Design-Based Research Iterations of a Virtual Learning Environment for Identity Exploration

Author(s):  
Amanda Barany ◽  
Aroutis Foster ◽  
Mamta Shah
Author(s):  
Stephen Petrina ◽  
Jennifer Jing Zhao

As educational systems emphasize and experiment with forms of online and remote learning, it is increasingly important to investigate the cultural competence of instructional designers. This chapter addresses the experiences of instructional designers in a 3D virtual learning environment designed for development of cultural competence. Design-based research (DBR) and user experience (UX) methodologies were employed to explore experience of six instructional designers in 3D virtual environment. A taxonomy of experience (ToE) established by Coxon guided qualitative data collection and analysis. Through examples and data, the chapter emphasizes the necessity for instructional designers to keep in mind the challenge of cultural diversity in the backgrounds of students and their own, and bring guidelines and principles into culturally sensitive and responsive instructional design processes. The authors recommend four future research directions, including cross-cultural instructional designer competencies along with research into cultural personas, avatars, and guest-host relations.


Author(s):  
Rubí Estela Morales-Salas ◽  
Daniel Montes-Ponce

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.


2021 ◽  
Vol 1779 (1) ◽  
pp. 012072
Author(s):  
BR Rosyadi ◽  
Khoirun Nisa ◽  
Irfan Afandi ◽  
Fathor Rozi ◽  
Ahmad Fawaid ◽  
...  

2021 ◽  
Vol 11 (15) ◽  
pp. 6811
Author(s):  
Emanuel Marques Queiroga ◽  
Carolina Rodríguez Enríquez ◽  
Cristian Cechinel ◽  
Alén Perez Casas ◽  
Virgínia Rodés Paragarino ◽  
...  

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.


Author(s):  
Larrilyn L. Grant ◽  
Michael J. Opperman ◽  
Brennan Schiller ◽  
Jonathan Chastain ◽  
Jennelle Durnett Richardson ◽  
...  

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