Learning analytics also provide a level of personalization in educational content and experiences for students to allow for more engaging and dynamic learning. 

This analytical data can also assist in identifying at risk students and teachers as well as best practices from students and teachers. 

Learning analytics also allows for sharing of this data with other members of the field by asking “practitioners and scholars to make their teaching and learning research available to the community at large, be open to critique and evaluation, and present findings in ways which others can build upon them (van Barneveld, Arnold, and Campbell, 2012, p. 6). 

The infographic posted in the course shows how I can analyze the words and tone of students’ messages and their time spent on activities. “Analysis of learner data may also provide insight into which students are at risk of dropping out or need additional support to increase their success, and confidence, in the learning process (Long and Siemens, 2011). 

This is a benefit for me as an instructor to get information about my students before midterm or finals because of “the increasing availability of data in real time, making it possible to process and act on it instantaneously” (Long and Siemens, 2011). 

While this information is mainly used at the course level, I hope to use it in the future at the program and institutional level to develop personalized curriculum plans. For personalized curriculum,  “learning content is not provided in a packaged textbook but is rendered or computed ‘on the fly,’ providing each learner with resources relevant to his or her profile, learning goals, and the knowledge domain the learner is attempting to master” (Long and Siemens, 2011).