scholarly journals Designing Online Courses for Screen Reader Users

2013 ◽  
Vol 17 (3) ◽  
Author(s):  
Lorna R. Kearns ◽  
Barbara A. Frey ◽  
Gabriel McMorland
Keyword(s):  
Learning Environment ◽  
Online Courses ◽  
Virtual Learning ◽  
Assistive Technologies ◽  
Virtual Learning Environment ◽  
Web Pages ◽  
Screen Reader ◽  
Course Material ◽  
Computer Based ◽  
Effective Use

A review of multiple online courses at one institution was conducted by a skilled screen reader user for the purpose of assessing the extent to which the courses were navigable and understandable to online students using assistive technologies. This paper identifies features of online courses that may present problems for screen reader users and recommends solutions to address those problems. The following two overarching recommendations are suggested and elaborated: 1) Just as a roadmap assists drivers in navigating unfamiliar terrain, principles of clarity, consistency, and organization should be applied to the design of online courses to orient students to the virtual learning environment. 2) Web pages and course documents should make effective use of metadata, i.e., machine-understandable information about computer-based content, in order for course material to be accurately understood by students using a screen reader.

scholarly journals CHARACTERIZING PATTERNS IN STUDENT ENGAGEMENT IN A TEAM-BASED AND PROJECT-CENTRIC COURSE

2021 ◽  
Author(s):  
Roza Ghaemi ◽  
Gabrielle Lam
Keyword(s):  
Student Engagement ◽  
Learning Environment ◽  
Online Courses ◽  
Meaningful Learning ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Online Engagement ◽  
Student Interviews

Student engagement is believed to be related to students’ meaningful learning, persistence andsatisfaction. Although its importance is well recognized, student engagement patterns in the virtual learning environment are not well understood, particularly in online courses that offer both synchronous and asynchronous paths. The purpose of this study is to characterize studentonline engagement in a team-based project-centric course, in terms of behavioral, cognitive, emotional, social and collaborative dimensions. Data from tracking logs and student interviews will be analyzed to elucidate any relationships between the various dimensions of online engagement.

Different Shades of Virtual Learning Environments

2021 ◽  
pp. 809-824
Author(s):  
Arshia Ayoub ◽  
Zahid Ashraf Wani
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Formal Education ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Virtual Space ◽  
Virtual Learning Environments ◽  
Virtual Objects ◽  
Current Trends ◽  
Computer Based

The emergence of the internet has opened a wealth of new, network-based applications, from digital music stores to new venues for scholarly publishing. It has replaced the physical learning environments with virtual ones. The virtual learning environments are utilized for not only imparting informal but formal education as well. A collaborative virtual learning environment is a computer-based, distributed, virtual space or set of places. In such places, people can meet and interact with others, with agents, or with virtual objects. Collaborative virtual learning environment might vary in their representational richness from 3D graphical spaces, 2.5D, and 2D environments to text-based environments. In this milieu, the proposed study shall make an endeavor to time travel and identify various virtual learning systems, services, and tool that are being used and adopted by virtual learners. In addition, the study shall also anticipate the future virtual learning environments based on current trends and future requirements.

Student Experiences with Mobile Electronic Updates from a Virtual Learning Environment

2012 ◽  
Vol 4 (3) ◽  
pp. 16-33
Author(s):  
Laura Crane ◽  
Phillip Benachour ◽  
Paul Coulton
Keyword(s):  
Learning Environment ◽  
Mobile Applications ◽  
Virtual Learning ◽  
Assistive Technologies ◽  
Virtual Learning Environment ◽  
Wireless Access ◽  
Student Experiences ◽  
Motivational Design ◽  
Potential Benefits ◽  
Instruction Theory

This paper describes the development of two mobile applications to disseminate course and module information to university students by sending notifications and electronic updates to their mobile devices. The two mobile applications are based on RSS and Twitter and provide notifications to users which are similar in format and transmission mode to these Web 2.0 entities. The aim of this study is to understand the potential benefits of using the mobile applications as assistive technologies to the existing virtual learning environment. The study uses the ARCS model of motivational design and instruction theory (attention, relevance, confidence, satisfaction) as a tool to enhance students’ experience and their subject engagement. User feedback revealed that although users were given flexibility with regards to temporal updates, they preferred temporal updates at specific times and not in real time. A lack of wireless access in some areas commonly used by students proved a further limitation.

Proposal of an instrument to evaluate interaction spaces in a VLE

2019 ◽  
pp. 1-13
Author(s):  
Rubí Estela Morales-Salas ◽  
Daniel Montes-Ponce
Keyword(s):  
Learning Environment ◽  
Design Space ◽  
Collaborative Work ◽  
Virtual Learning ◽  
Interaction Space ◽  
Learning Objects ◽  
Traditional Education ◽  
Virtual Learning Environment ◽  
Discussion Forums ◽  
Exhibition Space

A virtual learning environment is conceived as an interaction space that ease the realization of mediated activities by technology, in this case the internet; besides using multimedia materials, learning objects, social networks, among others; which have changed imminently the traditional education. In this article an instrument is proposed in a checklist format, to evaluate any platform that has interaction spaces such as a Virtual Learning Environment, in this case responding to four spaces or general indicators: information Space, Mediation / Interaction Space, Instructional Design Space and Exhibition Space. Criteria are used according to the interactions and activities carried out by the consultant and virtual student. These, in turn, come up from the analysis and interaction of the advisers achieved in the discussion forums and portfolio activities through collaborative work. It was situated as a qualitative research, with a descriptive nature since it is not limited to data collection only, but also it refers and analyzes the interaction of the advisers achieved in the discussion forums and portfolio activities through the collaborative work of the workshop course "Virtual Learning Environments" developed in a virtual learning environment.

scholarly journals Self-Regulation using Moodle Virtual Learning Environment (VLE) in Solar System Practice

2021 ◽  
Vol 1779 (1) ◽  
pp. 012072
Author(s):  
BR Rosyadi ◽  
Khoirun Nisa ◽  
Irfan Afandi ◽  
Fathor Rozi ◽  
Ahmad Fawaid ◽  
...  
Keyword(s):  
Solar System ◽  
Learning Environment ◽  
Self Regulation ◽  
Virtual Learning ◽  
Virtual Learning Environment

scholarly journals Using Virtual Learning Environment Data for the Development of Institutional Educational Policies

2021 ◽  
Vol 11 (15) ◽  
pp. 6811
Author(s):  
Emanuel Marques Queiroga ◽  
Carolina Rodríguez Enríquez ◽  
Cristian Cechinel ◽  
Alén Perez Casas ◽  
Virgínia Rodés Paragarino ◽  
...  
Keyword(s):  
Learning Environment ◽  
Predictive Models ◽  
Data Science ◽  
Educational Data Mining ◽  
Virtual Learning ◽  
Academic Community ◽  
Virtual Learning Environment ◽  
Educational Policies ◽  
Interdisciplinary Exchange ◽  
Computational Resources

This paper describes the application of Data Science and Educational Data Mining techniques to data from 4529 students, seeking to identify behavior patterns and generate early predictive models at the Universidad de la República del Uruguay. The paper describes the use of data from different sources (a Virtual Learning Environment, survey, and academic system) to generate predictive models and discover the most impactful variables linked to student success. The combination of different data sources demonstrated a high predictive power, achieving prediction rates with outstanding discrimination at the fourth week of a course. The analysis showed that students with more interactions inside the Virtual Learning Environment tended to have more success in their disciplines. The results also revealed some relevant attributes that influenced the students’ success, such as the number of subjects the student was enrolled in, the students’ mother’s education, and the students’ neighborhood. From the results emerged some institutional policies, such as the allocation of computational resources for the Virtual Learning Environment infrastructure and its widespread use, the development of tools for following the trajectory of students, and the detection of students at-risk of failure. The construction of an interdisciplinary exchange bridge between sociology, education, and data science is also a significant contribution to the academic community that may help in constructing university educational policies.

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