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• A Nim like game and a machine that plays it: a learning situation at the interface of mathematics and computer science

  317-326

  Pierre Esclafit

  Simon Modeste

  Nicolas Saby

  Views:

  366

  The purpose of this work is to take a didactic look at a learning situation located at the interface between mathematics and computer science. This situation offers a first approach to the concept of artificial intelligence through the study of a reinforcement learning device. The learning situation, inspired by the Computer Science Unplugged approach, is based on a combinatorial game, along with a device that learns how to play this game. We studied the learning potential when the human players face the machine. After an a priori analysis using the Theory of Didactic Situations (TDS), we conducted a pre-experiment in order to strengthen our hypotheses. In this article, we will focus on the analysis of the didactic variables, the values we have chosen for these variables and their effects on students’ strategies.

  Subject Classification: 97D99, 97K99, 97P80

• Proof step analysis for proof tutoring - a learning approach to granularity

  325-343

  Marvin Schiller

  Dominik Dietrich

  Christoph Benzmüller

  Views:

  189

  We present a proof step diagnosis module based on the mathematical assistant system Ωmega. The task of this module is to evaluate proof steps as typically uttered by students in tutoring sessions on mathematical proofs. In particular, we categorise the step size of proof steps performed by the student, in order to recognise if they are appropriate with respect to the student model. We propose an approach which builds on reconstructions of the proof in question via automated proof search using a cognitively motivated proof calculus. Our approach employs learning techniques and incorporates a student model, and our diagnosis module can be adjusted to different domains and users. We present a first evaluation based on empirical data.

• An improvement of the classification algorithm results

  131-142

  Kristína Machová

  Miroslav Puszta

  Peter Bednár

  Views:

  140

  One of the most important aspects of the precision of a classification is the suitable selection of a classification algorithm and a training set for a given task. Basic principles of machine learning can be used for this selection [3]. In this paper, we have focused on improving the precision of classification algorithms results. Two kinds of approaches are known: Boosting and Bagging. This paper describes the use of the first method – boosting [6] which aims at algorithms generating decision trees. A modification of the AdaBoost algorithm was implemented. Another similar method called Bagging [1] is mentioned. Results of performance tests focused on the use of the boosting method on binary decision trees are presented. The minimum number of decision trees, which enables improvement of the classification performed by a base machine learning algorithm, was found. The tests were carried out using the Reuters 21578 collection of documents and documents from an internet portal of TV Markíza.

• Integrating elements of data science into high-school teaching: Naïve Bayes-classification algorithm and programming in Python

  307-316

  Ödön Vancsó

  Péter Princz

  Views:

  261

  Probability theory and mathematical statistics are traditionally one of the most difficult chapters of mathematics to teach. One of the authors, Péter Princz has experience in teaching various topics via computer programming of the problem at hand as a class activity. The proposed method is to involve programming as a didactic tool in hard-to-teach topics. The intended goal in this case is to implement a naïve Bayes-classifier algorithm in Python and demonstrate the machine-learning capabilities of it by applying it to a real-world dataset of edible or poisonous mushrooms. The students would implement the algorithm in a playful and interactive way. The proposed incremental development process aligns well with the spirit of Tamás Varga who considered computers as modern tools of experimental problem solving as early as in the 1960s.

  Subject Classification: 97D40, 97D50, 97K50, 97K99, 97M60, 97P40, 97P50, 97U50

• Teaching Java programming using case studies

  245-256

  Zoltán Juhász

  Marián Juhás

  Ladislav Samuelis

  Csaba Szabó

  Views:

  181

  The paper deals with the technical background and the pedagogical issues of a specific implementation for the collection, assessment and archiving of the students' assignments written in Java. The implemented system automatically applies object-oriented metrics on the collected works in order to measure the characteristic features of the assignments. Tutors use these results for the detection of plagiarisms and for the selection of outstanding works. The paper interprets the measured values within a real Java course held in the 3rd term of the Informatics bachelor study programme at the technical university. Students have several case studies devoted to the simulation of the ATM (Automatic Teller Machine) at disposal. We conclude that the access to the analyzed pool of case studies, blended with the Sun Learning Connection license from the Sun Microsystems, Inc., is an effective way of teaching programming in Java.