and experiencing a hypothetical universe through restorying the self. The findings suggest that students presented a mix of current and future selves in multimodal artifacts. This study sheds light on promoting STEM identity by engaging students in presenting selves through modes of choice.}, journal={LEARNING MEDIA AND TECHNOLOGY}, author={Jiang, Shiyan and Wang, Changzhao}, year={2023}, month={Jun} }
 @article{sanei_kahn_yalcinkaya_jiang_wang_2023, title={Examining How Students Code with Socioscientific Data to Tell Stories About Climate Change}, volume={6}, ISSN={["1573-1839"]}, url={https://doi.org/10.1007/s10956-023-10054-z}, DOI={10.1007/s10956-023-10054-z}, journal={JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY}, author={Sanei, Hamid and Kahn, Jennifer B. and Yalcinkaya, Rabia and Jiang, Shiyan and Wang, Changzhao}, year={2023}, month={Jun} }
 @article{jiang_tang_tatar_rose_chao_2023, title={High school students' data modeling practices and processes: From modeling unstructured data to evaluating automated decisions}, ISSN={["1743-9892"]}, DOI={10.1080/17439884.2023.2189735}, abstractNote={ABSTRACT It’s critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students’ data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students’ processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies.}, journal={LEARNING MEDIA AND TECHNOLOGY}, author={Jiang, Shiyan and Tang, Hengtao and Tatar, Cansu and Rose, Carolyn P. and Chao, Jie}, year={2023}, month={Mar} }
 @article{jiang_mcclure_mao_chen_liu_zhang_2023, title={Integrating Machine Learning and Color Chemistry: Developing a High-School Curriculum toward Real-World Problem-Solving}, volume={12}, ISSN={["1938-1328"]}, url={https://doi.org/10.1021/acs.jchemed.3c00589}, DOI={10.1021/acs.jchemed.3c00589}, abstractNote={Artificial intelligence (AI) is rapidly transforming our world, making it imperative to educate the next generation about both the potential benefits and the challenges associated with AI. This study presents a cross-disciplinary curriculum that connects AI and chemistry disciplines in the high school classroom. Particularly, we leverage machine learning (ML), an important and simple application of AI to instruct students to build an ML-based virtual pH meter for high-precision pH read-outs. We used a "codeless" and free ML neural network building software, Orange, along with a simple chemical topic of pH to show the connection between AI and chemistry for high-schoolers who might have rudimentary backgrounds in both disciplines. The goal of this curriculum is to promote student interest and drive in the analytical chemistry domain and offer insights into how the interconnection between chemistry and ML can benefit high-school students in science learning. The activity involves students using pH strips to measure the pH of various solutions with local relevancy and then building an ML neural network model to predict the pH value based on the color changes of pH strips. The integrated curriculum increased student interest in chemistry and ML and demonstrated the relevance of science to students' daily lives and global issues. This approach is transformative in developing a broad spectrum of integration topics between chemistry and ML and understanding their global impacts.}, journal={JOURNAL OF CHEMICAL EDUCATION}, author={Jiang, Shiyan and Mcclure, Jeanne and Mao, Hongjing and Chen, Jiahui and Liu, Yunshu and Zhang, Yang}, year={2023}, month={Dec} }
 @article{jiang_2023, title={Investigating Adolescents' Participation Trajectories in a Collaborative Multimodal Composing Learning Environment}, volume={26}, ISSN={["1436-4522"]}, DOI={10.30191/ETS.202307_26(3).0003}, number={3}, journal={EDUCATIONAL TECHNOLOGY & SOCIETY}, author={Jiang, Shiyan}, year={2023}, month={Jul}, pages={21–36} }
 @article{ding_li_jiang_gapud_2023, title={Students' perceptions of using ChatGPT in a physics class as a virtual tutor}, volume={20}, ISSN={["2365-9440"]}, DOI={10.1186/s41239-023-00434-1}, abstractNote={AbstractThe latest development of Generative Artificial Intelligence (GenAI), particularly ChatGPT, has drawn the attention of educational researchers and practitioners. We have witnessed many innovative uses of ChatGPT in STEM classrooms. However, studies regarding students’ perceptions of ChatGPT as a virtual tutoring tool in STEM education are rare. The current study investigated undergraduate students’ perceptions of using ChatGPT in a physics class as an assistant tool for addressing physics questions. Specifically, the study examined the accuracy of ChatGPT in answering physics questions, the relationship between students’ ChatGPT trust levels and answer accuracy, and the influence of trust on students’ perceptions of ChatGPT. Our finding indicates that despite the inaccuracy of GenAI in question answering, most students trust its ability to provide correct answers. Trust in GenAI is also associated with students’ perceptions of GenAI. In addition, this study sheds light on students’ misconceptions toward GenAI and provides suggestions for future considerations in AI literacy teaching and research.}, number={1}, journal={INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION}, author={Ding, Lu and Li, Tong and Jiang, Shiyan and Gapud, Albert}, year={2023}, month={Dec} }
 @article{jiang_huang_lee_2023, title={Visualizing qualitative data: unpacking the complexities and nuances of technology-supported learning processes}, volume={7}, ISSN={1042-1629 1556-6501}, url={http://dx.doi.org/10.1007/s11423-023-10272-7}, DOI={10.1007/s11423-023-10272-7}, journal={Educational technology research and development}, publisher={Springer Science and Business Media LLC}, author={Jiang, Shiyan and Huang, Joey and Lee, Hollylynne S.}, year={2023}, month={Jul} }
 @article{jiang_nocera_tatar_yoder_chao_wiedemann_finzer_rose_2022, title={An empirical analysis of high school students' practices of modelling with unstructured data}, ISSN={["1467-8535"]}, DOI={10.1111/bjet.13253}, abstractNote={AbstractTo date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making.Practitioner notesWhat is already known about this topic

Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.
While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.
There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.
What this paper adds

Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.
Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.
Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.
Implications for practice and/or policy

It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.
Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.
To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).
}, journal={BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY}, author={Jiang, Shiyan and Nocera, Amato and Tatar, Cansu and Yoder, Michael Miller and Chao, Jie and Wiedemann, Kenia and Finzer, William and Rose, Carolyn P.}, year={2022}, month={Jul} }
 @article{jiang_lee_rosenberg_2022, title={Data science education across the disciplines: Underexamined opportunities for K-12 innovation}, ISSN={["1467-8535"]}, DOI={10.1111/bjet.13258}, journal={BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY}, author={Jiang, Shiyan and Lee, Victor R. and Rosenberg, Joshua M.}, year={2022}, month={Jul} }
 @article{wang_rose_ma_jiang_bao_li_2022, title={Design and Application of Automatic Feedback Scaffolding in Forums to Promote Learning}, volume={15}, ISSN={["1939-1382"]}, DOI={10.1109/TLT.2022.3156914}, abstractNote={Forums are essential components facilitating interactions in online courses. However, in large-scale courses, many posts generated, which results in learners’ difficulties. First, the posts are poorly organized and some deviate from the topic, making it difficult for learners’ knowledge acquisition. Second, learners cannot receive timely feedback and guidance, making the learning progress unclear for them. Well-designed scaffoldings should be built based on challenges of forums to improve learners’ learning outcomes, knowledge construction, and completion rate. While targeting the problems in online forums, this article proposed principles for the design of online scaffolding after analyzing the requirements of online learning scaffolding or scripts. Subsequently, in this article, we designed an automatic feedback scaffolding based on the principles and a knowledge construction model. The scaffolding provided learners with timely feedback and related learning guidance. Tags were used to assist learners in acquiring relevant information more easily. The scaffolding was then integrated into the Learning Cell Knowledge Community and used in an online course for 955 learners. The results showed that automatic feedback scaffolding positively affected learners’ learning and promoted positive knowledge transformation. Furthermore, we found that the scaffolding could help learners induce more constructive behaviors defined in the Interactive, Constructive, Active, and Passive deep learning framework that demonstrated the reason for learners’ knowledge transformation. At last, learners’ course completion rate also increased with the help of the scaffolding, which provided evidence that well-designed scaffolding can result in positive educational outcomes. In addition, the principles proposed could also contribute to further scaffolding design and practices.}, number={2}, journal={IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES}, author={Wang, Qi and Rose, Carolyn P. and Ma, Ning and Jiang, Shiyan and Bao, Haogang and Li, Yanyan}, year={2022}, month={Apr}, pages={150–166} }
 @article{peng_li_su_chen_jiang_2022, title={Effects of group awareness tools on students' engagement, performance, and perceptions in online collaborative writing: Intergroup information matters}, volume={53}, ISSN={["1873-5525"]}, DOI={10.1016/j.iheduc.2022.100845}, abstractNote={Low student engagement is still a problem in online collaborative writing. We integrated two types of awareness information (i.e., intergroup and intragroup information) into a collaborative writing platform to enhance student engagement. A quasi-experiment study was conducted to examine the effects of intergroup and intragroup awareness information. The experimental class of 81 students were presented with intergroup information while the control class of 80 students were presented with intragroup information, and these students were required to perform collaborative writing and peer evaluation activities. The Wilcoxon–Mann–Whitney U test revealed that the experimental class had significantly higher behavioral engagement in writing and better academic performance than the control class. Also, the results showed that the students with intergroup awareness information had deeper cognitive thinking and demonstrated more positive emotion than the students with intragroup awareness information in online discussion and peer evaluation. Additionally, this study investigated students' perceptions of the group awareness tool using the Technology Acceptance Model (TAM). This paper concludes with future research directions for supporting collaborative learning.}, journal={INTERNET AND HIGHER EDUCATION}, author={Peng, Yu and Li, Yanyan and Su, You and Chen, Kailiang and Jiang, Shiyan}, year={2022}, month={Apr} }
 @article{jiang_qian_tang_yalcinkaya_rose_chao_finzer_2022, title={Examining computational thinking processes in modeling unstructured data}, ISSN={["1573-7608"]}, DOI={10.1007/s10639-022-11355-3}, journal={EDUCATION AND INFORMATION TECHNOLOGIES}, author={Jiang, Shiyan and Qian, Yingxiao and Tang, Hengtao and Yalcinkaya, Rabia and Rose, Carolyn P. and Chao, Jie and Finzer, William}, year={2022}, month={Oct} }
 @article{rubel_herbel-eisenman_peralta_lim_jiang_kahn_2022, title={Intersectional feminism to reenvision mathematical literacies & precarity}, ISSN={["1754-0178"]}, DOI={10.1080/14794802.2022.2089908}, abstractNote={ABSTRACT Current global crises (e.g. COVID-19 pandemic and climate change) necessitate changes to mathematics curricula, especially related to using mathematics to solve real-world problems. We begin with the Programme for International Student Assessment's (PISA) framework for mathematical literacy (FML), since it functions as a global guide for curriculum. We demonstrate its inadequacy to solve current crises and to mediate the precarity of girls and women. Then we reenvision the FML by integrating concepts of critical mathematics education with intersectional feminism. We reenvision how to think about mathematical literacies. In particular, we add practices of feeling, acting, and reimagining to the conventional construct of mathematical reasoning. We reenvision ways to think about or classify real-world problem contexts by exploring three potential themes for real-world problem contexts.}, journal={RESEARCH IN MATHEMATICS EDUCATION}, author={Rubel, Laurie H. and Herbel-Eisenman, Beth and Peralta, Lee Melvin and Lim, Vivian and Jiang, Shiyan and Kahn, Jennifer}, year={2022}, month={May} }
 @article{lim_peralta_rubel_jiang_kahn_herbel-eisenmann_2022, title={Keeping pace with innovations in data visualizations: A commentary for mathematics education in times of crisis}, ISSN={["1863-9704"]}, DOI={10.1007/s11858-022-01449-0}, abstractNote={The mathematical medium of data visualization and other data representations (DV) has served as a primary means of communicating about the COVID-19 crisis. DVs about the pandemic are highly visible across news journalism and include an increasingly innovative and diverse set of representational forms. These representational forms employ multimodal, interactive, and narrative elements, among others, that create new possibilities for data storytelling. Building on current efforts to expand the teaching and learning of data practices in K-12 mathematics education, we argue that innovative DVs create new opportunities for teaching and learning mathematics, particularly during times of crisis. We illustrate our argument using three examples of innovative DVs from news journalism. We discuss how these DVs could serve as complementary resources alongside conventional graphs to support students as they use mathematics and mathematical representations to make sense of crises such as the COVID-19 pandemic. Our commentary seeks to bring current trends in data representation to bear in mathematics education. Leveraging such trends offers artifacts useful for teaching and opens up space for elevating emotion and experience as important aspects of mathematics curricula.}, journal={ZDM-MATHEMATICS EDUCATION}, author={Lim, Vivian Y. Y. and Peralta, Lee Melvin M. and Rubel, Laurie H. H. and Jiang, Shiyan and Kahn, Jennifer B. B. and Herbel-Eisenmann, Beth}, year={2022}, month={Dec} }
 @article{jiang_huang_sung_xie_2022, title={Learning Analytics for Assessing Hands-on Laboratory Skills in Science Classrooms Using Bayesian Network Analysis}, ISSN={["1573-1898"]}, DOI={10.1007/s11165-022-10061-x}, journal={RESEARCH IN SCIENCE EDUCATION}, author={Jiang, Shiyan and Huang, Xudong and Sung, Shannon H. and Xie, Charles}, year={2022}, month={Jul} }
 @article{moore_jiang_abramowitz_2022, title={What would the matrix do?: a systematic review of K-12 AI learning contexts and learner-interface interactions}, ISSN={["1945-0818"]}, DOI={10.1080/15391523.2022.2148785}, abstractNote={Abstract This systematic review examines the empirical literature published between 2014 and 2021 that situates artificial intelligence within K-12 educational contexts. Our review synthesizes 12 articles and highlights artificial intelligence’s instructional contexts and applications in K-12 learning environments. We focused our synthesis on the learning contexts and the learner-interface interactions. Our findings highlight that most of intelligent systems are being deployed in math or informal settings. Also, there are opportunities for more collaboration to facilitate teaching and learning in domain-specific areas. Additionally, researchers can explore how to implement more collaborative learning opportunities between intelligent tutors and learners. We conclude with a discussion of the reciprocal nature of this technology integration.}, journal={JOURNAL OF RESEARCH ON TECHNOLOGY IN EDUCATION}, author={Moore, Robert L. and Jiang, Shiyan and Abramowitz, Brian}, year={2022}, month={Nov} }
 @article{jiang_tatar_huang_sung_xie_2021, title={Augmented Reality in Science Laboratories: Investigating High School Students' Navigation Patterns and Their Effects on Learning Performance}, ISSN={["1541-4140"]}, DOI={10.1177/07356331211038764}, abstractNote={ Augmented reality (AR) has the potential to fundamentally transform science education by making learning of abstract science ideas tangible and engaging. However, little is known about how students interacted with AR technologies and how these interactions may affect learning performance in science laboratories. This study examined high school students’ navigation patterns and science learning with a mobile AR technology, developed by the research team, in laboratory settings. The AR technology allows students to conduct hands-on laboratory experiments and interactively explore various science phenomena covering biology, chemistry, and physics concepts. In this study, seventy ninth-grade students carried out science laboratory experiments in pairs to learn thermodynamics. Our cluster analysis identified two groups of students, which differed significantly in navigation length and breadth. The two groups demonstrated unique navigation patterns that revealed students’ various ways of observing, describing, exploring, and evaluating science phenomena. These navigation patterns were associated with learning performance as measured by scores on lab reports. The results suggested the need for providing access to multiple representations and different types of interactions with these representations to support effective science learning as well as designing representations and connections between representations to cultivate scientific reasoning skills and nuanced understanding of scientific processes. }, journal={JOURNAL OF EDUCATIONAL COMPUTING RESEARCH}, author={Jiang, Shiyan and Tatar, Cansu and Huang, Xudong and Sung, Shannon H. and Xie, Charles}, year={2021}, month={Aug} }
 @article{chen_bhat_jiang_zhao_2020, title={Advanced Driver Assistance Strategies for a Single-Vehicle Overtaking a Platoon on the Two-Lane Two-Way Road}, volume={8}, ISSN={["2169-3536"]}, DOI={10.1109/ACCESS.2020.2989082}, abstractNote={Recently, the ever-increasing vehicle population has become a severe challenge to traffic safety, especially the problem of a single-vehicle overtaking a platoon on the Two-Lane Two-Way (TLTW) road. Platooning has the potential to improve traffic efficiency and safety. However, there exists a perilous situation of “Neither overtake nor give up” when the single-vehicle overtakes a platoon on the TLTW road. This paper presents a flexible framework to automatically filter a large quantity of Advanced Driver Assistance Strategies (ADAS) and select the most suitable driver assistance information for the single-vehicle overtakes a platoon on the TLTW road. A step-by-step Single Vehicle Overtakes Platoon (SVOP) algorithm is designed to generate the coarse ADAS, which had given plenty of consideration to the vehicle safety, traffic efficiency, and driving comfort. Then, this paper obtains the raw data about the single-vehicle overtakes a platoon on the TLTW by using CARLA, which can help us to get 20 drivers’ upper and down boundaries of both velocity and acceleration. In addition, the extracted ranges of velocity and acceleration are used to quantitatively analyse the drivers’ driving features and filter the ADAS information. Finally, a Bayesian nonparametric approach is developed to segment driver’s driving raw data temporal sequences into small analytically interpretable components without using prior knowledge. So that the accurately overtaking characteristics can be obtained, and the ADAS can be further filtered. Experimental results demonstrate that the obtained coarse ADAS are only valid in theory but not acceptable by most of the drivers. Nonetheless, by leveraging the nonparametric Bayes algorithm, the driver’s overtaking behavior can be divided into different primitives, from which some could obtain the driver’s acceptance range for the velocity and acceleration. 92.3% ~ 94.78% invalid SVOP ADAS could be filtered out by leveraging the primitive-based SVOP approach. Thus, after filtering, the overtaking scheme is the most acceptable strategy for drivers.}, journal={IEEE ACCESS}, author={Chen, Junjie and Bhat, Manoj and Jiang, Shiyan and Zhao, Ding}, year={2020}, pages={77285–77297} }
 @article{jiang_huang_xie_sung_yalcinkaya_2020, title={Augmented Scientific Investigation: Support the Exploration of Invisible "Fine Details" in Science via Augmented Reality}, DOI={10.1145/3392063.3394406}, abstractNote={Augmented reality (AR) has great potential to radically change science education by making abstract science concepts visible and interactive. In this paper, we describe initial investigations into high school students' perceptions of learning science with an AR technology (i.e., SmartIR) through analyzing semi-structured interviews. SmartIR is an app that supports the investigation of science, such as thermodynamics. Specifically, it can show changes in thermal imaging over time and provides a data analytics function that visualizes data for analyzing and interpreting the changes. Our analysis of 31 interviews shows that students perceived the exploration of science phenomena with "fine details", including a full vision of second-by-second changes in thermal imaging, as helpful and engaging to understand science concepts. In future work, these findings will be triangulated with logging data of their interactions with SmartIR and student-generated lab reports.}, journal={PROCEEDINGS OF IDC 2020}, author={Jiang, Shiyan and Huang, Xudong and Xie, Charles and Sung, Shannon and Yalcinkaya, Rabia}, year={2020}, pages={349–354} }
 @article{shen_chen_barth-cohen_jiang_eltoukhy_2022, title={Connecting computational thinking in everyday reasoning and programming for elementary school students}, volume={54}, ISSN={["1945-0818"]}, DOI={10.1080/15391523.2020.1834474}, abstractNote={Abstract Computational thinking (CT) has been advocated as an essential problem solving skill students need to develop. Emphasizing on CT applied in both programming and everyday contexts, we developed a humanoid robotics curriculum and a computerized assessment instrument. We implemented the curriculum with six classes of 125 fifth graders. Quantitative methods were used to compare students’ performance from pretest to posttest. Learning analytics techniques were applied to examine students’ problem solving processes. The results showed that students’ CT performance improved in both programming and everyday reasoning contexts and that the curriculum benefited students with varied initial performance. The study shed light on how to connect and assess CT in everyday reasoning and programming contexts.}, number={2}, journal={JOURNAL OF RESEARCH ON TECHNOLOGY IN EDUCATION}, author={Shen, Ji and Chen, Guanhua and Barth-Cohen, Lauren and Jiang, Shiyan and Eltoukhy, Moataz}, year={2022}, month={May}, pages={205–225} }
 @article{jiang_kahn_2020, title={Data wrangling practices and collaborative interactions with aggregated data}, volume={15}, ISSN={["1556-1615"]}, DOI={10.1007/s11412-020-09327-1}, number={3}, journal={INTERNATIONAL JOURNAL OF COMPUTER-SUPPORTED COLLABORATIVE LEARNING}, author={Jiang, Shiyan and Kahn, Jennifer}, year={2020}, month={Sep}, pages={257–281} }
 @article{kahn_jiang_2021, title={Learning with large, complex data and visualizations: youth data wrangling in modeling family migration}, volume={46}, ISSN={["1743-9892"]}, DOI={10.1080/17439884.2020.1826962}, abstractNote={ABSTRACT We present a micro-analysis of youth interactions with large complex, socioeconomic datasets and data visualization tools. Middle and high school youth used georeferenced data and data visualization tools to assemble models that present their family migration histories in relation to larger socioeconomic trends in a summer program. Using screen-capture and video recordings, field notes, and artifacts, we analyzed youth’s step-by-step decision-making and interaction with data interfaces in data wrangling, which we define as the practices for selecting, interpreting, and integrating datasets in order to build meaningful data displays and tell a story with the data. We identify patterns in youth’s data wrangling trajectories and propose a conceptual model for describing the stages (Find, Relate, Challenge, Build) of youth learning to construct models and tell stories about family migration. In addition, we highlight student struggles and opportunities for learning to be explored in future learning environment designs with large, complex datasets and data interfaces.}, number={2}, journal={LEARNING MEDIA AND TECHNOLOGY}, author={Kahn, Jennifer and Jiang, Shiyan}, year={2021}, month={Apr}, pages={128–143} }
 @article{jiang_shen_smith_kibler_2020, title={Science identity development: how multimodal composition mediates student role-taking as scientist in a media-rich learning environment}, volume={68}, ISSN={["1556-6501"]}, DOI={10.1007/s11423-020-09816-y}, number={6}, journal={ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT}, author={Jiang, Shiyan and Shen, Ji and Smith, Blaine E. and Kibler, Kristin Watson}, year={2020}, month={Dec}, pages={3187–3212} }