In the last few weeks I have shared several academic organizations that sponsor conferences, journals and other forums for discussion about learning analytics. This acadenic organization also offers some excellent resources for scholars in the area of learning analytics. I hope that it is helpful for you and your research.
Center for Learning Analytics Research Newsroom
This website offers some great, practical, resources for educators and researchers who want to apply learning analytics. There are case studies, resources and strategies for the application of learning analytics in the classroom. For sure, this might be a great resource for practitioners and researchers in this growing field.
SOLAR, the Society for Learning Analytics Research, is an excellent resource for learning analytics. In fact, there is even a list of open jobs related to learning analytics. The international network offers conferences, journals, and other resources for scholars in the area of learning analytics. Also, for Phoenix-area researchers, or for those who are interested in traveling for a conference, there is an upcoming conference in Tempe, Arizona in March of 2019. You may consider joining, or otherwise engaging, with this relevant network of researchers.
This interesting articles summarizes the way that analytics is affecting education. Although skilled teachers and professors tend to adapt their pedagogical approaches to their individual students, there is growing evidence that formalizing this type of assessment can help students learn more efficiently. It may be the case that the implementation of this analytics-based knowledge would be an interesting applied research topic.
This confernce link offers a description of an interesting, and relevant, upcoming conference on learning analytics. This conference is unique in that data scientists, instructional designers, educators, and others are invited to attend. This interdisciplinary conference will therefore be quite holistic in its approach to recent developments in learning analytics.
The EDUCAUSE library on learning analytics is an excellent source of information for researchers that want to learn more about this area of research. Tehir website provides definitions about learning analytics as well as several articles about recent scholarly research developments. For sure, there may be no other better source for comprehensive information about learning analytics than this single source.
This article offers the argument that machine learning should not add complexity to the workplace, but should rather simplify processes to make decision making easier. For sure, as academic articles continue to develop new, increasingly complex, concepts around augmented analytics, such as artificial intelligence generating new artificial intelligence, starting in a simple way can be an efficient way to enter into the "artificial intelligence" development space.
Just a few years ago, data visualization was being discussed by organizations as an effective way to resolve organizational challenges. Along with this visualization trend, there was a drive to create "self service" reporting for operators and leaders. Recently, organizations have started to consider how "machine learning" algorithms may transform organizations through self-adjusting rule-based models. Building on this machine learning trend, some leading organizations are considering "augmented analytics" as a way to innovate more quickly than the competition.
Many organizations have moved to "open" floor plans with cubicles, or desks, with just a few offices around them of very senior leaders. The rationale for this change has been that employees who sit together will talk more with each other and, therefore, collaborate with each other. However, there have been a series of recent articles that provide some evidence that these set-ups actually discourage collaboration because employees attempt to preserve their privacy even more when sitting close together.
In nearly any industry that is applying machine learning, and other artificial intelligence, to improve outcomes, the question of application often emerges. This article offers several detailed examples of machine learning applications in health care. Although this is a health care example, it could be applied to similar consumer-oriented processes in many industries. I hope that this article inspires some ideas of how AI and machine learning may be relevant in your own industry.