Orchestration Graphs

This paper presents a method for visualizing students' learning logs using discrete graphs. These logs contain the following four items: attendance, time spent browsing slides, submission of a report and the quiz score for each lesson. The data were collected using learning management systems and the e-text systems. By using these data, we construct graphs for each grade of which the nodes represent all combinations of achievements and failures for the four items. The graphs enable us to observe the features of students' learning activities for each obtained grade. The order in which the above four items are presented changes the visual features of the graph. Moreover, the construction of a graph from the data of the same class held previously enables us to inform students of the learning activities they should avoid. Finally, future research plans regarding this method are presented.

A computer representation of teaching-learning processes in collaborative learning settings consists of modelling not only the sequence of learning activities and educational resources as existing learning design languages propose, but also modelling both the sequence of invocations of tools needed to carry out the learning activities and the flow of data among those tools. Existing data flow approaches only model data with activities but not data with tools. In this paper, we present LeadFlow4LD, a learning design and workflow-based method to achieve such a computational representation of collaborative learning processes in an interoperable and standard way. The proposed method has been assessed through the specification and enactment of a variety of non-trivial collaborative learning situations. The experimental results indicate that the level of expressiveness of the proposal is adequate in order to represent the flow of tools invocations and data which was missing in other existing research approaches.

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.

Lecture Notes in Computer Science, 2014

We propose a probabilistic graphical model for predicting student attainment in web-based education. We empirically evaluate our model on a crowdsourced dataset with students and teachers; Teachers prepared lessons on various topics. Students read lessons by various teachers and then solved a multiple choice exam. Our model gets input data regarding past interactions between students and teachers and past student attainment. It then estimates abilities of students, competence of teachers and difficulty of questions, and predicts future student outcomes. We show that our model's predictions are more accurate than heuristic approaches. We also show how demographic profiles and personality traits correlate with student performance in this task. Finally, given a limited pool of teachers, we propose an approach for using information from our model to maximize the number of students passing an exam of a given difficulty, by optimally assigning teachers to students. We evaluate the potential impact of our optimization approach using a simulation based on our dataset, showing an improvement in the overall performance. 1 See, for example the report on Peter Norvig and Sebastian Thrun's online artificial intelligence course, with its "100,000 student classroom", in norvig the 100 000 student classroom.html.

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

The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners' behaviors, and to identify critical tasks and course sessions in a given course design. We also introduce a recently developed probabilistic Bayesian model to learn sequence trajectories of students and predict student performance. The application of our data-driven sequence-based analyses to data from learners undertaking an on-line Business Management course reveals distinct behaviors within the cohort of learners, identifying learners or groups of learners that deviate from the nominal order expected in the course. Using course grades a posteriori, we explore differences in behavior between high and low performing learners. We find that high performing learners follow the progression between weekly sessions more regularly than low performing learners, yet within each weekly session high performing learners are less tied to the nominal task order. We then model the sequences of high and low performance students using the probablistic Bayesian model and show that we can learn engagement behaviors associated with performance. We also show that the data sequence framework can be used for task centric analysis; we identify critical junctures and differences among types of tasks within the course design. We find that non-rote learning tasks, such as interactive tasks or discussion posts, are correlated with higher performance. We discuss the application of such analytical techniques as an aid to course design, intervention, and student supervision.

This paper presents our research of a pedagogical model known as Knowledge Community and Inquiry (KCI), focusing on our design of a technological infrastructure for the orchestration of the complex CSCL scripts that characterize KCI curricula. We first introduce the KCI model including some basic design principles, and describe its dependency on real time learning analytics. Next, we describe our technology, known as SAIL Smart Space (S3), which provides scaffolding and analytic support of sequenced interactions amongst people, materials, tools and environments. We outline the critical role of the teacher in our designs and describe how S3 supports their active role in orchestration. Finally we outline two implementations of KCI/S3 and the role of learning analytics, in supporting dynamic collective visualizations, real time orchestrational logic, and ambient displays.