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專題演講
Professor Lyn D. English
Professor Lyn D. English
School of Teacher Education and Leadership
Queensland University of Technology, Australia
Innovating Mathematics Learning within STEM
This presentation will consider how we might implement more innovative approaches to mathematics learning within a rapidly developing STEM world. Mathematics seems to be one of the more difficult subjects to be integrated within STEM education and thus tends to play a secondary role—a situation that calls for innovative intervention. In addressing this situation, consideration will be given first to the changing global landscape of STEM and what STEM literacy involves today. Such changes include greater global awareness of STEM, increased complexity of global problems requiring STEM solutions, and the proliferation of data and data representations (and misrepresentations). Importantly, artificial intelligence is developing at a prolific rate, such that educational research cannot keep up. The implications for mathematics and STEM` education are becoming immense. All of these STEM developments require students to not only possess future-oriented disciplinary knowledge but also powerful ways of thinking with this knowledge. Examples of how we might promote advanced mathematical competencies within STEM-based modelling problems will be presented. Input from conference participants is encouraged as we tackle these educational challenges.
Professor Yasmin Kafai
Professor Yasmin Kafai
Learning Sciences & Technologies Program
University of Pennsylvania, USA
Learning About Algorithm Auditing: How High School Teachers and Students Can Systematically and Critically Evaluate Machine Learning Applications
While there is widespread interest in introducing young people and teachers to artificial intelligence applications, there is little research on how we can support them in critical inquiry about their limitations. Outside of K-12 education, an effective strategy in evaluating black-boxed systems is algorithm auditing—a method for understanding algorithmic systems' opaque inner workings and external impacts from the outside in. In this talk I will present findings from our work introducing high school teachers and students to algorithm auditing practices for examining fairness, accountability, and justice in AI systems. I will introduce the five-step method of algorithm auditing which we have developed with teachers and implemented in high school classrooms across the US. Connecting to the familiar scientific inquiry, algorithm auditing provides an accessible and no-coding experience needed approach which can also be used outside of computer science classrooms. It shifts student roles from passive AI consumption to critical agency, creating systemic change while building teacher capacity to address algorithmic justice.
Yasmin B. Kafai is the Lori and Michael Milken President’s Distinguished Professor at the Graduate School of Education, University of Pennsylvania, with a secondary appointment in Computer and Information Science. A leading learning designer and researcher, she develops online tools, projects, and communities that foster coding, critical thinking, and creativity. With colleagues at the MIT Media Lab, Kafai developed Scratch, the widely popular programming language now used by over 100 million young people worldwide, with postings over one billion projects. Her current research is focused on developing algorithm auditing of machine learning applications which supports high school students and teachers in investigating AI systems and examining how high school students can design their own babyGPTs, small generative language models, to gain better understanding of their functionality and limitations. Additionally, through the nationwide Exploring Computer Science curriculum, she has pioneered the use of electronic textiles to introduce computing, engineering, and machine learning in high school classrooms. Kafai is the author of several influential books, including Connected Code: Why Children Need to Learn Programming and Connected Gaming: What Making Videogames Can Teach Us About Learning and Literacy. Most recently, she co-edited Designing Constructionist Futures: The Art, Theory, and Practice of Learning Designs—all published by the MIT Press. She earned her doctorate in education from Harvard University while working at the MIT Media Lab. Kafai is an elected Fellow of the American Educational Research Association and the International Society for the Learning Sciences.
Professor Emily Dare
Professor Emily Dare
Science/STEM Education,
Louisiana State University, USA
Supporting Teachers’ Integrated STEM Practice Through Novel Research
The focus on Science, Technology, Engineering, and Mathematics education - commonly referred to as STEM education - has been lauded and critiqued since its inception in the 1990s. In more recent years and specifically within K-12 education, the term “STEM education” has become synonymous with integrated STEM education. This approach challenges the traditional, isolated approach to teaching STEM content in favor of a pedagogical shift that requires educators to consider multiple STEM disciplines simultaneously. In this, integrated STEM education puts forth a more realistic representation of how STEM content is used beyond the confines of formal education. This has major implications for K-12 students, as integrated STEM pedagogical approaches may spark and ignite interest in STEM careers. While most literature agrees that integrated STEM education includes the use of real-world contexts, more student-centered approaches, and intentional development of 21st century skills, defining integrated STEM education for practice has been challenging. This presentation will dive into the challenges faced by K-12 educators when it comes to integrated STEM education and provide research-based insight on how to address common challenges. This includes discussion surrounding the development of the STEM Observation Protocol (STEM-OP), which can be used to start new conversations within educational spaces to not only conduct educational research, but also help others develop and refine their integrated STEM classroom practices through reflective practice.
Professor Hsiao-Ching She
（佘曉清教授）
Professor Hsiao-Ching She
（佘曉清教授）
Institute of Education,
National Yang Ming Chiao Tung University,
Taiwan, ROC
Decoding How Students Learn Science: From Learning Analytics to
Neural Signatures
Earlier of my research focused on conceptual change—investigating how students construct and reconstruct scientific concepts—and on promoting effective science learning that fosters deeper conceptual understanding and strengthens scientific reasoning and argumentation (She, 2002, 2004; She & Liao, 2010; Lee & She, 2010; Yeh & She, 2010; Chen & She, 2015). The use of learning analytics allows us to measure, collect, analyze, and interpret learners’ data to generate data-driven insights that enhance our understanding of student learning and optimize the effectiveness of online learning environments. Our recent studies have focused on developing a series of educational programs, including educational games (Kuo et al., 2024; Cheng et al., 2015; Cheng et al., 2017), simulation-based collaborative learning programs in chemistry and physics (Chen et al., 2024; Guo et al., 2023), and scientific inquiry programs in physics and biology (Chou et al., 2022; Syu et al., 2025), all designed to enhance students’ scientific conceptual understanding and competencies. These programs have effectively enhanced students’ conceptual understanding and strengthened their scientific competencies in inquiry, problem-solving, and modeling. Supported by learning analytics, we have gained deeper insights into how students improve their performance, process information, and why their learning outcomes vary, while also enabling the prediction and optimization of future learning outcomes.
Meanwhile, our research has advanced the integration of eye-tracking and neuroimaging tools—such as EEG and fMRI—to uncover how learners think and how their brains function during the processes of conceptual construction and reconstruction. Through the application of eye-tracking technology, we have explored how students process scientific information, revealing how they construct and reconstruct scientific concepts across diverse learning contexts—including learning about mitosis and meiosis (She & Chen, 2009), retrieving physics conceptions through multiple modalities (Chen et al., 2014), constructing atomic models (Chen et al., 2015), solving geometry problems with multiple scaffolds (Liang et al., 2021), developing evidence-based reasoning supported by metacognitive eye-tracking feedback (Tsai et al., 2019), and learning physics concepts through analogical reasoning (Chen et al., 2021)—culminating in recent investigations into the cognitive mechanisms underlying scientific conceptual change (She et al., 2025). Furthermore, the use of EEG and fMRI has revealed the neural mechanisms underlying various aspects of scientific cognition, including working memory in physics, chemistry, and biology (Lai et al., 2012; Huang et al., 2013; Chou et al., 2014, 2015; Tsai et al., 2019; Liang et al., 2020), problem solving in physics (She et al., 2012), decision-making in chemistry (Huang et al., 2018), relational reasoning in physics (Chou et al., 2023), and hippocampus-related retrieval of scientific semantic memory (She et al., 2023). In this talk, we will share how our program has advanced students’ scientific learning and competencies through the integration of learning analytics, eye-tracking, and neuroscience technologies
This talk presents how our research has advanced students’ scientific learning and competencies through the integration of learning analytics, eye-tracking, and neuroscience technologies. Through learning analytics, we uncover patterns in how students process, integrate, and transform information into meaningful learning outcomes. Eye-tracking technology further reveals where students direct their attention, how the sequence of their visual focus influences conceptual understanding, and how these attentional patterns can predict learning performance. Building upon these findings, the incorporation of neuroscience tools such as EEG and fMRI allows us to explore how key brain regions—including the prefrontal cortex, hippocampus, and temporal lobe—work together to support scientific knowledge retrieval and conceptual change.
Professor Jon-Chao Hong
（洪榮昭教授）
Professor Jon-Chao Hong
（洪榮昭教授）
Department of industrial Education,
National Taiwan Normal University,
Taiwan, ROC
Embedded iSTEAM in hands-on competition
1. GoSTEAM
Embedding STEAM in designing GoSTEAM includes, on science domain, students have to know the material properties, and science principles to be applied to carry out those moving and transport functions, and build the architecture. On technology domain, students have to use wood laser cut machine and/or 3D printing to design track and architectures for marbles to move, and transport. On engineering domain, students work out the design and adjust parts to ensure marbles can move between mechanical structures smoothly, stably and reliable. On math domain, students have to apply the concept such as proportional calculation, symmetry analysis, calculate the amount of force, and spatial geometry. On arts domain, students can apply their cultural knowledge to design the mechanical structures for marble moving with esthetic perspective.
將STEAM融入GoSTEAM設計中，在科學領域，學生需要了解材料特性，並運用科學原理來實現滾珠移動功能，建構出對應的機關。在科技領域，學生需要使用木材雷射切割機和/或3D列印技術，設計出彈珠移動和運輸的軌道和結構。在工程領域，學生需要設計並調整零件，以確保彈珠能夠在機械結構之間平穩、穩定、長久移動。在數學領域，學生需要運用比例計算、對稱性分析、計算彈珠動量的大小、空間幾何等概念。在藝術領域，學生可以運用其文化知識，以美學視角設計關卡的結構。
2. iSTEAM-PowerTech
In designing miniature models for PowerTech, Science necessitates an understanding of material properties and the functions of mechanisms including power, constant speed, acceleration, force, gravity, friction, and so on. Regarding technology include material processing activities, like sawing wooden boards to shape the model's structure and assembly of parts precisely and effectively. Engineering involves addressing uncertainties problems related to controllability, balancing, reliability, stability and efficiency, flexibility during transitions, and dynamic operation. As for mathematics included, geometric primitives, counting, multiplication, and decimals. The construction and comprehension of graphs were also integral components of this mathematical exploration. Arts into the process of model creation involved fostering students' appreciation for culture, aesthetics, and the humanities; that presenting miniature performance.
在為PowerTech設計仿生機構時，在科學學習上，需要理解材料特性和各種機制的功能，包括功率、恆速、加速度、力、重力、摩擦力等等。技術學習則包括材料加工活動，例如鋸木板以塑造模型結構以及精確有效地組裝零件。工程學習則涉及解決與可控性、平衡、可靠性、穩定性和效率、過渡期間的靈活性以及動態操作相關的不確定性問題。數學學習包括幾何計算、運動量、扭力計算及圖表的建構和理解分。藝術學習在仿生機構設計過程，融入文化、美學和人文的應用能力，提升仿生機構的運動的仿生效果。
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