Learning Technologies

Content creators can develop a variety of different digital media assets with easy-to-use editors. Media asset types include digital articles, audio podcasts, demonstrative animations, videos with interactive elements, as well as 360° videos and HTML5-compliant mini applications.

Each learning object represents a self-contained topic that may be consumed separately from others. The related learning object metadata thoroughly describes the content and is based upon open specifications, such as IMS Common Cartridge, Content Packaging, LOM or the outdated ADL SCORM. Quizzes, exercises and tests, in turn, can be developed based on the IMS QTI specification.

The contents are stored in a separate content repository in order to be reusable for different learning environments, re-sellable for third party stakeholders and provide content-protection mechanisms. Once created, learning objects can be grouped into didactic blocks for micro-learning or offered as a hierarchical combination to be presented in interactive courses.

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.

The utilization of Fraunhofer's adaptive learning technologies increases the efficiency of content creation and teaching for institutional members. Additionally, this technology paves the way for more effective and convenient platform usage and overall learning process for the course participants. Since all components offer standardized APIs, they can be easily integrated into existing learning environments. The Fraunhofer FOKUS experts are available to provide further insights, offer workshops on artificial intelligence and interoperability in learning environments and provide advice for a seamless transition into adopting the adaptive learning technologies.

• Personalized user interfaces for adaptive learning, easy-to-use editors and admin services, as well as specialized paradigms to learn anywhere and anytime.
• Support for open standards, specifications and various devices (such as computers, laptops, smartphones, tablets and Smart TVs).
• Learning analytics for processing big data, personalized learning with the support of learning recommender systems/individual learning paths, and approximation of users’ knowledge levels, learning weaknesses and needs.
• Understanding of different media types, including mini applications for course exercises, 360° interactive videos and gamification elements (such as badges, experience points, leaderboards, etc.).