Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Speech-plans: Generating evaluative responses in spoken dialogue

Recent work on evaluation of spoken dialogue systems indicates that better algorithms are needed for the presentation of complex information in speech. Current dialogue systems often rely on presenting sets of options and their attributes sequentially. This places a large memory burden on users, who have to remember complex trade-offs between multiple options and their attributes. To address these problems we build on previous work using multiattribute decision theory to devise speech-planning algorithms that present usertailored summaries, comparisons and recommendations that allow users to focus on critical differences between options and their attributes. We discuss the differences between speech and text planning that result from the particular demands of the speech situation.

Communicating Resilience: A Discursive Leadership Perspective

In this essay we challenge whether current conceptions of optimism, hope, and resilience are complete enough to account for the complexity and nuance of developing and maintaining these in practice. For example, a quick perusal of popular outlets (e.g., Forbes, Harvard Business Review) reveals advice to managers urging them to “be optimistic,” or “be happy” so that these types of emotions or feelings can spread to the workplace. One even finds simple advice and steps to follow on how to foster these types of things in the workplace (McKee; Tjan). We argue that this common perspective focuses narrowly on individuals and does not account for the complexity of resilience. Consequently, it denies the role of context, culture, and interactions as ways people develop shared meaning and reality. To fill this gap in our understanding, we take a social constructionist perspective to understand resilience. In other words, we foreground communication as the primary building block to sharing meaning and creating our worlds. In so doing, we veer away from the traditional focus on the individual and instead emphasise the social and cultural elements that shape how meaning is shared by peoples in various contexts (Fairhurst, Considering Context). Drawing on a communication, discourse-centered perspective we explore hope and optimism as concepts commonly associated with resilience in a work context. At work, leaders play a vital role in communicating ways that foster resilience in the face of organisational issues and events (e.g., environmental crises, downsizing). Following this lead, discursive leadership offers a framework that positions leadership as co-created and as the management of meaning through framing (Fairhurst, Power of Framing). Thus, we propose that a discursive leadership orientation can contribute to the communicative construction of resilience that moves away from individual perspectives to an emphasis on the social. From a discursive perspective, leadership is defined as a process of meaning management; attribution given by followers or observers; process-focused rather than leader-focused; and as shifting and distributed among several organizational members (Fairhurst Power of Framing). By switching from the individual focus and concentrating on social and cultural systems, discursive leadership is able to study concepts related to subjectivity, cultures, and identities as it relates to meaning. Our aim is to offer leaders an alternative perspective on resilience at the individual and group level by explaining how a discursive orientation to leadership can contribute to the communicative construction of resilience. We argue that a social constructionist approach provides a perspective that can unravel the multiple layers that make up hope, optimism, and resilience. We begin with a peek into the social scientific perspective that is so commonplace in media and popular portrayals of these constructs. Then, we explain the social constructionist perspective that grounds our framework, drawing on discursive leadership. Next, we present an alternative model of resilience, one that takes resilience as communicatively constructed and socially created. We believe this more robust perspective can help individuals, groups, and cultures be more resilient in the face of challenges

Development of a Trust-Aware User Simulator for Statistical Proactive Dialog Modeling in Human-AI Teams

The concept of a Human-AI team has gained increasing attention in recent years. For effective collaboration between humans and AI teammates, proactivity is crucial for close coordination and effective communication. However, the design of adequate proactivity for AI-based systems to support humans is still an open question and a challenging topic. In this paper, we present the development of a corpus-based user simulator for training and testing proactive dialog policies. The simulator incorporates informed knowledge about proactive dialog and its effect on user trust and simulates user behavior and personal information, including socio-demographic features and personality traits. Two different simulation approaches were compared, and a task-step-based approach yielded better overall results due to enhanced modeling of sequential dependencies. This research presents a promising avenue for exploring and evaluating appropriate proactive strategies in a dialog game setting for improving Human-AI teams.Comment: Preprint Version submitted to ACM UMA

Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking

As an essential component in task-oriented dialogue systems, dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing the dialogue. Representations of dialogue states are dependent on the domain ontology and the user's goals. In several task-oriented dialogues with a limited scope of objectives, dialogue states can be represented as a set of slot-value pairs. As the capabilities of dialogue systems expand to support increasing naturalness in communication, incorporating dialogue act processing into dialogue model design becomes essential. The lack of such consideration limits the scalability of dialogue state tracking models for dialogues having specific objectives and ontology. To address this issue, we formulate and incorporate dialogue acts, and leverage recent advances in machine reading comprehension to predict both categorical and non-categorical types of slots for multi-domain dialogue state tracking. Experimental results show that our models can improve the overall accuracy of dialogue state tracking on the MultiWOZ 2.1 dataset, and demonstrate that incorporating dialogue acts can guide dialogue state design for future task-oriented dialogue systems.Comment: Published in Spoken Dialogue Systems I, Interspeech 2021. Code is now publicly available on Github: https://github.com/youlandasu/ACT-AWARE-DS