Michal Ptaszynski

This paper presents a modular system for the support of experiments and research in text classification. Usually the research process requires two general kinds of abilities. Firstly, to laboriously analyse the provided data, perform experiments and from the experiment results create materials for preparing a scientific paper such as tables or graphs. The second kind of task includes, for example, providing a creative discussion of the results. To help researchers and allow them to focus more on creative tasks, we provide a system which helps performing the laborious part of research. The system prepares datasets for experiments, automatically performs the experiments and from the results calculates the scores of Precision, Recall, F-score, Accuracy, Specificity and phi-coefficient. It also creates tables in the LaTex format containing all the results and it draws graphs depicting and informatively comparing each group of results.

A safe and comfortable place to live is a basic need of every human. However, modern tendencies in housing construction appear not to answer this deeply rooted human need: the need to live in a sustainable environment and to create neighbourly ties. One of the forms of neighboirhood units is Cohousing. The name has been coined as a combination of the words „community” and „housing”, and describes a situation in which a group of people initiates the construction of a settlement in which social ties and integration go hand in hand with respecting the autonomy and privacy of an individual.

This paper presents a meta-analysis of experiments performed with language combinatorics (LC), a novel language model generation and feature extraction method based on combinatorial manipulations of sentence elements (e.g., words). Along recent years LC has been applied to a number of text classification tasks, such as affect analysis, cyberbullying detection or future reference extraction. We summarize two of the most extensive experiments and discuss general implications for future implementations of combinatorial language model.

In this paper, we present our research on modeling learning motivation of students by analyzing class evaluation questionnaires, carried out with students as respondents at the end of each term to improve the courses in future semesters. We firstly defined three elements influencing learning motivation: (1) interest, (2) usefulness in the future and (3) satisfaction. Original questionnaire enquiring about those three elements was designed and conducted in multiple classes across different school years. Next, we conducted an experiment to classify students’ motivation for learning using the provided answers. The results of the experiment showed that students’ learning motivation can be estimated using the three elements defined in this study.

The paper presents a method of optimizing Quantitative Learner’s Motivation Model with the use of genetic algorithm. It is focused on optimizing the formula for prediction of learning motivation by means of different weights for three values: interest, usefulness in the future and satisfaction. For the purpose of this optimization, we developed a C++ library that implements a genetic algorithm and an application in C# which uses that library with data acquired from questionnaires enquiring about those three elements. The results of the experiment showed improvement in the estimation of student’s learning motivation.