Analytics, Big Data, Assessment, and the Interconnectedness of All Things

Here are some resources that, in whole, present a web of ideas surrounding predictive analytics, big data, assessment, and the roles each of these play in shaping and changing student attitudes, learning, and educational systems. 

• Penetrating the Fog: Analytics in Learning and Education by Long and Siemens (2011, Educause Review), examines the idea that data collection (including student data trails and activity streams mined from online courses) can yield vast amounts of information that, when analyzed, could be used to improve learners’ experiences, spark comparison between institutions, and affect pedagogical approaches, course design, and institutional decision-making.
• Many issues come into play when thinking about students, data, and analytics (like the danger of a return to behaviorism!), and Ekowo and Palmer’s Predictive Analytics in Higher Education: Five Guiding Practices for Ethical Use offers guidance for developing a valid system and for navigating issues like privacy and bias.
• A related article, Fritz’s Student-facing Learning Analytics and Self-regulated Learning: Check My Activity at UMBC (2017) discusses student responsibility and motivation as related to student-facing learning-analytics. The Check My Activity tool, developed to work in conjunction with the Blackboard LMS, allows students to compare their individual activity with that of anonymous classmates and has been shown to boost performance.
• Finally, the 2007 article Assessment Through the Student’s Eyes, by Rick Stiggins, sparks thoughts about how students of all ages are “data-based decision makers” who, when fully included in the assessment process, can help themselves build and maintain success.