Artificial Intelligence (AI) is one of the key drivers of adaptive learning. For instance, in digital learning environments, analysis of students’ interactions with learning objects provides vital information about the their learning behavior. As such, all user interactions should be persisted as ADL xAPI or IMS CALIPER statements in a learning record store. The analysis of the usage data leads to a better understanding of the learning process and thus, optimized content, teaching and learning thereafter.
The emergence of online courses has enabled new research opportunities in characterizing individual learning behaviors in different ways, by taking into account the specificities of learning management systems. X-means clustering, for example, is used to extract typical learning patterns, such as completing, auditing and disengaging from distinct university courses. The behaviors found in these courses may provide insights into typical patterns that can lead to better grades.
For learners, Fraunhofer FOKUS developed a Smart Learning Recommender (SLR), where students can keep track of their personal predicted knowledge level on different learning objects at any point in time and obtain personalized learning recommendations to overcome individual learning weaknesses. In addition to content metadata, such as exam relevance, lecture times and prerequisites, SLR takes different factors into account for each student and learning object, such as the user’s self-assessments, interactions with the content, excercise performances, as well as individual forgetting curves and the learning progress of classmates. At the same time, teachers can make use of this data to get an overview of the students’ overall progress and be aware of any potential knowledge gaps.
Moreover, the researchers designed a generator to provide personalized learning paths through knowledge networks. These paths are constructed by considering the time at which they are requested and alternate routes are suggested to provide the user with a selection of preferred learning items. Another promising approach in utilizing artificial intelligence methods is to offer virtual learning assistance in terms of chatbots, with which users can communicate in textual natural languages. This approach reduces barriers to accessibility because learners do not have to log into a platform. Instead, they can access their learning content, definitions and content recommendations directly, e.g., through a text messenger.