Building Learning Networks for Lifelong Learners: Challenges, Models, Technologies and Standards

2006 ◽  
Author(s):  
R. Koper
Keyword(s):  
Learning Networks ◽  
Lifelong Learners

scholarly journals The Technological Dimension of a Massive Open Online Course: The Case of the CCK08 Course Tools

2009 ◽  
Vol 10 (5) ◽  
Author(s):  
Antonio Fini
Keyword(s):  
Drop Out ◽  
Online Course ◽  
Learning Tools ◽  
Discussion Forums ◽  
Learning Networks ◽  
Massive Open Online ◽  
Lifelong Learners ◽  
Learning Network ◽  
Massive Open Online Course ◽  
Effective Use

In 2008, a new term emerged in the already crowded e-learning landscape: MOOC, or massive open online course. Lifelong learners can now use various tools to build and manage their own learning networks, and MOOCs may provide opportunities to test such networks. This paper focuses on the technological aspects of one MOOC, the Connectivism and Connective Knowledge (CCK08) course, in order to investigate lifelong learners’ attitudes towards learning network technologies. The research framework is represented by three perspectives: (a) lifelong learning in relation to open education, with a focus on the effective use of learning tools; (b) the more recent personal knowledge management (PKM) skills approach; and (c) the usability of web-based learning tools. Findings from a survey of CCK08 participants show that the course attracted mainly adult, informal learners, who were unconcerned about course completion and who cited a lack of time as the main reason for incompletion. Time constraints, language barriers, and ICT skills affected the participants’ choice of tools; for example, learners favoured the passive, filtered mailing list over interactive but time-consuming discussion forums and blogs. Some recommendations for future MOOCs include highlighting the pedagogical purpose of the tools offered (e.g., learning network skill-building) and stating clearly that the learners can choose which tools they prefer to use. Further research on sustainability and instructor workload issues should be conducted to determine the cost and effectiveness of MOOCs. Investigation is also necessary to understand whether such terms as <i>course</i>, <i>drop-out</i>, and <i>attrition</i> are appropriate in relation to MOOCs.

Breast Cancer Image Classification: A Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Pooja Pathak ◽  
Anand Singh Jalal ◽  
Ritu Rai
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Treatment Options ◽  
Breast Cancer Diagnosis ◽  
Conventional Approach ◽  
Learning Networks ◽  
Cad System ◽  
Ultrasound Computed Tomography ◽  
Core Concepts ◽  
Magnetic Resonance Imaging Mri

Background: Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment options. There are various methods for screening breast cancer such as mammogram, ultrasound, computed tomography, Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis (CAD) framework, due to the vast amount of data. Objective: This paper aims to cover the approaches used in CAD system for the detection of breast cancer. Method: In this paper, the methods used in CAD systems are categories in two classes: the conventional approach and artificial intelligence (AI) approach. The conventional approach covers the basic steps of image processing such as preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and deep learning networks used for diagnosis. Conclusion: This review discusses some of the core concepts used in breast cancer and presents a comprehensive review of efforts in the past to address this problem.

Pola Implementasi Gerakan Literasi Sekolah Di SMP Negeri 5 Malang

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Saiful Rahman ◽  
Akhsanul In’am
Keyword(s):  
High School ◽  
Junior High School ◽  
Reading Ability ◽  
Junior High ◽  
The State ◽  
School Resources ◽  
Lifelong Learners ◽  
Writing Ability ◽  
Reading Clubs ◽  
Using Data

Abstract:Description Alternative: Abstract: The students 'reading ability is low, it certainly affects the students' writing ability. Implementation of the School Literacy Movement at the stage of habituation to reading, the lack of assistance in extracurricular reading clubs that are scheduled at the State Junior High School 5 Malang. This study aims to foster students' character through the culture of school literacy that embodies the Implementation of the School Literacy Movement so that students of SMP Negeri 5 Malang become lifelong learners. This study uses a qualitative approach with a descriptive type that describes the School Literacy Movement in State Junior High School 5 Malang by using data collection techniques of observation, interviews, and documentation. The results showed that: 1) The habituation phase was carried out by way of students bringing reading books from home or borrowing books to the library. At this stage a class reading corner was prepared, reading 15 minutes before learning began, and a literacy journal; 2) The Development and Learning Phase increases the school resources especially at the State Junior High School 5 Malang, namely the existence of a reading corner in each class, an increase in the number of books, a 30-minute reading club, and a product of the School Literacy Movement.Keywords: School Literacy Movement, Habituation, Development, and Learning Abstrak: Kemampuan membaca peserta didik tergolong rendah pasti berpengaruh terhadap kemampuan menulis peserta didik. Implementasi Gerakan Literasi Sekolah pada tahap pembiasaan minat baca, kurangnya pendampingan pada ekstrakurekuler club baca yang di agendakan di Sekolah Menengah Pertama Negeri 5 Malang.  Penelitian ini bertujuan untuk menumbuhkembangkan budi pekerti peserta didik melalui pembudayaan literasi sekolah yang mewujudkan dalam Implementasi Gerakan Literasi Sekolah supaya peserta didik SMP Negeri 5 Malang menjadi pembelajar sepanjang hayat. Penelitian ini menggunakan pendekatan kualitatif dengan jenis deskriptif yang mendeksripsikan Gerakan Literasi Sekolah di Sekolah Menengah Pertama Negeri 5 Malang dengan  menggunakan teknik pengumpulan data observasi, wawancara, dan dokumentasi. Hasil penelitian menunjukan bahwa: 1) Tahap Pembiasaan dilaksanakan dengan cara peserta didik membawa buku bacaan dari rumah atau meminjam buku ke perpustakaan. Pada tahap ini sudah disiapkan pojok baca kelas, membaca 15 menit sebelum pembelajaran dimulai, dan jurnal literas; 2) Tahap Pengembangan dan Pembelajaran meningkatkan sumber daya sekolah khusnya di Sekolah Menengah Pertama Negeri 5 Malang yaitu adanya pojok baca di masing-masing kelas, penambahan jumlah buku, adanya club baca 30 menit, dan hasil produk Gerakan Literasi Sekolah. Kata Kunci: Gerakan Literasi Sekolah, Pembiasaan, Pengembangan dan Pembelajaran

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

AI Magazine ◽  
2012 ◽  
Vol 34 (1) ◽  
pp. 10 ◽  
Author(s):  
Steve Kelling ◽  
Jeff Gerbracht ◽  
Daniel Fink ◽  
Carl Lagoze ◽  
Weng-Keen Wong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Feedback Loop ◽  
Human Computation ◽  
Computational Power ◽  
Learning Networks ◽  
Learning Network ◽  
Computer Learning ◽  
The Individual ◽  
Citizen Science Project

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. In this paper we explore how Human-Computer Learning Networks can leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

scholarly journals Transfer learning networks with skip connections for classification of brain tumors

2021 ◽  
Author(s):  
Saleh Alaraimi ◽  
Kenneth E. Okedu ◽  
Hugo Tianfield ◽  
Richard Holden ◽  
Omair Uthmani
Keyword(s):  
Brain Tumors ◽  
Transfer Learning ◽  
Learning Networks
Sign in / Sign up

Export Citation Format

Share Document