Entwicklungsfeld III

The language dimension of mental models
in digital learning environments

Mental models support learners to develop conceptual and operational representations of learning environments. They help learners to structure their knowledge and to translate learning goals for their knowledge acquisition into cognitive learning processes. It is essential for digital environments supporting self-paced learning not only to provide a clear picture of the knowledge the learner can acquire but also to help the learners in developing a clear image of their state of knowledge acquisition and to keep up a high level of learning motivation. This paper describes the human-computer interaction in the context of learning environments and the particular role of natural language in order to enrich this interaction.

How Natural Language and AI Raises Adaptivity and Individuality of Self-Paced Learning

Self-paced learning fosters the effective, efficient, and sustainable acquisition of knowledge and supports learning journeys adapted to the learners’ individual capacities and aspirations. There are many ways to engineer adaptivity for digital learning platforms. This paper presents design and prototypical implementation of a digital learning platform that uses AI-based analysis of natural language (NL) to engineer adaptivity for digital learning. NL provides many benefits for the communication layer of learning platforms. Learners appreciate the more natural interaction with their learning environment that, in turn, leads to a higher learning motivation and engagement. Natural language analysis (NLA) provides powerful methods and tools that help to summarize, categorize, and relate learning content. NLA produces annotations that foster the dynamic composition of learning components, like course chapters and related quizzes. Our NLA features include the application of nomenclatures like ESCO (The European Skills, Competences, Qualifications and Occupations taxonomy) as they provide unique and standardized vocabularies covering not only the learning components within and across learning materials but also descriptions of learners and their career aspirations. Our system enriches these annotations by adding further operable information from named entity recognition.

Prototypisches Lehrangebot zum Thema “Design Thinking”

Ein wesentlicher Mehrwert des Projektes entsteht durch die enge Verzahnung der datengetriebenen, plattformseitigen Innovationen insbesondere hinsichtlich Qualifikationsbedarfsanalyse und Lerntransferanalyse mit daraufhin angepassten Lerninhalten und Lernszenarien.

Auf diesem Weg sollen für die Branche stilbildende Lernszenarien prototypisch entwickelt werden, die nicht nur Wissen vermitteln, sondern den Kompetenzerwerb stärken.