Combining symbolic and sub-symbolic AI in the context of education and learning

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.

Schlagwörter

Fachgebiet (DDC)

330 - Wirtschaft

Projekt

Veranstaltung

AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020)

Startdatum der Ausstellung

Enddatum der Ausstellung

Startdatum der Konferenz

23.03.2020

Enddatum der Konferenz

25.03.2020

Datum der letzten Prüfung

ISBN

ISSN

Sprache

Englisch

Während FHNW Zugehörigkeit erstellt

Ja

Zukunftsfelder FHNW

Publikationsstatus

Veröffentlicht

Begutachtung

Peer-Review der ganzen Publikation

Open Access-Status

Diamond

Lizenz

Zitation

TELESKO, Rainer, Stephan JÜNGLING und Phillip GACHNANG, 2020. Combining symbolic and sub-symbolic AI in the context of education and learning. In: Andreas MARTIN, Knut HINKELMANN, Hans-Georg FILL, Aurona GERBER, Doug LENAT, Reinhard STOLLE und Frank VAN HARMELEN (Hrsg.), Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Palo Alto. 2020. Verfügbar unter: https://doi.org/10.26041/fhnw-6675

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