The Great Classroom Migration

Education is continuously changing and constantly adapting to philosophies and methods, even more so in the world of educational technology. The author's own professional path has taken her from chalk and blackboard, to advanced tools adapted to promote learning in both synchronous and asynchronous environments. Most recently in the last months, teachers all over the world were asked to become virtual teachers; teachers scrambled to convert their face-to-face classrooms into a virtual environment overnight. This sudden change from face-to-face to online learning was unprecedented and will have long lasting effects on K-12 education for many years to come. This chapter will outline the journey that was taken to convert a face-to-face class into the virtual environment at the American Community School of Athens, Greece.

Strategies and Tools for Promoting Discourse During Mathematics Problem-Solving in Online Settings

Inquiry-based mathematics instruction with collaborative reasoning and problem-solving necessitates opportunities for rich discourse as students make and test conjectures, explain their reasoning, and critique the reasoning of others. This discourse occurs in an environment where participants feel safe to try out ideas and learn from mistakes. Research in mathematics education includes many frameworks and strategies for encouraging discourse in face-to-face settings. Orchestrating such discourse presents a unique challenge in online settings where discourse usually takes the form of discussions about shared readings or experiences rather than collaborative problem-solving of a mathematical task. Examples of strategies and tools for orchestrating discourse during mathematics problem-solving in a graduate program for K-5 teachers that meets in both synchronous and asynchronous environments are shared. This is followed by a discussion of the affordances and constraints of supporting discourse in online settings. Finally, recommendations for instruction and directions for future research are suggested.

Fast and Robust Asynchronous Rendezvous Scheme for Cognitive Radio Networks

The rendezvous process is considered a key operation that allows a secondary user (SU) to access an unused authorized spectrum in cognitive radio networks (CRNs). Most existing works focused on fast guaranteed rendezvous without considering a sophisticated jamming attack environment. In this paper, I propose a fast and robust asynchronous rendezvous scheme that can improve robustness against jamming attacks under symmetric asynchronous environments in which all SUs have the same available channels. Unfortunately, in CRNs, each SU can have a different number of available channels due to their relative position to primary nodes (PUs). Therefore, I extend my fast and robust asynchronous rendezvous scheme (FRARS) to a general asymmetric scenario while preserving robustness against jamming attacks. I derive the maximum rendezvous time (MTTR) of my new algorithm and the upper bound of the expected TTR (ETTR) and compare it with the state-of-the-art algorithms such as jump-stay (JS) and Enhanced jump-stay (EJS). My numerical results show that the performance of the proposed technique is better than that of JS and EJS in terms of MTTR and ETTR. Also, the performance will be more significant when there are security concerns about a sophisticated jamming attack.

Student and Instructors Perceptions of Helpful Feedback for Asynchronous Online Learning

Research into online courses suggests that instructor feedback is effective for student learning when the feedback is personalized, specific, and timely. However, even with the technical ability to receive feedback any time at any location through varied devices, students still report dissatisfaction with instructor feedback. This chapter describes a mixed-methods study examining instructor and student perceptions of feedback in asynchronous environments. The study's results showed that students find personal and corrective feedback most helpful while instructors identified corrective and Socratic feedback as most helpful. Students characterized helpful feedback to be personalized, specific, and timely. Students also reported that grades were a form of feedback measuring stages of understanding but also an incentive for them to revise their work in order to achieve higher grades. The chapter includes strategies instructors can use for integrating each feedback type into their course design along with digital communication methods that enhance the student learning experience.

Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data

We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they allow new components to be introduced on the fly as needed. This, however, posts an important challenge to distributed estimation -- how to handle new components efficiently and consistently. To tackle this problem, we propose a new estimation method, which allows new components to be created locally in individual computing nodes. Components corresponding to the same cluster will be identified and merged via a probabilistic consolidation scheme. In this way, we can maintain the consistency of estimation with very low communication cost. Experiments on large real-world data sets show that the proposed method can achieve high scalability in distributed and asynchronous environments without compromising the mixing performance.

WORK DONE ON MIDDLEWARE INFRASTRUCTURE FOR PREDICATE DETECTION

Context-awareness is an essential feature of pervasive applications, and runtime detection of contextual properties is one of the primary approaches to enabling context awareness. However, existing context-aware middleware does not provide sufficient support for detection of contextual properties in asynchronous environments. The contextual activities usually involve multiple context collecting devices, which are fully-decentralized and interact in an asynchronous manner. However, existing context consistency checking schemes do not work in asynchronous environments, since they implicitly assume the availability of a global clock or relay on synchronized interactions. To this end, we present the Middleware Infrastructure for Predicate detection in Asynchronous environments (MIPA), which supports context awareness based on logical time. Design and Structure of MIPA are explained in detail.

MIDDLEWARE INFRASTRUCTURE FOR PREDICATE DETECTION IN ASYNCHRONOUS ENVIRONMENT

Context-awareness is an essential feature of pervasive applications, and runtime detection of contextual properties is one of the primary approaches to enabling context awareness. However, existing context-aware middleware does not provide sufficient support for detection of contextual properties in asynchronous environments. The contextual activities usually involve multiple context collecting devices, which are fully-decentralized and interact in an asynchronous manner. However, existing context consistency checking schemes do not work in asynchronous environments, since they implicitly assume the availability of a global clock or relay on synchronized interactions. To this end, we present the Middleware Infrastructure for Predicate detection in Asynchronous environments (MIPA), which supports context awareness based on logical time. Design and Structure of MIPA are explained in detail.