scholarly journals An Intelligent Ubiquitous Learning Environment and Analytics on Images for Contextual Factors Analysis

2020 ◽  
Vol 10 (24) ◽  
pp. 8996
Author(s):  
Mohammad Nehal Hasnine ◽  
Gökhan Akçapınar ◽  
Kousuke Mouri ◽  
Hiroshi Ueda
Keyword(s):  
Learning Environment ◽  
Contextual Factors ◽  
Image Understanding ◽  
Vocabulary Knowledge ◽  
Sensor Data ◽  
Generation Process ◽  
Learning Design ◽  
Learning Contexts ◽  
New Learning ◽  
Applied Artificial Intelligence

Contextual factors in which learning occurs are crucial aspects that learning analytics and related disciplines aim to understand for optimizing learning and the environments in which learning occurs. In foreign vocabulary development, taking the notes or memos of learning contexts along with other factors, play an essential role in quick memorization and reflection. However, conventional tools fail to automate the learning contexts generation process as learners still need to take memos or e-notes to describe their vocabulary learning contexts. This paper presents the Image Understanding Project (hereafter IUEcosystem) that could produce smartly-generated learning contexts primarily in a learner’s target languages. The IUEcosystem uses visual content analysis of lifelogging images as the sensor data to produce smartly-generated learning contexts that could be used as an alternative to handwritten memos or electronic notes. The IUEcosystem uses applied artificial intelligence to produce smartly-generated learning contexts. This intelligent learning environment collects a learner’s learning satisfaction and interaction data and, later on, analyzes them to produce time-based notifications for enhancing retention. Furthermore, a new learning design is presented that aims to map a learner’s prior vocabulary knowledge with new learning vocabularies to be learned. This learning design would help learners to review and recall prior knowledge while learning new vocabulary.

scholarly journals Innovating a Flexible Concept Based Curriculum in a Course: An Experience Using Fractal Model

2019 ◽  
pp. 397-404
Author(s):  
Larisa Enriquez Vazquez
Keyword(s):  
Fractal Model ◽  
Learning Design ◽  
Master's Program ◽  
Educational Model ◽  
Learning Contexts ◽  
New Learning ◽  
Digital Environments ◽  
Innovación Educativa ◽  
The University ◽  
And Training

Fractal is an educational model that tries to respond to the new learning contexts in which we find ourselves, which are characterized by the need to learn and update knowledge continuously and constantly and with the opportunity to access a large number of options for learning and training through the use of technologies, computer networks and digital environments, among others. The fractal model considers four interrelated elements but one element particularly stands out; it is the curriculum based on concepts that allows to expand and integrate different areas of knowledge to a specific, initial perspective. In addition, Fractal presents aspects that can be linked to connectivism and rhizomatic learning, through a concrete proposal of flexible learning design, which can be useful for formal and non-formal courses. The following Master’s program at the University of La Sabana, in Colombia, presents an experience applying the model in a close context (https://www.unisabana.edu.co/programas/posgrados/centro-de-tecnologias-para-la-academia/maestria-en-innovacion-educativa-mediada-por-tic-virtual/nuestro-programa/).

scholarly journals BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2144
Author(s):  
Stefan Reitmann ◽  
Lorenzo Neumann ◽  
Bernhard Jung
Keyword(s):  
Point Clouds ◽  
Training Data ◽  
Sensor Data ◽  
Generation Process ◽  
Data Generation ◽  
Depth Sensor ◽  
Depth Sensing ◽  
Depth Sensors ◽  
3D Point Clouds ◽  
Wide Range

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

The new learning environment: Fact or fiction?

1995 ◽  
Vol 15 (4) ◽  
pp. 237-238 ◽  
Author(s):  
Peter Birchenall
Keyword(s):  
Learning Environment ◽  
New Learning

scholarly journals Acquisition and User Behavior in Online Science Laboratories before and during COVID-19 Pandemic

2021 ◽  
Author(s):  
Raghu Raman ◽  
Ricardo Vinuesa ◽  
Prema Nedungadi
Keyword(s):  
Learning Environment ◽  
User Behavior ◽  
Session Time ◽  
New Learning ◽  
Science Laboratories ◽  
And Behaviors ◽  
Relevant Pedagogy

The Covid-19 pandemic has resulted in the closure of schools at every level, globally, forcing education to move online. Meeting the needs of students online for Science Lab classes, in particular, is a challenge since the physical labs are not available to the teachers or students. OLabs is a virtual Science Lab providing a complete learning environment of theory, experimental procedures, videos, animations, simulations, and assessments that capture real lab experiences with the relevant pedagogy. This study looks at the acquisition and behaviors of users, on the OLabs platform, during pre and Covid-19 times. Using Google Analytics, we observe that, during the pandemic time, users increasingly adopted OLabs as a new learning pedagogy for performing experiments as indicated by parameters like the number of users; the number of unique pages viewed per session; time spent on viewing content; bounce rate; and preference for content types such as theory, simulations, videos, and animations.

Virtual Academic Advising in Challenging Times

2020 ◽  
pp. 184-196
Author(s):  
Georgina Argüello
Keyword(s):  
Distance Education ◽  
Learning Environment ◽  
Academic Advising ◽  
Colleges And Universities ◽  
Academic Advisors ◽  
Education Students ◽  
Technology Tools ◽  
New Learning ◽  
Technology Applications ◽  
Definition Of

With the rapid shift to remote learning because of the pandemic, the academic advisors of colleges and universities had to adapt and change some of the ways they were advising the traditional higher education students. In this new normal, where social distance needs to be present and non-traditional education takes precedence in the learning environment, academic advisors had to rapidly adjust and use different technology tools of virtual advising. Over the past few years, colleges and universities that offer distance education programs have been struggling in engaging and retaining their non-traditional online learners. However, with the pandemic, these institutions may encounter the challenge of not only retaining the non-traditional students but also, the new distance learners. Therefore, academic advisors will need to use creative ways of providing advising services in this new learning environment. Many studies have demonstrated that virtual advising has been helpful to aid the distance education students. Virtual advising uses different technology applications and platforms. Using it correctly can help students and advisors with the registration cycles and with any other concerns the students may have. In this chapter, the author explains academic advising and the role of an advisor, the definition of virtual advising, the importance of combining the different approaches of academic advising into virtual advising, and the different technology tools that can assist academic advisors when doing their job of supporting the students in the new learning environment.

Exploring a New Learning Environment Through Massive Open Online Course

2018 ◽  
pp. 24-36
Author(s):  
Shweta Pandey ◽  
Satyam Prakash Tripathi
Keyword(s):  
Learning Environment ◽  
Information Science ◽  
Online Courses ◽  
Online Course ◽  
Library And Information Science ◽  
Massive Open Online ◽  
Scholarly Literature ◽  
Massive Open Online Course ◽  
Literature Reviews ◽  
New Learning

As the Massive Open Online Course (MOOC) concept is adding a new dimension to online learning and presenting a deeper impact in different disciplines including the library and information science area, library and information science professionals are producing scholarly literature on MOOC-related issues. Through this chapter, the authors gave the overview of the genesis of MOOCs in a new learning environment. This article gives the outlook of MOOCs, which are one of the latest trends in education. This chapter also explores various literature reviews on the conceptual framework and discusses the online courses in general and specifically for LIS domain.

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