Teaching Plan M7-m18
In our Doctoral network we have 12 PhD students with different backgrounds: i.e. Engineering, Psychology, Neurosciences. To start on solid and similar bases, teaching will focus (1) on basic knowledge necessary to begin and develop their individual project and (2) on selected advanced topics relevant in all projects. Teaching is firstly implemented in the form of teaching blocks. After ~6 months into their project, the students will get various specialized sessions on topics related to hard, soft and transferable skills. These topics are addressed in monthly one-hour lessons. Further teaching will take place during the first Network Workshop in months 9. Progress reports of the PhD candidates are presented in mini symposia.
Different forms of teaching will be used. While in the network workshop in class teaching and interactive teaching will take place, teaching blocks will make use of massive open online courses (MOOC), videos, video conferences and seminar-like interactive forms of teaching. Monthly teaching events rely on online forms and video conferences. The mini symposia include scientific presentations and discussions. They are flanked by a critical appraisal of the presentation style and content. PhDs’ performance will be assessed by exams, quizzes, assignments and in presentations. To assure high quality of teaching PhD students provide feedback to their teacher through survey forms.
Overall, diverse teaching methods and corresponding evaluations should foster efficient, motivated learning at a high level. The goal is to expand the knowledge of PhD candidates, spark enthusiasm for research, and nurture their personal development.
Overview
Each teaching block addresses different aims.
Block 1: Introduction to the auditory field
Part a) recapitulates the biophysical and neurophysiological foundations of spatial hearing. Part b) deals with disorders and diseases of the auditory pathway that impair spatial hearing. Diagnostic methods are presented which can be used to assess the chances of success of CI implantation. Part c) focuses on the parts of the auditory pathway that are relevant for speech comprehension. Spatial hearing plays an essential role in speech comprehension in complex acoustic environments.
Block 2: Electrophysiology, cognitive and neurocognitive Studies
This block provides an overview of research questions on hearing with a particular focus on spatial hearing. Since EEG plays an extraordinary role in the neurophysiological investigation of hearing in our training network, the part a) will introduce the electrophysiology of hearing. In Part b), research questions on cognitive and neurocognitive aspects of hearing will be discussed.
Block 3: Spatial hearing, processing and perception
Stimuli in natural environments generally stimulate various sensory channels simultaneously. For example, in spatio-visual perception, hearing controls head and eye movements and directs visual attention within the field of view. Moreover, in everyday situations, perception is an active process, where sensory organs are moved to enhance perception. Part a) of teaching block 3 covers processing principles involved in the integration of multisensory stimuli. Part b) introduces applications of active and multisensory perception in the domain of spatial hearing which are critically involved in virtual reality and immersive audiology.
Block 4: Data analysis and auditory modelling toolbox
In the project, both behavioral and physiological signals related to spatial hearing, with and without cochlear implants (CI), are recorded. In Part a), time series analysis and statistical analysis methods are taught. Part b) introduces an auditory modelling toolbox, which allows for simulating auditory signal processing from the auricle (outer ear) to the brainstem. Simulations of spatial hearing in both, normal hearing- and hearing-impaired individuals, as well as CI users, are applied in several projects of CherISH.
Block 5: introduction to machine learning
Machine learning includes a range of methods that are exceptionally well-suited for analyzing various problems for which no closed-form solutions can be provided. The following methods will be introduced in the two blocks: linear regression, logistic regression, k-means algorithm, support vector machines, decision trees, random forests, artificial neural networks, and generative adversarial networks. The online lectures will be supplemented with exercises.