Combining symbolic and sub-symbolic AI in the context of education and learning
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Author (Corporation)
Publication date
2020
Typ of student thesis
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Collections
Type
04B - Conference paper
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Parent work
Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020)
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DOI of the original publication
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Series number
Volume
1
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Pages / Duration
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Place of publication / Event location
Palo Alto
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Abstract
Abstraction abilities are key to successfully mastering the Business Information Technology Programme (BIT) at the FHNW (Fachhochschule Nordwestschweiz). Object-Orientation (OO) is one example - which extensively requires analytical capabilities. For testing the OO-related capabilities a questionnaire (OO SET) for prospective and 1st year students was developed based on the Blackjack scenario. Our main target of the OO SET is to identify clusters of students which are likely to fail in the OO-related modules without a substantial amount of training. For the interpretation of the data the Kohonen Feature Map (KFM) is used which is nowadays very popular for data mining and exploratory data analysis. However, like all sub-symbolic approaches the KFM lacks to interpret and explain its results. Therefore, we plan to add - based on existing algorithms - a “postprocessing” component which generates propositional rules for the clusters and helps to improve quality management in the admission and teaching process. With such an approach we synergistically integrate symbolic and sub-symbolic artificial intelligence by building a bridge between machine learning and knowledge engineering.
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Subject (DDC)
Event
AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020)
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Conference start date
23.03.2020
Conference end date
25.03.2020
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Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
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Peer review of the complete publication
Open access category
Diamond
License
Citation
Telesko, R., Jüngling, S., & Gachnang, P. (2020). Combining symbolic and sub-symbolic AI in the context of education and learning. In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, & F. van Harmelen (Eds.), Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020) (Vol. 1). https://doi.org/10.26041/fhnw-6675