To support situational learning in the classroom, the teachers need to arrange scenarios according to the teaching content and context so that the students can immerse in the scenarios to experience situational learning and teaching. Teachers commonly use Augmented reality AR, Virtual Reality VR, and 2D/3D computer screen displays to apply situational learning in the classroom. However, the existing digital reality mechanisms do not let the students see how they perform in the reality to do reflection learning and show it to their classmates so that the students feel responsible for learning and perform better to get recognition from their classmates. We designed and developed an alternative approach called Digital Theater to be used by teachers to apply situational learning in the classroom. Using video and skeleton capturing techniques, the video and sound of the students as actors can be captured, mixed, and put into the digital reality generated by the computer according to the context scenario of the learning content. The students formed groups to perform on the stage in the classroom. The Digital Theater system displays the performance as a stage drama that shows to the students who act as the actors or the audience in the classroom. The digital theater mechanism can be easily applied in an existing classroom. Moreover, the AI cognitive recognition mechanisms such as facial recognition, gesture recognition, speech recognition, and dialog language processing can be integrated into the digital theater. Therefore, the student's physical performance can be captured, recognized, and evaluated in the digital space. The digital space, including virtual humans and robots, can interact and respond based on the designed learning script. After that, Digital Theater was formed as a digital game room for students to explore. Novel learning design can be devised on the digital theater mechanism. The virtual human and physical robots can be integrated into the digital theater to improve the student's learning performance. The physical robot can be used as a learning and presentation tool for designing how to learn and presenting learning results.
The COVID-19 pandemic forced educators around the world to make a sudden move to remote learning, often without the benefit of appropriate training and resources. As we begin to adapt to the ‘new normal’, there are opportunities to incorporate the findings of more than 20 years research on online education into our daily practice. For the last decade, The Open University in the UK has been producing ‘Innovating Pedagogy’ reports. These introduce new approaches together with sound advice based on evidence, common sense, and clarity. The pedagogies range from small-scale innovations, which individual educators can try out in their classes, to sweeping trends that may shape education futures around the world. In her keynote, Professor Rebecca Ferguson, lead author on the report for several years, introduces some of the pedagogies most relevant for a world in which teaching increasingly takes place at a distance. These include hybrid models that combine face-to-face and online approaches, enriched realities that extend the possibilities for learning, microcredentials, online laboratories, virtual studios, wellbeing education, and student-led analytics
Machine-learned based detectors have become an increasingly important part of contemporary AIED systems, measuring and/or predicting constructs ranging from knowledge, to disengagement, to affect, to stopout. However, often when models are developed, they are only tested to a very limited degree (usually just on held-out students from the original data set) and are then used in different situations without further evaluation. In this talk, I will discuss evidence around when detectors generalize -- and when they don't -- in terms of student identity and changes in the learning system itself, using examples from multiple studies in our research group spanning from stopout, to gaming the system, to wheel-spinning, to affect. I will offer some simple guidelines about the situations that seem to be linked to successful model generalization and propose some steps forward for better understanding this challenge.
IDC theory owes its origins to a group of prominent Asian researchers concerned with the worrying trends of students' declining interest in learning. Indeed, their concerns about examination-driven education have struck a chord among scholars, especially in the Asian context. IDC theory posits that once interest in learning is piqued through interest-driven learning activities, students will be engaged in the knowledge creation process. Learning habits then ensue through repetition of the aforesaid process in the students' daily routine. This keynote focuses on interest-driven learning with the main aim of bridging theory and practice. In this talk, I will illustrate the trajectory of IDC theory in Asian classrooms since its inception in 2014. I will then share the findings of a recent study focusing on the first loop — interest. I will showcase empirical data illustrating how interest in educational technology can be developed through learning activities designed based on IDC tenets. I will end my talk by highlighting the key characteristics that emerged from the interest loop and how teacher education can benefit from IDC theory.
When I was a graduate student in the 1990s, online discussion environments were called “CSILE-like,” after the original environment that Marlene Scardamalia and colleagues developed to support knowledge building. Knowledge building was—and is—an effort to introduce students to a culture and the processes of knowledge creation. Nowadays, discussion forums are ubiquitous: Twitter, WeChat, Facebook, Whatsapp, TikTok, Instagram, and more. There also is an abundance of personal websites, vlogs, and podcasts. And where I live, newscast report on their own opinion polls and at talk shows scientists sit next to comedians and restaurant operators to debate the science of climate change or the Corona pandemic. People often do some Googling before they speak or consult their social networks. At first sight at least, all this it does look somewhat like knowledge building—e.g., democratization of knowledge and idea diversity. In this presentation I consider knowledge building as an educational model considering these developments. Is it an idea that has been passed by, or can it be a way forward? What kinds of educational development are then needed?
Learning a new language other than one’s first language (L1) is always challenging. It takes time, effort, focus, motivation, and sustained involvement. The use of language for communication and social interaction has always been a key competency in the 21st century. Technology plays a significant role in helping today’s learners to acquire a language. Research on language learning has become highly interdisciplinary, drawing upon a range of fields such as psychology, education, neuroscience, and recently machine learning. TELL study follows the theoretical and methodological advances in education, cognitive science, and neuroscience. Moreover, TELL research depends heavily on the latest technologies. In this speech, I will illustrate the new paradigm of language learning in the modern digital era, examine TELL based on various methods and platforms supported by new digital technologies such as AI, mobile computing, VR, and digital games. It will also include how emerging technologies can further extend the influence of TELL research.