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Leopold Hamminger

Leopold Hamminger
Europa Virtuelle Volkshochschule



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Leopold Hamminger is currently in the final stages of his doctoral dissertation titled 'Subjective theories on learning success in adult education', at the Department of Educational Science, University of Salzburg. Leopold does research in Educational Technology, Educational Assessment and Adult Education. He is pedagogical director of 'Europa Virtuelle Volkshochschule', an Austrian eLearning institution.


Publications (6)
Full-text available
Supporting document for thesis. Summary of Klafki's multiparadigmatic approach to educational science (1998). (in German language)
Full-text available
The challenges of Natural Language Understanding (NLU) and Natural Language Processing (NLP) are discussed, especially as they pertain to using German language. The concept of "machine understanding" is presented and its application in the area of education: generating quizzes.
Full-text available
The Sokrates AI - based quiz generator uses learning texts as input (e.g., pdf) and outputs any number of automatically generated multiple choice / fill-the-blank - questions. Optionally directly into a Moodle quiz.
Constructivist teaching in virtual environments places particular emphasis on the use of discussion forums. This article presents the implementation of a concept of constructivist didactics through an e-learning course on the Blackboard platform of the Department of Education at the University of Salzburg, Austria. Students were motivated to build...


Questions (5)
Suppose a brilliant scientist develops a theory that has a positive impact on ourselves and society, in a specific field such as education.
Now assume that this scientist subscribes to beliefs in another field (such as politics), which most of us would regard as wrong or even dangerous and evil.
What would be the proper way to communicate his brilliant idea? Making him famous without mentioning his failings? Or disregard these as irrelevant?
Thank you for your thoughts.
Say I have two audio files, one with a specific word spoken by a native speaker, the other with the same word spoken by a learner. When I say word, I probably need to make this phoneme.
Using the native speaker - file, I want to detect the differences to the learner's file. Obviously gender difference, pitch, speed, and the like should be ignored.
To make this clearer: the initial phonemes in Jack, chin, gin, etc. are often pronounced wrongly by non-native speakers. Could an algorithm detect this? Or would I classify Jack pronounced by 10 native speakers pronounce Jack, and then feed the learner's Jack?
Is this possible at all? Would TensorFlow be a tool to try?
Thank you for your ideas.
A semi-serious discussion on manners in respect of adults, children, infants and animals led to these questions:
1. On what type of scale would you measure "manners"? From 'no manners' to 'very good manners' (e.g., 0 - 4); or 'very bad manners .. no manners ... very good manners' (e.g., -4 ... 0 ... +4)?
2. Having in mind your choice of scale, how would you operationalize the construct 'manners', in order to measure it?
Enjoy, and thank you in advance for your ideas.



Projects (3)