Assessing a Collaborative Online Environment for Music Composition

The current pilot study tested the effectiveness of an e-learning environment built to enable students to compose music collaboratively. The participants interacted online by using synchronous and asynchronous resources to develop a project in which they composed a new music piece in collaboration. After the learning sessions, individual semi-structured interviews with the participants were conducted to analyze the participants\u2019 perspectives regarding the e-learning environment\u2019s functionality, the resources of the e-learning platform, and their overall experience with the e-learning process. Qualitative analyses of forum discussions with respect to metacognitive dimensions, and semi-structured interview transcriptions were performed. The findings showed that the participants successfully completed the composition task in the virtual environment, and that they demonstrated the use of metacognitive processes. Moreover, four themes were apparent in the semi-structured interview transcriptions: Teamwork, the platform, face-to-face/online differences, and strengths/weaknesses. Overall, the participants exhibited an awareness of the potential of the online tools, and the task performed. The results are discussed in consideration of metacognitive processes, and the following aspects that rendered virtual activity effective for learning: The learning environment, the platform, the technological resources, the level of challenge, and the nature of the activity. The possible implications of the findings for research on online collaborative composition are also considered

Intelligent e-Learning Systems: An Educational Paradigm Shift

Learning is the long process of transforming information as well as experience into knowledge, skills, attitude and behaviors. To make up the wide gap between the demand of increasing higher education and comparatively limited resources, more and more educational institutes are looking into instructional technology. Use of online resources not only reduces the cost of education but also meet the needs of society. Intelligent e-learning has become one of the important channels to reach out to students exceeding geographic boundaries. Besides this, the characteristics of e-learning have complicated the process of education, and have brought challenges to both instructors and students. This paper will focus on the discussion of different discipline of intelligent e-learning like scaffolding based e-learning, personalized e-learning, confidence based e-learning, intelligent tutoring system, etc. to illuminate the educational paradigm shift in intelligent e-learning system

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Diseño de un sistema difuso para valoración de aportes en sistemas colaborativos

Actualmente los consumidores no son pasivos, están altamente conectados y vinculados con las empresas. Por ello, algunas empresas aprovechan estas características para innovar productos a partir de los aportes de clientes. La co - creacióncomo modelo colaborativo para la innovación se caracteriza por estar dividida en etapas relacionadas entre sí, con el fin de capturar apropiadamente los aportes de los clientes. La capacidad de valorar y clasificar los aportes de los agentes que participanen este proceso colaborativo es una tarea de crucial importancia para las organizaciones. El presente artículo propone un sistema difuso que permite valorar los aportes de los agentes que participan en forma colaborativa en la co - creación de productos y servicios

Involving Users to Improve the Collaborative Logical Framework

In order to support collaboration in web-based learning, there is a need for an intelligent support that facilitates its management during the design, development, and analysis of the collaborative learning experience and supports both students and instructors. At aDeNu research group we have proposed the Collaborative Logical Framework (CLF) to create effective scenarios that support learning through interaction, exploration, discussion, and collaborative knowledge construction. This approach draws on artificial intelligence techniques to support and foster an effective involvement of students to collaborate. At the same time, the instructors’ workload is reduced as some of their tasks—especially those related to the monitoring of the students behavior—are automated. After introducing the CLF approach, in this paper, we present two formative evaluations with users carried out to improve the design of this collaborative tool and thus enrich the personalized support provided. In the first one, we analyze, following the layered evaluation approach, the results of an observational study with 56 participants. In the second one, we tested the infrastructure to gather emotional data when carrying out another observational study with 17 participants

Web 2.0 technologies for learning: the current landscape – opportunities, challenges and tensions

This is the first report from research commissioned by Becta into Web 2.0 technologies for learning at Key Stages 3 and 4. This report describes findings from an additional literature review of the then current landscape concerning learner use of Web 2.0 technologies and the implications for teachers, schools, local authorities and policy makers

Human-AI symbiosis: The best approach for AI implementation in business decision-making in complex systems

In today's business landscape, there is significant discourse surrounding the role of Artificial Intelligence (AI) in various aspects of business operations. Decision-making, in particular, is a crucial component of every business-related activity. As businesses expanded and generated massive amounts of data, it became clear that humans alone could no longer make consistently accurate decisions. Moreover, it is demonstrated that humans often rely on heuristics and cognitive biases in their decision-making, leading to suboptimal outcomes. Given today's business environment's complexity, instability, and interconnected nature, businesses possess all the characteristics of complex systems. With the aid of AI, decision-making can be significantly enhanced. Various subfields of AI, such as artificial neural networks, fuzzy logic networks, and agents, have been developed in recent years, playing a pivotal role in enabling AI-driven decision-making. Findings through using purposeful and complex systems suggest that although AI subfields in decision-making can make sound decisions, they exhibit deficiencies in complex systems where human interaction and interconnectedness across different organizational levels are present. Currently, AI technology is not equipped to address these challenges. As a result, the decision-making process should not be entirely delegated to machines and AI. This discussion gives rise to the duality of augmentation and automation. Decision-making can be categorized into three levels: operational, tactical, and strategic, ranging from structured to unstructured decisions. The analysis reveals that AI performs admirably as an assistant or replacement tool at the operational level. However, as moving towards tactical and strategic decisions, although its augmentation abilities remain somewhat consistent, its capabilities for replacement and automation diminish significantly. Consequently, AI is believed to lack the ability to automate strategic and unstructured business decisions completely

Artificial Intelligence Approaches in Student Modeling: Half Decade Review (2010-2015)

Intelligent Tutoring Systems (ITSs) are special classes of E-learning systems designed using Artificial Intelligence (AI) approaches to provide adaptive and personalized tutoring based on the individuality of students. The student model is an important component of an ITS that provides the base for this personalization. During the course of interaction between student and the ITS, the system observe student’s actions and other behavioral properties, create a quantitative representation of these student’s attributes called a student model