(AI) Innovation in (AI) Education
Teaching Philosophy
Our group has embraced problem-based and active learning as the fundamental principles of the education process. To implement these principles, we have adopted a flipped classroom approach, where live sessions are conducted with active participation and substantial group and plenum discussions. Additionally, we assign projects and challenges in their labs and coursework. The teaching process of the ML group incorporates many pedagogical principles to achieve the goal of effective education.
Pedagogical Principles
Bank of micro-modules: The concept of a "bank of micro-modules" means that the course can be customized for various groups of students by selecting specific micro-modules from a pre-prepared collection. This approach will decrease the load that the teacher needs to spend on preparing individual modules or designing multiple courses for different levels. Each micro-module is made up of a brief recorded video or lecture (lasting between 5 and 10 minutes), a quiz with immediate feedback, a peer-reviewed assignment, and a reflection question to assess knowledge.
Teach-back method: We have created an initial implementation of the teach-back method, in conjunction with the use of study groups. The core of this implementation is a set of key concepts, and related questions that students divide among themselves on a weekly basis in their respective group. Each student then sets out to read more about their concept and explain it to other members of their group during a meeting. We set out to achieve the following goals with this exercise: i) enhance student engagement, and motivation ii) improve student comprehension and retention on key concepts, while iii) keeping the teacher hours at a reasonable level. This approach was adopted partly in response to evidence demonstrating the limitations of instructor-led explanations for promoting conceptual understanding, as well as to comply with the university's guidelines. We are currently in the process of improving the implementation.
Inclusion as intention and practice together: Inclusive education is the effort to ensure that everyone has equal access to education. The approach taken by LTU acknowledges that there may be conflicts when inclusive education principles clash with the values and traditions of the education system. This means that they have made significant efforts to address academic structures, traditions, and practices that contribute to the marginalization and exclusion of students. Inclusive education is not only focused on special needs students but aims to benefit all students who are at risk of being marginalized.
Major Contribution
MOOCs: Production of 2 MOOC courses
- Introduction to artificial intelligence in tourism
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- Computer vision, Image understanding for efficient business and industry.
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Universeh (EU funding- European Space University for Earth and Humanity)
Modularization – Life-long learning
Lifelong learning, Practical introduction to data science
Joint course with Orbero university: Natural language processing
Courses
- D7064E – Neuromorphic Computing
- D0030E – Introduction to Artificial Intelligence
- Z0009E – Introduction to AI
- D7046E – Neural networks and learning machines
- D7047E – Advanced deep learning
- D7043E – Advanced Data Mining
- D0033E – Machine Learning and Pattern recognition
- D0036E – Programming for Machine Learning
- D7062E – Artificial Intelligence and Pattern Recognition
- D7058E – Text Mining
Publications
- DigiHealth-AI: Outcomes of the First Blended Intensive Programme (BIP) on AI for Health–a Cross-Disciplinary Multi-Institutional Short Teaching Course
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- Pedagogical Principles in the Online Teaching of Text Mining: A Retrospection
- How University Course becomes Self-driven: MOOC as an Example
- Innovative Education Approach Toward Active Distance Education: a Case Study in the Introduction to AI course
- 2D matrix design for better industry-tailored courses
- Experiences from implementing teach-back in the teaching of Artificial Intelligence
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