Another publication at this year ED-Media conference is about “The Multiplication Table as an innovative Learning Analytics Application“. The presentation has been recorded and can be find here.
The main topic of this paper is the development of a web-based application that helps children to learn the one-digit multiplication table. The developed application supports individual learning process of the pupils and also provides the teachers with the possibility to intervene according to the analysis of users’ answers. The application uses modern technologies in order to offer high performance and availability to the users. The system also provides an interface for mobile clients, which present the questions and the processed data in different forms. The answers of the pupils, as well as other gathered data from the application show interesting results related to the participation and learning improvement.
[Draft version @ ResearchGate]
Reference: Kraja, E., Taraghi, B. & Ebner, M. (2017). The Multiplication Table as an innovative Learning Analytics Application. In J. Johnston (Ed.), Proceedings of EdMedia: World Conference on Educational Media and Technology 2017 (pp. 810-820). Association for the Advancement of Computing in Education (AACE).
Because we are not able to attend the ED-Media conference 2017 in Washington this year, we are doing our presentations virtually. The second of four talks is about “The Multiplication Table as an Innovative Learning Analytics Application ” and presents the complete reworked webapplication to train the multiplication table:
Our contribution to this year Learning Analytics Conference was about “Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming“.
One-digit multiplication errors are one of the most extensively analysed mathematical problems. Research work primarily emphasises the use of statistics whereas learning analytics can go one step further and use machine learning techniques to model simple learning misconceptions. Probabilistic programming techniques ease the development of probabilistic graphical models (bayesian networks) and their use for prediction of student behaviour that can ultimately influence learning decision processes.
[Full paper @ ResearchGate]
[Full paper @ ACM Library]
Reference: Taraghi, B., Saranti, A., Legenstein, R. & Ebner, M. (2016) Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edingburg, United Kingdom, 25/04/16 – 29/04/16, pp. 449-453., 10.1145/2883851.2883895
Our contribution about “Determining the Causing Factors of Errors for Multiplication Problems” at this year European Immersive Education Summit is now online available.
Literature in the area of psychology and education provides domain knowledge to learning applications. This work detects the difficulty levels within a set of multiplication problems and analyses the dataset on different error types as described and determined in several pedagogical surveys and investigations. Our research sheds light to the impact of each error type in simple multiplication problems and the course of error types in problem-size.
Reference: Taraghi, B., Frey, M., Saranti, A., Ebner, M., Müller, V. & Großmann, A. (2014) Determining the Causing Factors of Errors for Multiplication Problems, European Immersive Education Summit, 2014, Vienna, pp. 144 – 153 [Link to article]
Beni did the presentation about “Markov Chain and Classification of Difficulty Levels Enhances the Learning Path in One Digit Multiplication” at this HCII conference in Creete, Greece. Here are his slides:
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Our publication of this year Learning Analytics and Knowledge Conference (LAK 2014) about “On using markov chain to evidence the learning structures and difficulty levels of one digit multiplication” is now online available.
Understanding the behavior of learners within learning applications and analyzing the factors that may influence the learning process play a key role in designing and optimizing learning applications. In this work we focus on a specific application named “1×1 trainer” that has been designed for primary school children to learn one digit multiplications. We investigate the database of learners’ answers to the asked questions (N > 440000) by applying the Markov chains. We want to understand whether the learners’ answers to the already asked questions can affect the way they will answer the subsequent asked questions and if so, to what extent. Through our analysis we first identify the most difficult and easiest multiplications for the target learners by observing the probabilities of the different answer types. Next we try to identify influential structures in the history of learners’ answers considering the Markov chain of different orders. The results are used to identify pupils who have difficulties with multiplications very soon (after couple of steps) and to optimize the way questions are asked for each pupil individually.
Reference: Taraghi, B., Ebner, M., Saranti, A., Schön, M. (2014) On Using Markov Chain to Evidence the Learning Structures and Difficulty Levels of One Digit Multiplication, In: Proceedins of the Fourth International Conference on Learning Analytics And Knowledge, ACM, New York, p. 68-72
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 🙂 .