Digitale Lehre an und rund um der Technischen Universität Graz
Die Masterarbeit von Michael Steyrer hat sich mit einem “Adaptiven Informationssystem für Mathematische Lernanwendungen” beschäftigt und stellt einen guten Beitrag im Forschungsfeld Learning Analytics dar. Der entwickelte mehrstellige Multiplikationstrainer kann hier verwendet werden, nachdem man sich auf mathe.tugraz.at registriert hat.
Hier aber nun die Präsentation der Masterarbeit:
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Mein Vortrag im Rahmen der OPCO Veranstaltung zu Learning Analytics steht mittlerweile auch als Aufzeichnung zur Verfügung. Ich stelle dabei auch unseren Mathetrainer vor. Viel Spaß beim Ansehen und ich freu mich wie immer über Rückmeldungen.
Im Rahmen der OPCO 12 Veranstaltung hat man mich gebeten, den Trend Learning Analytics unter die Lupe zu nehmen. Ich habe bewusst den Titel “Gedanken zu …” gewählt um auszudrücken, dass ich mir selbst noch unsicher bin wie man den Begriff verorten soll. Nichtsdestotrotz ist Educational Data Mining und die (aus meiner Sicht) Teilmenge Learning Analytics eine Entwicklung die wohl nicht mehr aufzuhalten ist.
Daten, Daten, Daten und deren Interpretation werden uns in Zukunft auch im Lern- und Lehrbereich stark beschäftigen, wiewohl es durchaus auch Gefahren in sich birgt, die in Folie 24 mit dem Informatiker-Sprichwort frei nach Peter Purgathofer zum Ausdruck kommt “Alles was mit Daten passieren kann, wird auch passieren“.
Hier also einmal die Folien, das Liveevent beginnt dann heute hier um 14.00 Uhr.
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At this year LAK 2012 Conference on “Learning Analytics and Knowledge” we presented our first results of the netidee project “Multiplication Trainer“. Our publication is also online available:
Abstract:
One of the first and basic mathematical knowledge of schoolchildren is the multiplication table. At the age of 8 to 10 eachchild has to learn by training step by step, or more scientifically, by using a behavioristic learning concept. Due to this fact it can be mentioned that we know very well about the pedagogicalapproach, but on the other side there is rather less knowledgeabout the increase of step-by-step knowledge of the schoolchildren.In this publication we present some data documenting thefluctuation in the process of acquiring the multiplication tables.We report the development of an algorithm which is able to adaptthe given tasks out of a given pool to unknown pupils. For this purpose a web-based application for learning the multiplicationtable was developed and then tested by children. Afterwards so-called learning curves of each child were drawn and analyzed bythe research team as well as teachers carrying out interestingoutcomes. Learning itself is maybe not as predictable as we knowfrom pedagogical experiences, it is a very individualized processof the learners themselves.It can be summarized that the algorithm itself as well as thelearning curves are very useful for studying the learning success.Therefore it can be concluded that learning analytics will becomean important step for teachers and learners of tomorrow.
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Reference: Schön, M., Ebner, M., Kothmeier, G. (2012) It’s Just About Learning the Multiplication Table, Conferecnce Proceeding of LAK12: 2nd International Conference on Learning Analytics & Knowledge, 29 April – 2 May 2012, Vancouver, BC, Canada
Our publication on “It’s just About Learning the Multiplication Table” got accepted for this year conference on Learning Analytics and Knowledge 2012. We will talk about our research results concerning the project “Intelligent Multiplication Table”. The abstract of our contribution:
One of the first and basic mathematical knowledge of school children is the multiplication table. At the age of 8 to 10 each child has to learn by training step by step, or more scientifically, by using a behavioristic learning concept. Due to this fact it can be mentioned that we know very well about the pedagogical approach, but on the other side there is rather less knowledge about the increase of step-by-step knowledge of the school children.
In this publication we present some data documenting the fluctuation in the process of acquiring the multiplication tables. We report the development of an algorithm which is able to adapt the given tasks out of a given pool to unknown pupils. For this purpose a web-based application for learning the multiplication table was developed and then tested by children. Afterwards so- called learning curves of each child were drawn and analyzed by the research team as well as teachers carrying out interesting outcomes. Learning itself is maybe not as predictable as we know from pedagogical experiences, it is a very individualized process of the learners themselves.
It can be summarized that the algorithm itself as well as the learning curves are very useful for studying the learning success. Therefore it can be concluded that learning analytics will become an important step for teachers and learners of tomorrow.
We are looking forward to meet you in Vancouver to discuss our research results 🙂 .