Collaboration in the Semantic Grid: a Basis for e-Learning

The CoAKTinG project aims to advance the state of the art in collaborative mediated spaces for the Semantic Grid. This paper presents an overview of the hypertext and knowledge based tools which have been deployed to augment existing collaborative environments, and the ontology which is used to exchange structure, promote enhanced process tracking, and aid navigation of resources before, after, and while a collaboration occurs. While the primary focus of the project has been supporting e-Science, this paper also explores the similarities and application of CoAKTinG technologies as part of a human-centred design approach to e-Learning

UNDERSTANDING THE PARADOX OF MENTAL EFFORT IN ONLINE LEARNING CONVERSATIONS

This study investigates inquiry-based interaction and learning outcomes mediated by two types of artifact-centered discourse environments. The study aims to promote social construction of knowledge by optimizing the division of mental effort between pragmatic and semantic grounding activities. We present a theoretical research model by combining social constructivism, grounding theory, and cognitive load theory. We carried out a quasi-experimental study using survey instruments, content analysis, sequential analysis, and knowledge tests for a holistic approach to understand the paradox of mental effort in online learning conversations. The primary finding of this study is that a linked artifact-centered discourse environment facilitates pragmatic grounding activities to attain a common ground in online learning conversations. Additionally, less need for pragmatic grounding activities leaves more room for semantic grounding activities. Finally, more semantic grounding activities lead to a deeper understanding of the learning material

A Pedagogical Application Framework for Synchronous Collaboration

Designing successful collaborative learning activities is a new focus of research within the E-Learning community. The social dimension inside the traditional face-to-face collaborative learning is important and must be included in the online learning designs. In this thesis, we introduce the concept of Pedagogical Application Frameworks, and describe Beehive, a pedagogical application framework for synchronous collaborative learning. Beehive guides teachers in reusing online collaborative learning activities based on well-known pedagogical designs, to accomplish their educational objectives within a certain educational setting, and also simplifies the development of new pedagogical collaboration designs. Beehive’s conceptual model has four abstraction layers: Pedagogical Techniques, Collaboration Task patterns, CSCL Components, and CSCL script. By following the framework’s guidelines and specifications, developers will place the control of designing pedagogical collaboration tools in the teacher’s hand rather than in the software designer’s

A Pedagogical Application Framework for Synchronous Collaboration

Designing successful collaborative learning activities is a new focus of research within the E-Learning community. The social dimension inside the traditional face-to-face collaborative learning is important and must be included in the online learning designs. In this thesis, we introduce the concept of Pedagogical Application Frameworks, and describe Beehive, a pedagogical application framework for synchronous collaborative learning. Beehive guides teachers in reusing online collaborative learning activities based on well-known pedagogical designs, to accomplish their educational objectives within a certain educational setting, and also simplifies the development of new pedagogical collaboration designs. Beehive’s conceptual model has four abstraction layers: Pedagogical Techniques, Collaboration Task patterns, CSCL Components, and CSCL script. By following the framework’s guidelines and specifications, developers will place the control of designing pedagogical collaboration tools in the teacher’s hand rather than in the software designer’s

Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

© 2019, Copyright © 2017 Taylor & Francis Group, LLC. Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers

Enhancing Free-text Interactions in a Communication Skills Learning Environment

Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs