Orchestration Graphs

This dissertation investigates the design of large online courses from the pedagogical perspective of knowledge communities. Much of the learning sciences literature has concerned itself with groups of up to 20-30 students, but in universities, courses of several hundred to more than a thousand students are common. At the same time, new models for life-long and informal learning, such as Massive Open Online Courses, are emerging. Amidst this growing enthusiasm for innovation around technology and design in teaching, there is a need for theoretically grounded innovations and rigorous research around practical models that support new approaches to learning. One recent model, known as Knowledge Community and Inquiry (KCI), engages students in the co-construction of a community knowledge base, with a commonly held understanding of the collective nature of their learning, and then provides a sequence of scaffolded inquiry activities where students make use of the knowledge base as a resource. Inspired by this approach to designing courses, the research began with a redesign of an in-service teacher education course, which increased in size from 25 to 75 students. This redesign was carefully analyzed, and design principles extracted. The second step was the design of a Massive Open Online Course for several thousand in-service teachers on technology and inquiry, in collaboration with an affiliated secondary school. A number of innovative design ideas were necessary to accommodate the large number of users, the much larger diversity in terms of background, interest, and engagement among MOOC learners, and the opportunities provided by the platform. The resulting design encompasses a 6- week long curriculum script, and a number of overlapping micro-scripts supported by a custom- written platform that integrated with the EdX platform in a seamless manner. This thesis presents the course structure, including connection to disciplinary principles, its affordances for community and collaboration and its support of individual differentiated learning and collective epistemology. It offers design principles for scripting and orchestrating collective inquiry designs for MOOCS and higher education courses.

2017 IEEE Frontiers in Education Conference (FIE), 2017

— In industry, professionals often work with a variety of stakeholders and collaborators from multiple disciplines. This ability to work collaboratively can be as important to a project's success as their technical skills. Traditionally in STEM education, these collaborative skills are developed in a capstone course which mimics an industry experience. These experiences are invaluable in preparing students for the collaborative real-world nature of industry; however, these experiences can also be very stressful for students in dysfunctional teams with members who haven't developed necessary social, technical or teamwork skills. Although students may be exposed to some team-based activities in previous courses, it is not clear that this piecemeal exposure teaches students to work in teams effectively. Flipped classroom and active learning attempt to fill this gap by exposing students to peer learning earlier in the curriculum. However, these techniques are peppered throughout the curriculum and may not target all the skills necessary for teamwork. Design patterns in education formalize pedagogical approaches. But, applying design patterns without an intended progression or overarching goal may not lead students to successfully adopt these skills. Design patterns have the potential to scaffold students' development throughout the curriculum, but only if staged effectively and systematically. In this paper, we propose Spectrums and Dependency Graphs to ensure that students are prepared for each new design pattern as they experience it. Spectrums can plot design patterns along a continuum between introductory and capstone courses. Dependency graphs recursively specify patterns that prepare students for subsequent patterns. Each pattern will contain prerequisite skills or experiences that students have demonstrated in a previous pattern. In this way, students are systematically progressed from introductory to capstone courses. Through these two models, we attempt to get a better overview of the curriculum and create progressions through that curriculum that ensure students are prepared at each level, building on previous skills.

International Journal of Education and Learning, 2022

In recent years, the interest in Massive Open Online Courses (MOOCs) and Learning Analytics research have highly increased in the areas of educational technologies. The emergence of new learning technologies requires new perspectives on Educational Design. When the areas of MOOCs, Learning Analytics and Instructional Design developed, the interest and connection between these three concepts became important for research. Learning Analytics provides progress information and other individualized support in MOOC settings where teachers are not able to provide learners with individual attention, which would be possible in a traditional face-to-face setting. Through collective views over the learning process, the overall progress and performance are indicated. Moreover, results can lead to Educational Design improvements. Every time a learner interacts with the system, data is created and collected. Many Educational Designers do not take advantage of this data and thereby, losing the possibility to impact the course design in a powerful way. This research work strongly focuses on the implication of Learning Analytics for Educational Design in MOOCs. Many methods and algorithms are used in the analytical learning process in MOOCs. Currently, a great variety of learning data exists. First, well-known Instructional Design patterns from different models were collected and listed. In a second step, through the collected data is used to point out which of these patterns can be answered by using Learning Analytics methods. The findings of the study show that it is possible to better understand which environments and experiences are best suited for learning by analyzing students' behaviors online. These results have great potential for a rapidly and easier understanding and optimization of the learning process for educators.

arXiv (Cornell University), 2020

2014