39,786 research outputs found
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
A Literature Review on Intelligent Services Applied to Distance Learning
Distance learning has assumed a relevant role in the educational scenario. The use of
Virtual Learning Environments contributes to obtaining a substantial amount of educational data.
In this sense, the analyzed data generate knowledge used by institutions to assist managers and
professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide
variety of intelligent services for assisting in the learning process. This article presents a literature
review in order to identify the intelligent services applied in distance learning. The research covers
the period from January 2010 to May 2021. The initial search found 1316 articles, among which
51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning
systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems
or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the
principal services offered are recommendation systems and learning systems. In these services, the
analysis of student profiles stands out to identify patterns of behavior, detect low performance, and
identify probabilities of dropouts from courses.info:eu-repo/semantics/publishedVersio
A Hybrid Recommender Strategy on an Expanded Content Manager in Formal Learning
The main topic of this paper is to find ways to improve learning in a formal Higher Education Area. In this environment, the teacher publishes or suggests contents that support learners in a given course, as supplement of classroom training. Generally, these materials are pre-stored and not changeable. These contents are typically published in learning management systems (the Moodle platform emerges as one of the main choices) or in sites created and maintained on the web by teachers themselves. These scenarios typically include a specific group of students (class) and a given period of time (semester or school year). Contents reutilization often needs replication and its update requires new edition and new submission by teachers. Normally, these systems do not allow learners to add new materials, or to edit existing ones.
The paper presents our motivations, and some related concepts and works. We describe the concepts of sequencing and navigation in adaptive learning systems, followed by a short presentation of some of these systems. We then discuss the effects of social interaction on the learners’ choices. Finally, we refer some more related recommender systems and their applicability in supporting learning.
One central idea from our proposal is that we believe that students with the same goals and with similar formal study time can benefit from contents' assessments made by learners that already have completed the same courses and have studied the same contents. We present a model for personalized recommendation of learning activities to learners in a formal learning context that considers two systems. In the extended content management system, learners can add new materials, select materials from teachers and from other learners, evaluate and define the time spent studying them. Based on learner profiles and a hybrid recommendation strategy, combining conditional and collaborative filtering, our second system will predict learning activities scores and offers adaptive and suitable sequencing learning contents to learners. We propose that similarities between learners can be based on their evaluation interests and their recent learning history. The recommender support subsystem aims to assist learners at each step suggesting one suitable ordered list of LOs, by decreasing order of relevance.
The proposed model has been implemented in the Moodle Learning Management System (LMS), and we present the system’s architecture and design.
We will evaluate it in a real higher education formal course and we intend to present experimental results in the near future
Living Innovation Laboratory Model Design and Implementation
Living Innovation Laboratory (LIL) is an open and recyclable way for
multidisciplinary researchers to remote control resources and co-develop user
centered projects. In the past few years, there were several papers about LIL
published and trying to discuss and define the model and architecture of LIL.
People all acknowledge about the three characteristics of LIL: user centered,
co-creation, and context aware, which make it distinguished from test platform
and other innovation approaches. Its existing model consists of five phases:
initialization, preparation, formation, development, and evaluation.
Goal Net is a goal-oriented methodology to formularize a progress. In this
thesis, Goal Net is adopted to subtract a detailed and systemic methodology for
LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps.
Big data, crowd sourcing, crowd funding and crowd testing take place in
suitable steps to realize UUI, MCC and PCA throughout the innovation process in
LIL 2.0. It would become a guideline for any company or organization to develop
a project in the form of an LIL 2.0 project.
To prove the feasibility of LIL Goal Net Model, it was applied to two real
cases. One project is a Kinect game and the other one is an Internet product.
They were both transformed to LIL 2.0 successfully, based on LIL goal net based
methodology. The two projects were evaluated by phenomenography, which was a
qualitative research method to study human experiences and their relations in
hope of finding the better way to improve human experiences. Through
phenomenographic study, the positive evaluation results showed that the new
generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf
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Technology-enhanced Personalised Learning: Untangling the Evidence
Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students
Training Engineers for the Ambient Intelligence Challenge
The increasing complexity of the new breed of
distributed intelligent systems, such as the Internet of Things
(IoT), which require a diversity of languages and protocols, can
only be tamed with design and programming best practices.
Interest is also growing for including the human factor, as
advocated by the Ambient Intelligence (AmI) research field, whose
focus is on transparently and intelligently supporting people.
These new design methodologies are increasingly needed in the
toolbox of new electronic and computer engineers, and teaching
strategies should be devised that allow students to acquire a
systems-level view instead of getting lost in technology-oriented
approaches. This paper describes a study carried out over two
academic years, in a course in ambient intelligence at Politecnico
di Torino, Italy. In the course, a project-based learning approach
was adopted, in which students design and prototype an AmI
system, and their progress is closely monitored throughout the
semester. The paper presents the learning goals and teaching
strategies, analyzes the learning outcomes from the qualitative
and quantitative points of view, and highlights the lessons learned
in the process
Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning
The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning
A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations
With the development of artificial intelligence, personalized learning has
attracted much attention as an integral part of intelligent education. China,
the United States, the European Union, and others have put forward the
importance of personalized learning in recent years, emphasizing the
realization of the organic combination of large-scale education and
personalized training. The development of a personalized learning system
oriented to learners' preferences and suited to learners' needs should be
accelerated. This review provides a comprehensive analysis of the current
situation of personalized learning and its key role in education. It discusses
the research on personalized learning from multiple perspectives, combining
definitions, goals, and related educational theories to provide an in-depth
understanding of personalized learning from an educational perspective,
analyzing the implications of different theories on personalized learning, and
highlighting the potential of personalized learning to meet the needs of
individuals and to enhance their abilities. Data applications and assessment
indicators in personalized learning are described in detail, providing a solid
data foundation and evaluation system for subsequent research. Meanwhile, we
start from both student modeling and recommendation algorithms and deeply
analyze the cognitive and non-cognitive perspectives and the contribution of
personalized recommendations to personalized learning. Finally, we explore the
challenges and future trajectories of personalized learning. This review
provides a multidimensional analysis of personalized learning through a more
comprehensive study, providing academics and practitioners with cutting-edge
explorations to promote continuous progress in the field of personalized
learning.Comment: 82 pages,5 figure
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