Learning Analytics: Predicting & Solving Issues in Online Learning

Concurrent Session 8
Leadership

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Brief Abstract

The overarching goal of this proposed presentation is to discuss the use of learning analytics in Canvas to track and predict students’ performances and to provide timely support for online learners. In addition, the issue of students’ privacy in online courses will also be reviewed through our preliminary survey data.

Presenters

Martonia Gaskill is an Assistant Professor in the College of Education Teacher Education Department at the University of Nebraska at Kearney (UNK). She currently teaches both graduate and undergraduate level courses in general education and instructional technology, and teaches both online and face to face. Her research interests include online learning, collaboration in online learning environments, faculty development, digital cheating, pre-service teacher education and technology integration into teaching and learning.

Extended Abstract

The overarching goal of this proposed presentation is to discuss the use of learning analytics in Canvas to track and predict students’ performances and to provide timely support for online learners. In addition, the issue of students’ privacy in online courses will also be reviewed through our preliminary survey data. According to Horizon Report 2016, learning analytics is an educational application of web analytics aimed at learner profiling, a process of collecting and analyzing details of individual student performances in their online courses. Learning analytics has developed in three stages, moving from an emphasis on hindsight to foresight. The first stage was describing results, the second stage was diagnosing, and the third stage is predicting what will happen in the future. Within the scope of this proposed presentation, we will:

  1. Present how we are using learning analytics in Canvas LMS courses, in the instructor’s role, to track and diagnose students’ performances.
  2. Demonstrate the learning analytics feature in Canvas
  3. Discuss options for diving deeper into learning analytics with additional powerful tools such as Google analytics in the third stage of predicting students’ performances;
  4. Review preliminary survey data about online learners’ perspectives on the issue of privacy of their learning behaviors in Canvas.

The presentation will conclude with tips on how to use the learning analytics feature in Canvas and a call for research collaboration to unleash the power of this feature to improve students’ successe in online learning environments.