An integrated approach for analysing and assessing the performance of virtual learning groups

Collaborative distance learning involves a variety of elements and factors that have to be considered and measured in order to analyse and assess group and individual performance more effectively and objectively. This paper presents an approach that integrates qualitative, social network analysis (SNA) and quantitative techniques for evaluating online collaborative learning interactions. Integration of various different data sources, tools and techniques provides a more complete and robust framework for group modelling and guarantees a more efficient evaluation of group effectiveness and individual competence. Our research relies on the analysis of a real, long-term, complex collaborative experience, which is initially evaluated in terms of principled criteria and a basic qualitative process. At the end of the experience, the coded student interactions are further analysed through the SNA technique to assess participatory aspects, identify the most effective groups and the most prominent actors. Finally, the approach is contrasted and completed through a statistical technique which sheds more light on the results obtained that far. The proposal draws a well-founded line toward the development of a principled framework for the monitoring and analysis of group interaction and group scaffolding which can be considered a major issue towards the actual application of the CSCL proposals to real classrooms.Peer ReviewedPostprint (author's final draft

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Learn-ciam: a model-driven approach for the development of collaborative learning tools

This paper introduces Learn-CIAM, a new model-based methodological approach for the design of flows and for the semi-automatic generation of tools in order to support collaborative learning tasks. The main objective of this work is to help professors by establishing a series of steps for the specification of their learning courses and the obtaining of collaborative tools to support certain learning activities (in particular, for in-group editing, searching and modeling). This paper presents a complete methodological framework, how it is supported conceptually and technologically, and an application example. So to guarantee the validity of the proposal, we also present some validation processes with potential designers and users from different profiles such as Education and Computer Science. The results seem to demonstrate a positive reception and acceptance, concluding that its application would facilitate the design of learning courses and the generation of collaborative learning tools for professionals of both profiles

Meta-Governance Framework to Guide the Establishment of Mass Collaborative Learning Communities

UIDB/00066/2020 ERASMUS +619130-EPP-1-2020-1-FR-EPPKA2-CBHE-JPThe application of mass collaboration in different areas of study and work has been increasing over the last few decades. For example, in the education context, this emerging paradigm has opened new opportunities for participatory learning, namely, “mass collaborative learning (MCL)”. The development of such an innovative and complementary method of learning, which can lead to the creation of knowledge-based communities, has helped to reap the benefits of diversity and inclusion in the creation and development of knowledge. In other words, MCL allows for enhanced connectivity among the people involved, providing them with the opportunity to practice learning collectively. Despite recent advances, this area still faces many challenges, such as a lack of common agreement about the main concepts, components, applicable structures, relationships among the participants, as well as applicable assessment systems. From this perspective, this study proposes a meta-governance framework that benefits from various other related ideas, models, and methods that together can better support the implementation, execution, and development of mass collaborative learning communities. The proposed framework was applied to two case-study projects in which vocational education and training respond to the needs of collaborative education–enterprise approaches. It was also further used in an illustration of the MCL community called the “community of cooks”. Results from these application cases are discussed.publishersversionpublishe

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Active E-Learning by Doing with ALDO

It has been proved how teaching and learning educational processes can largely benefit from the application of ICT-based services within e-learning platforms, such as collaborative editing and advanced data visualizations. However, among state-of-the-art solutions, no one is able to tackle the problem in a comprehensive way. In this extended abstract, we discuss ALDO (Active e-Learning by DOing), a novel, advanced digital framework supporting integrated facilities for effective, active e-learning. ALDO includes an active repository for collecting, sharing, retrieving, and analyzing relevant materials, collaborative editing services, an e-learning platform, and advanced visualization tools to inspect the spatial and temporal dimension of specific data contexts. All such services and tools are made available to teachers/students through a dedicated Web portal. Although the present research was carried out within the H2020 Project DETECt (Detecting Transcultural Identity in European Popular Crime Narratives), by focusing on the specific data context of European crime narrative, the generality of the framework makes it suitable for any type of educational task. The design and creation of above tools and services, together with their uses, are presented and discussed through a series of real examples taken from DETECt

A Research Framework for Collaborative eLearning in an End User Training Context

The growth in the application of information technology to end-user training (EUT) underscores a fundamental need to understand how these e-learning technologies improve the learning process. Results from the limited EUT studies provide an inconsistent picture of the effects of e-learning technologies. Also, collaborative learning has become one of the most used techniques in American education, yet, we could find only one EUT study on collaborative learning. This paper applies adaptive structuration theory (AST) to the specific area of technology-mediated end-user training. The main focus is on understanding learning, collaboration and technology structures involved, their interactions and appropriation, and their impact on learning outcomes. By integrating social cognitive and social development theories into the AST framework, the model presented investigates both the learning process and functional/structural aspects of technology-mediated end-user training. Propositions are developed for future empirical testing

Communication partner training: re-imagining community and learning

Background: Learning is integral to Communication Partner Training (CPT) initiatives. Key theories include experiential learning and adult learning theory. The ways in which these have been applied, however, do not consistently address the needs of people with aphasia and other stakeholders in CPT. Participatory, relational, and collaborative approaches, subsumed within an expansive learning framework, which provides theoretical principles and scope for critical examination of the “who”, “why”, “what”, and “how” of learning have the potential to address these shortcomings. Aims: The objective of this paper is to critically review experiential and adult learning in CPT, subsequently examining participatory and relational approaches within the framework of expansive learning, using an example from a health-care context. Main contribution: Expansive learning is described, and its potential application examined through an example of CPT in a healthcare context and critical discussion of the literature. Conclusions: Expansive learning provides a sound theoretical and practical basis for CPT initiatives across a range of contexts, and enhances our understanding of how to achieve goals of communicative access and social participation