Group items tagged

Learning analytics is broader, however, in that it is concerned not only with analytics but also with action, curriculum mapping, personalization and adaptation, prediction, intervention, and competency determination.

For now, it’s sufficient to state that our data trails and profile, in relation to existing curriculum, can be analyzed and then used as a basis for prediction, intervention, personalization, and adaptation.

Effective utilization of learning analytics can help schools and universities to pick up on signals that indicate difficulties with learner performance. Just as individuals communicate social intentions through signals well before they actually “think” they make a decision, learners signal success/failure in the learning process through reduced time on task, language of frustration (in LMS forums), long lag periods between logins, and lack of direct engagement with other learners or instructors.

Curriculum in schools and higher education is generally pre-planned. Designers create course content, interaction, and support resources well before any learner arrives in a course (online or on campus). This is an “efficient learner hypothesis” (ELF) – the assertion that learners are at roughly the same stage when they start a course and that they progress at roughly the same pace. Any educator knows that this is not true and will eagerly resist the assertion that their teaching assumes ELF. But systems don’t lie.

Learning content should be more like computation – a real-time rendering of learning resources and social suggestions based on the profile of a learner, her conceptual understanding of a subject, and her previous experience.