The Grid 2: Blueprint for a New Computing Infrastructure

Learning analytics is a significant area of technology-enhanced learning that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learning-focused perspectives and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining and academic analytics. Finally, it examines developing areas of learning analytics research, and identifies a series of future challenges.

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Learning analytics: drivers, developments and challenges

Argumentation in artificial intelligence

Computers have transformed every facet of our culture, most dramatically communication, transportation, finance, science, and the economy. Yet their impact has not been generally felt in education due to lack of hardware, teacher training, and sophisticated software. Another reason is that current instructional software is neither truly responsive to student needs nor flexible enough to emulate teaching. The more instructional software can reason about its own teaching process, know what it is teaching, and which method to use for teaching, the greater is its impact on education. 

Building Intelligent Interactive Tutors discusses educational systems that assess a student's knowledge and are adaptive to a student's learning needs. Dr. Woolf taps into 20 years of research on intelligent tutors to bring designers and developers a broad range of issues and methods that produce the best intelligent learning environments possible, whether for classroom or life-long learning. The book describes multidisciplinary approaches to using computers for teaching, reports on research, development, and real-world experiences, and discusses intelligent tutors, web-based learning systems, adaptive learning systems, intelligent agents and intelligent multimedia.

*Combines both theory and practice to offer most in-depth and up-to-date treatment of intelligent tutoring systems available
*Presents powerful drivers of virtual teaching systems, including cognitive science, artificial intelligence, and the Internet
*Features algorithmic material that enables programmers and researchers to design building components and intelligent systems

Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning

We propose that the design and implementation of effective Social Learning Analytics (SLA) present significant challenges and opportunities for both research and enterprise, in three important respects. The first is that the learning landscape is extraordinarily turbulent at present, in no small part due to technological drivers. Online social learning is emerging as a significant phenomenon for a variety of reasons, which we review, in order to motivate the concept of social learning. The second challenge is to identify different types of SLA and their associated technologies and uses. We discuss five categories of analytic in relation to online social learning; these analytics are either inherently social or can be socialised. This sets the scene for a third challenge, that of implementing analytics that have pedagogical and ethical integrity in a context where power and control over data are now of primary importance. We consider some of the concerns that learning analytics provoke, and suggest that Social Learning Analytics may provide ways forward. We conclude by revisiting the drivers and trends, and consider future scenarios that we may see unfold as SLA tools and services mature.

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Social learning analytics

Drawing on sociocultural discourse analysis and argumentation theory, we motivate a focus on learners' discourse as a promising site for identifying patterns of activity which correspond to meaningful learning and knowledge construction. However, software platforms must gain access to qualitative information about the rhetorical dimensions to discourse contributions to enable such analytics. This is difficult to extract from naturally occurring text, but the emergence of more-structured annotation and deliberation platforms for learning makes such information available. Using the Cohere web application as a research vehicle, we present examples of analytics at the level of individual learners and groups, showing conceptual and social network patterns, which we propose as indicators of meaningful learning.

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Discourse-centric learning analytics

This paper describes the work undertaken in the Scholarly Ontologies Project. The aim of the project has been to develop a computational approach to support scholarly sensemaking, through interpretation and argumentation, enabling researchers to make claims: to describe and debate their view of a document's key contributions and relationships to the literature. The project has investigated the technicalities and practicalities of capturing conceptual relations, within and between conventional documents in terms of abstract ontological structures. In this way, we have developed a new kind of index to distributed digital library systems. This paper reports a case study undertaken to test the sensemaking tools developed by the Scholarly Ontologies project. The tools used were ClaiMapper, which allows the user to sketch argument maps of individual papers and their connections, ClaiMaker, a server on which such models can be stored and saved, which provides interpretative services to assist the querying of argument maps across multiple papers and ClaimFinder, a novice interface to the search services in ClaiMaker.

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Sensemaking tools for understanding research literatures: Design, implementation and user evaluation

Blockchain technology has the potential to revolutionise education in a number of ways. In this paper, we explore the applications of Smart Blockchain Badges on data science education. In particular, we investigate how Smart Blockchain Badges can support learners that want to advance their careers in data science, by offering them personalised recommendations based on their learning achievements. This work aims at enhancing data science accreditation by introducing a robust system based on the Blockchain technology. Learners will benefit from a sophisticated, open and transparent accreditation system, as well as from receiving job recommendations that match their skills and can potentially progress their careers. As a result, this work contributes towards closing the data science skills gap by linking data science education to the industry.

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Smart Blockchain Badges for Data Science Education

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 that have been deployed to augment exiting collaborative environments, and the ontology that is used to exchange structure, promote enhanced process tracking, and aid navigation of resources before, after, and during a collaboration. 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-centered design approach to e-Learning.

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COLLABORATION IN THE SEMANTIC GRID: A BASIS FOR e-LEARNING

This paper introduces the Memetic toolkit for recording the normally ephemeral interactions conducted via internet video conferencing, and making these navigable and manipulable in linear and non-linear ways. We introduce two complementary interaction visualizations: argumentation-based concept maps to elucidate the conceptual structure of the discourse using a visual language, and interactive event timelines generated from the meeting metadata. We discuss in detail the affordances of Memetic's tools, in particular the Compendium hypermedia mapping tool, and the Meeting Replay tool that renders the semantic navigation indices into the videoconference replays. Additionally, with respect to methodology and evaluation, we describe how we are engaging diverse end-user communities in the process of designing and deploying these tools.

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Michelle Bachler

Papers

Discourse-centric learning analytics

Sensemaking tools for understanding research literatures: Design, implementation and user evaluation

Smart Blockchain Badges for Data Science Education

COLLABORATION IN THE SEMANTIC GRID: A BASIS FOR e-LEARNING

Memetic: An Infrastructure for Meeting Memory