This talk presents a vision for Personal Informatics for Learners (PIL). This builds on three foundations. The first, and oldest of these, comes from the decades of research on Open Learner Models (OLMs) in Artificial Intelligence in Education. These fields strive to create high quality teaching systems with personalisation driven by a fine-grained, carefully crafted model of the learner. The second is the far newer, but very fast growing LAK, Learning Analytics and Knowledge. This arose with wide deployment of learning technology that captures huge quantities of learning data and the tantalising possibilities such data offers. The third, Personal Informatics (PI) comes from Ubicomp research, aiming to harness personal sensor data about diverse aspects of life, particularly for health and wellness. PIL is grounded on the view that the learner should be empowered to take responsibility for their own learning. To achieve this, PIL needs to make progress on many fronts. We need mechanisms for collecting the right learning data and managing it effectively. We also need new classes of the interfaces and systems that enable learners to control their data, do personal data mining and engage in meta-cognitive processes of reflection, self-monitoring, planning.
The talk begins by reviewing the broad scope and vision described above. Taking that lens, I then present a two sets of case studies. The first relates to group-work, with interfaces onto data harvested as groups use an online collaboration tool or collaborate around an interactive tabletop or wall display, with diverse models, from the simple to richer ones built by data mining. Key insights come from the series of studies, both in the lab and in authentic classrooms. The second set of case studies is for individual learning, ranging from mastering computer software and curriculum-wide learner modelling to personal informatics, for health and wellness, harnessing data from activity trackers, virtual reality and mobile food logging. The talk concludes with key lessons learnt and a research agenda for PIL.
Problem-solving tasks are often categorized into two types: ill-structured one and well-structured one, in the context of education/learning, cognitive science and artificial intelligence. Then, from an educational viewpoint, ill-structured tasks are further more important because they are useful to promote a learner to think about target learning contents deeply and to master computational or logical thinking skills, including metacognition and self-regulation. This talk presents an additional characterization of problem-solving tasks by using two factors, (1) well/ill-structured domain model and (2) well/ill-structured task setting. Here, a well-structured domain model provides a problem space as a set of states and a set of operators linking one state with the next. Then, a well-structured task setting provides a specific problem space with an initial sate and a goal state. Based on this characterization, “moderately ill-structured tasks” can be defined as a category of tasks specified by “well-structured domain model” and “ill-structured task setting”. If a task is set in well-structured domain model, it is possible to realize computer-based monitoring and diagnosis of learner’s activities for the task. If the task setting is ill-structured, for example, open-ended, a learner is required to engage in the task as ill-structured one. Therefore, moderately ill-structured tasks are promising to realize computer-based intelligent support for solving the tasks, while keeping educational advantages of ill-structured tasks. In this paper, a definition of moderately ill-structured tasks is described. As a method to realize scaffolding and intelligent support, information structure open approach where the domain model is represented as information structure and open for learners to direct manipulation are proposed. In this talk, using arithmetic word problems as an example of learning contents, (1) well-structured domain model of arithmetic word problems, and (2) design of moderately ill-structured task as “problem-posing assignment” based on the information structure model are introduced. Moreover, (3) implementation and practical uses of several intelligent learning environments that support learners to solve the moderately ill-structured tasks as problem-posing are reported.
A goal of educational technology since the 1930s has been to adapt teaching to the personal needs of each student. Significant developments have included programmed instruction, branched instruction, intelligent tutoring systems, and adaptive courseware. Personalized learning is coming back into prominence with the development of new techniques for linking learning analytics to adaptive teaching. Research challenges include how to enable personalization of informal and inquiry-led learning, and how to link personalization with learning through conversation and social networking. Personalized open learning must offer opportunities for students from widely differing backgrounds to learn in ways that match their needs and abilities. This requires new designs for flexibility of timing, pace, facilitation and assessment. For informal learning, personalization must align with changes in context, learning materials co-created by students, and self-directed study. In social networked learning, students need support to merge their individual pathways through the curriculum into shared goals, positive interdependence and productive conversation. I shall discuss recent work at The Open University on predictive analytics and flexible pathways for learners, as part of a strategic university initiative in personalized open learning. Our iSpot and nQuire-it platforms combine informal science learning with personalization through reputation management and student authoring. Adaptive crowdsourcing may offer mechanisms for personalized social networked learning.
Mixed realities that combine digital and real experiences are now becoming a true reality. These experiences are being delivered over smartphones as well as increasingly accessible and practical head mounted displays. This ubiquity of devices is in turn making mixed reality the next digital frontier in entertainment, education and the workplace. But what do we know about where these technologies have value? Where do they add to the learning experience? And what theories and evidence can we generate and build upon to provide a foundation for using these technologies productively for learning? We have been working on mixed realities in education for over a decade and have started to learn about where, when and for whom they can add value. Part of this understanding stems from differentiating the wide variety of mixed realities and focusing on affordances. Landscape based Augmented Realities, popularized by Pokemon Go, have fundamentally different affordances than smartphone based Virtual Realities like Google Cardboard, which in turn are different than immersive experiences delivered by headsets like the Oculus Rift and HTC Vive. The core of our work has been doing research and development to identify these affordances that match with key learning challenges, particular in Science, Technology, Engineering and Mathematics (STEM). In this talk, I will draw upon our work in location-based Augmented Reality games, as well as work in Virtual Reality. In the realm of Augmented Reality, I will discuss a long series of design experiments through which we have learned about where these technologies play an important role in learning, primarily around socio-scientific issues. In the space of Virtual Reality our newest designs and experiments focus on the concept of scale, and how we can use Virtual Realities to teach about STEM systems at radically different scales. This talk will provide a history and overview of these experiences, including iterations of design research experiments.
Collaborative, creative and critical literacies are essential to young people’s productive participation in 21st century lifeworlds. Yet, research has shown that the classroom can often be an uncomfortable and problematic space for developing such skills and dispositions. One major challenge lies in how we can more effectively assess and scaffold the development of these ‘new(er) literacies’ in learners, at both individual and collective levels, and as they occur naturalistically in peer interactions during acts of learning. With more appreciation for the dynamic and non-linear nature of 21st century literacies and their constitutive socio-interactional processes, educators worldwide are increasingly cognizant of the limitations of conventional assessment and pedagogic modalities. To this end, well-designed computer-supported collaborative learning (CSCL) environments that leverage on formative social learning analytics (LA) may bring new affordances to bear on this educational imperative of our time.
In this talk, I will showcase one techno-pedagogical innovation that exemplifies how the purposeful coupling of CSCL and formative LA affordances can serve to enhance collective creativity and critical literacies in secondary students within the disciplinary domain of English language learning. Alongside the explication of key design principles and empirical learning gains, I will also foreground the pedagogical dilemmas and challenges encountered throughout the iterative cycles of design, enactment, adoption and diffusion. In doing so, I hope to underscore both the educational promises and problems that arise as the ‘rubber’ of well-intentioned learning innovations ‘hits the road’ of entrenched socio-institutional beliefs and practices in mainstream schooling.
In this presentation, I will discuss ways to merge technology with effective and active language learning through the applications of Self-Regulated Learning (SRL) and Collaborative Learning (CL), focusing on learning support. There are four main parts to the presentation: (1) SRL support for course completion and dropout reduction and the limitations of SRL, (2) CL support to increase engagement, (3) bridging SRL and CL for quality interactions, and (4) current issues and future implications.
How do people behave and regulate their learning in TELL? When online learning materials were assigned with a due date, there were found to be seven learning types: (1) procrastination, (2) learning habit, (3) random, (4) diminished drive, (5) early bird, (6) chevron, and (7) catch-up. When the relationships between the learning types and their learning outcomes were analyzed, the results showed that the students with the learning habit type scored significantly higher on the test than did students with the procrastination type. These results imply that regulated learning could increase learning effectiveness and lead to better learning outcomes in e-learning. However, the problem is that most of us (generally 70 to 80 percent) are said to be procrastinators. Therefore, it is difficult to conclude that all procrastinators learn ineffectively. Then, the concepts of active and passive procrastination will be introduced.
The communicative approach has been encouraged for effective and active language learning. Not only SRL but CL is also essential to increase the size, depth, and fluency of language proficiency. To increase quality interactions among students, Community of Inquiry (CoI) was employed for quality interactions in our research projects, with increments of social, cognitive, and teaching presences. The design and support for effective CL and bridging SRL and CL will be presented through demos of our developed systems.
Finally, limitations of SRL, current issues with CL, and the combination of SRL and CL will be discussed. Innovation and creative skills are required as the 21st century skills. These skills should be applied to language learning, as well. The agenda for innovative TELL will be proposed for our future development.
As the use of digital technology in teaching and learning proliferates, teachers face challenges of how to choose appropriate tools and integrate them in their teaching. Teachers need explicit training in creating student-centred learning designs that harness the affordances of the technology, and in implementing them effectively in their classroom context. Several teacher professional development programs exist, but two key challenges are how to sustain results beyond the duration of the program, and how to scale such efforts.
In this talk, I will describe how we address the sustainability and scalability issues in teacher professional development for learner-centered technology integration practices. As part of Project TUET (Teacher Use of Educational Technology), a flagship project in the department of Educational Technology at IIT Bombay, we have developed models, training program designs and tools that empower teachers in effective technology integration practices within their own teaching-learning context. We have developed the A2I2 (Attain-Align-Integrate-Investigate) model and its associated design principles of immersivity, pertinency and transfer of ownership. We have implemented A2I2-based teacher professional development programs in face-to-face, blended and fully online modes both at the school level and higher education level. We have scaled up such programs upto 4000 participants via learner-centered MOOCs. I will present evidence of how teachers’ design expertise has evolved, and how some teachers have successfully transferred ownership of the problem of effective technology integration via action research in their own classroom. I will discuss our efforts towards building a community of practice over the past five years, and conclude with recommendations for designing and implementing large-scale and potentially sustainable teacher professional development programs.