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Essentially, learning design establishes the objectives and pedagogical plans, which can then be evaluated against the outcomes captured through learning analytics

Common elements within all learning designs include the following:

• A set of resources for the student to access, which could be considered to be prerequisites to the learning itself (these may be files, diagrams, questions, web links, prereadings, etc.)

• Tasks the learners are expected to carry out with the resources (prepare and present findings, negotiate understanding, etc.)

• Support mechanisms to assist in the provision of resources and the completion of the tasks; these supports indicate how the teacher, other experts, and peers might contribute to the learning process (e.g., such as moderation of a discussion or feedback on an assessment piece; Bennett et al., 2004)

Checkpoint analytics is the snapshot data that indicate a student has met the prerequisites for learning by accessing the relevant resources of the learning design. For instance, checkpoint analytics would relate to metrics such as log-ins into the online course site, downloads of a file for reading, or signing up to a group for a collaborative assignment. Although these forms of analytics may be valuable for providing lead indictors of student engagement, they do not, in isolation of other data, provide insight into the learning process or understanding of how students are learning and what they are learning. As checkpoint analytics exclusively measures access to the resources included in a learning design, its value lies in providing teachers with broad insight into whether or not students have accessed prerequisites for learning and/or are progressing through the planned learning sequence (akin to attendance in a face-to-face class).

Data on whether or not students have accessed prereadings or organized themselves into groups for upcoming assignments could be considered checkpoints that indicate whether the foundations for learning have been established, and thus checkpoint analytics concentrates on highlighting which students have completed these learning prerequisites and which have not.

Process analytics gives direct insight into learner information processing and knowledge application (Elias, 2011) within the tasks that the student completes as part of a learning design. For example, social network analysis of student discussion activity on a discussion task provides a wealth of data that can offer insight into an individual student’s level of engagement on a topic, his or her established peer relationships, and therefore potential support structures.

The articulation of the nature of support available within learning designs helps to interpret process learning analytics. These supports give an indication of what roles we can expect to see learners and teachers taking within collaborative spaces such as discussion forums (e.g., whether we would expect exclusively student-to-student interactions in a group discussion on construction of a group assignment or facilitator-centric interactions in the Q&A portion of the forum). In this way they help to provide an expected configuration based on what support was built into the learning design.