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Strukturbaum
Keine Einordnung ins Vorlesungsverzeichnis vorhanden.
Veranstaltung ist aus dem Semester
WiSe 2023/24
, Aktuelles Semester: WiSe 2024/25
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Praxisprojekt "Achieving Mastery by Deliberate Reinforcement" (co-supervised with Dr. Bibeg Limbu)
Sprache: Englisch
Keine Belegung möglich
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(Keine Nummer)
Praxisprojekt
WiSe 2023/24
12 SWS
keine Übernahme
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Fakultät:
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Informatik
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Teilnehmer/-in
Maximal : 20
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Master of Science Angewandte Informatik (Ingenieur- oder Medieninfor, Abschluss 87, Master of Science Angewandte Informatik (Ingenieur- oder Medieninfor (87AIM)
(
1.
-
3.
Semester )
- Kategorie : WP
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Bachelor of Science Angewandte Informatik (Ingenieur- oder Medieninfor, Abschluss 83, Bachelor of Science Angewandte Informatik (Ingenieur- oder Medieninfor (83AIM)
(
5.
-
6.
Semester )
- Kategorie : WP
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Master of Science Angewandte Kognitions- und Medienwissenschaft, Abschluss 87, Master of Science Angewandte Kognitions- und Medienwissenschaft (87AKM)
(
1.
-
3.
Semester )
- Kategorie : WP
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Bachelor of Science Angewandte Kognitions- und Medienwissenschaft, Abschluss 83, Bachelor of Science Angewandte Kognitions- und Medienwissenschaft (83AKM)
(
5.
Semester )
- Kategorie : WP
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Zugeordnete Lehrperson:
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Chounta
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Termin:
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Dienstag
14:00
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18:00
wöch.
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Raum :
LF 125
LF
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| Bemerkung: |
Deliberate practice is a framework of expertise development or mastery which posits that in order to achieve superior performance in a task practice must be structured and supplemented with feedback. Both of these criteria have historically relied on expert mentor/s, such as in cases of any elite sports athlete. However, such facilities are unobtainable for the common folk which is where the domain of Computational Cognitive Modelling (of expertise) is promising. In this project, you will be acquainted (& challenged) to undertake this thesis by modeling a cognitive task using ACT-R, which is both a theory of common cognitive architecture (see Figure below) and a cognitive modeling language based on the theory. The choice of the task will be left to the students as it enforces the basic understanding of the theory and the domain as well. ACT-R also provides a remote interface for Python or LISP which you will use to generate actionable/qualitative/meaningful feedback for anyone looking to learn the cognitive task (one you selected) using your application. You will also evaluate your model against an expert performer (human) and additional points will be provided for simulating expertise in the cognitive task (or any efforts to do with algorithms such as fast and frugal heuristics). Any experimental study involving multiple participants are welcome.
To register, please self-enrol in Moodle: https://moodle.uni-due.de/course/view.php?id=41767
For questions, please contact Dr. Bibeg Limbu |
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