Measuring the Complexity of Learning Content to Enable Automated Comparison, Recommendation, and Generation

Abstract

Learning content is increasingly diverse in order to meet learner needs for individual personalization, progression, and variety. Learners may encounter material through different content, which invites a measurable comparison method in order to tell when delivered content is sufficient or similar. Content recommendation and generation similarly motivate a fine-grained measure that enhances the search for just the right content or identifies where new learning content is needed to support all learners. Complexity offers a fine-grained way of measuring content which works across instructional domains and media types, potentially adding to existing qualitative and quantitative content descriptions. Reductionist complexity measures focus on quantifiable accounting which practitioners and computers in support of practice can use together to communicate about the complexity of learning content. In addition, holistic complexity measures incorporate contextual influences on complexity that practitioners typically reason about when they understand, choose, and personalize learning content. A combined measure of complexity uses learning objectives as a focus point to let teachers and trainers manage the scope of reductionist elements and capture holistic context factors that are likely to affect the learning content. The combined measure has been demonstrated for automated content generation. This concrete example enables an upcoming study on the expert acceptance and usability of complexity for differentiating between hundreds of generated scenarios. As the combined complexity measure is refined and tested in additional domains, it has potential to help computers reason about learning content from many sources in a unified manner that experts can understand, control, and accept.