Investigating Students' Experiences with Collaboration Analytics for Remote Group Meetings.

Remote meetings have become the norm for most students learning synchronously at a distance during the ongoing coronavirus pandemic. This has motivated the use of artificial intelligence in education (AIED) solutions to support the teaching and learning practice in these settings. However, the use of such solutions requires new research particularly with regards to the human factors that ultimately shape the future design and implementations. In this paper, we build on the emerging literature on human-centred AIED and explore students’ experiences after interacting with a tool that monitors their collaboration in remote meetings (i.e., using Zoom) during 10 weeks. Using the social translucence framework, we probed into the feedback provided by twenty students regarding the design and implementation requirements of the system after their exposure to the tool in their course. The results revealed valuable insights in terms of visibility (what should be made visible to students via the system), awareness (how can this information increase students’ understanding of collaboration performance), and accountability (to what extent students take responsibility of changing their behaviours based on the system’s feedback); as well as the ethical and privacy aspects related to the use of collaboration analytics tools in remote meetings. This study provides key suggestions for the future design and implementations of AIED systems for remote meetings in educational settings

Understanding the implications of Social Translucence for systems supporting communication at work

In this paper we describe a study that explored the implications of the Social Translucence framework for designing systems that support communications at work. Two systems designed for communicating availability status were empirically evaluated to understand what constitutes a successful way to achieve Visibility of people's communicative state. Some aspects of the Social Translucence constructs: Visibility, Awareness and Accountability were further operationalized into a questionnaire and tested relationships between these constructs through path modeling techniques. We found that to improve Visibility systems should support people in presenting their status in a contextualized yet abstract manner. Visibility was also found to have an impact on Awareness and Accountability but no significant relationship was seen between Awareness and Accountability. We argue that to design socially translucent systems it is insufficient to visualize people's availability status. It is also necessary to introduce mechanisms stimulating mutual Awareness that allow for maintaining shared, reciprocical knowledge about communicators' availability state, which then can encourage them to act in a socially responsible way

Understanding the implications of social translucence for systems supporting communication at work

In this paper we describe a study that explored the implications of the Social Translucence framework for designing systems that support communications at work. Two systems designed for communicating availability status were empirically evaluated to understand what constitutes a successful way to achieve Visibility of people's communicative state. Some aspects of the Social Translucence constructs: Visibility, Awareness and Accountability were further operationalized into a questionnaire and tested relationships between these constructs through path modeling techniques. We found that to improve Visibility systems should support people in presenting their status in a contextualized yet abstract manner. Visibility was also found to have an impact on Awareness and Accountability but no significant relationship was seen between Awareness and Accountability. We argue that to design socially translucent systems it is insufficient to visualize people's availability status. It is also necessary to introduce mechanisms stimulating mutual Awareness that allow for maintaining shared, reciprocical knowledge about communicators' availability state, which then can encourage them to act in a socially responsible way

An Analytics Based Architecture and Methodology for Collaborative Timetabling in Higher Education

Class scheduling in higher education, also known as “timetabling”, is a complex process that involves many people across an institution for several months every year, and literature on the topic has been rapidly evolving over the last 15 years. We propose architecture and methodology to enable the implementation of systems that can help users gain insight on non-trivial existing and emerging enrollment patterns that need to be considered for planning purposes, and to facilitate collaborative timetabling activities. University of Pittsburgh data on undergraduate enrollments during six recent fall terms is used to illustrate the proposed ideas. Core components are specified by: First, modeling the problem using Association Rule Analysis where the sets of courses that individual students take in an academic term are treated as transactions. This renders combinations of courses called itemsets. A new backtracking algorithm called MASAI is proposed to determine the maximum number of seats available per itemset. This corresponds to the identification of itemsets of interest as in the case at hand course itemsets with no seats available are primary targets. MASAI is a novel approach to the identification of itemsets of interest that uses information that is not available in transactional data to determine the maximum number of seats possible in each itemset. Second, in order to facilitate deeper analyses that consider the relationships between course itemsets, the problem is modeled as a multi-mode graph that incorporates information obtained with the Association Rule Analysis and MASAI. A Generalized Clique Percolation Method (GCPM) is proposed to enable the identification of overlapping and hierarchical communities in graphs/networks. GCPM is used to identify communities in the multi-mode graph, enabling the discovery of non-trivial enrollment patterns, and the identification of scheduling practices that limit the enrollment options for students. Third, the elements that would form the core of a socially translucent environment that is based on the previous components are discussed. This collaborative environment is intended to provide scheduling authorities with access to shared information on enrollment patterns and how decisions on scheduling of courses in their departments impact the overall institution’s schedule and the enrollment options for students

Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence

Health coaching is a promising approach to support self-management of chronic conditions like type 2 diabetes; however, there aren’t enough coaching practitioners to support those in need. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to enable innovative, automated health coaching interventions, but important gaps remain in applying AI and ML to coaching interventions. This thesis aims to identify computational approaches and interactive technologies that enable automated health coaching systems. First, I utilized computational approaches that leverage individuals’ self-tracking and health data and used an expert system to translate ML inferences into personalized nutrition goal recommendations. The system, GlucoGoalie, was evaluated in multiple studies including a 4-week deployment study which demonstrated the feasibility of the approach. Second, I compared human-powered and automated/chatbot approaches to health coaching in a 3-week study which found that t2.coach — a scripted, theoretically-grounded chatbot designed through an iterative, user-centered process — cultivated a coach-like experience that had many similarities to the experience of messaging with actual health coaches, and outlined directions for automated, conversational coaching interventions. Third, I examined multiple AI approaches to enable micro-coaching dialogs — brief coaching conversations related to specific meals, to support achievement of nutrition goals — including a knowledge-based system for natural language understanding, and a data-driven, reinforcement learning approach for dialog management. Together, the results of these studies contribute methods and insights that take steps towards more intelligent conversational coaching systems, with resonance to research in informatics, human-computer interaction, and health coaching