How You Will Be Marked

A number of module-based assignments are required throughout the course with a culminating final assessment. A breakdown of the assignments and their proposed weight are described below.

1. Project 1. Analytics Model. Develop an analytics model to gain insight into a complex topic using both qualitative and quantitative methods. For this assignment, you are required to select a particular topic or subject area (current events, historical activities, a learning challenge) and detail how you will "interrogate" this subject using various analytics tools to demonstrate your mastery of the tools and concepts introduced during the course. Your project can be in the form of a presentation, a paper, a video, a simulation, or other digital artifact.

   Weight: 40% of your final grade
   Due: Conclusion of Week 8 (Week 5 is peer and faculty review)

2. Participation Analysis. (threaded discussion, blogs). The participation grade has two components. The first is obviously your active participation in the Landing through posting on your blog, commenting on other's posts, Moodle discussions, and so on. The grade for your participation is based on your analysis and visualization of participation—you are required to use analytics tools such as visualization tools and discourse analysis tools to communicate to others (and the course prof) how you have contributed to the course.

   Weight: 20% of your final grade
   Due: Conclusion of Week 12 (Week 6 is peer review)

3. Project 2. Technique and Tool Matrix. Tools, methods, and context. This matrix will demonstrate your understanding of the different analytics tools available for evaluating and gaining insight into complex learning and knowledge settings. The matrix will detail the tools and methods that are most appropriate in various analytic contexts. Your contributions here should go beyond the three specific methodologies that we cover in this course (social networks, influence, and discourse). While we don't spend much time looking at recommender systems or dashboards, these are obviously important tools and methods to consider in learning analytics.

   Weight: 20% of your final grade
   Due: Conclusion of Week 11

4. Concept Map. Map of personal learning and knowledge relatedness. A concept map shares many of the attributes of linked data. It is a personal map that details how you as a learner connect various course elements.

   Weight: 20% of your final grade
   Due: At conclusion of Week 12 (Week 6 is peer review)

While most of the assignments are due toward the end of the course, you will be actively working on the concept map and participation beginning in Week 1. At the end of Week 6, you will share these two artifacts with other course participants for peer feedback. Your analytics project will be shared at the end of Week 5 for feedback from peers and course instructor. The peer and faculty feedback between Week 5 and Week 6 should provide you with clear insight into your progress in the course.