Special Issue "eLearning"
A special issue of Future Internet (ISSN 1999-5903).
Deadline for manuscript submissions: closed (31 August 2015) | Viewed by 19329
Tremendous growth in the last few years in regards to eLearning, especially through the popularization of Massive Open Online Courses (MOOCs) and Microlearning, prompts us to ask: “what is the learning that is occurring within the online space and what are the outcomes?” Understanding what type of learning is resulting from these efforts, is still a major challenge. Many of the eLearning systems being used have limited dashboard capabilities, with many not going beyond the basic course management components; however, given the scale of the eLearning efforts in recent years, dashboards, defined as sophisticated monitoring systems, are a necessity for all participants—students, instructors, and administrators—as they have the potential to enable us to make sense of the learning that is occurring within the virtual space. What are then the issues surrounding the development of dashboards to visualize learning processes and outcomes? We posit that the major obstacles are no longer about the technical know-how; the technical expertise to develop eLearning dashboards is relatively easy to identify and procure. Rather, the challenges are about conceptualizing and designing dashboards that provide meaningful analytics that engage participants, including instructors and administrators, in making connections of what they are learning.
Within this context, this Future Internet Special Issue on eLearning aims to explore the topic of dashboards. Therefore, we invite researchers to submit manuscripts that focus on eLearning Dashboards.
Some may approach it from a learning dashboard perspective that targets learners focusing on questions such as “what should be on a learning dashboard that is meaningful to learners and would encourage repeat engagement and reflection about what is being learned?”
Some may approach it from a teaching dashboard perspective asking questions about how to support learners who are at risk, or those who may need more challenging learning opportunities. For example, “what information do we want to know about learners to help us predict dropout?” “When would such information be most useful?”
Others may choose to approach it from an institutional perspective with a focus on trying to capture learning experiences over time and asking questions such as “What type of programs would best engage our learners further?” or “what information do we need to capture to generate meaningful information about how our students learn?” and “how do we analyze it intelligently using techniques from, e.g., AI and Big Data?” and “What are the overall curricular outcomes and competencies?”
We also welcome manuscripts that address the broader social and ethical dimension about what data is being captured and how it is used to generate data for a dashboard.
Prof. Yianna Vovides
Prof. Liz Bacon
Manuscript Submission Information
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- online learning
- deep learning
- big data
- artificial intelligence