Drahomíra Herrmannová

Martin Hlosta, Drahomira Herrmannova, Lucie Vachova et al.

In recent years, distance education has enjoyed a major boom. Much work at The Open University (OU) has focused on improving retention rates in these modules by providing timely support to students who are at risk of failing the module. In this paper we explore methods for analysing student activity in online virtual learning environment (VLE) -- General Unary Hypotheses Automaton (GUHA) and Markov chain-based analysis -- and we explain how this analysis can be relevant for module tutors and other student support staff. We show that both methods are a valid approach to modelling student activities. An advantage of the Markov chain-based approach is in its graphical output and in the possibility to model time dependencies of the student activities.

Drahomira Herrmannova, Steven R. Young, Robert M. Patton et al.

Identifying and extracting data elements such as study descriptors in publication full texts is a critical yet manual and labor-intensive step required in a number of tasks. In this paper we address the question of identifying data elements in an unsupervised manner. Specifically, provided a set of criteria describing specific study parameters, such as species, route of administration, and dosing regimen, we develop an unsupervised approach to identify text segments (sentences) relevant to the criteria. A binary classifier trained to identify publications that met the criteria performs better when trained on the candidate sentences than when trained on sentences randomly picked from the text, supporting the intuition that our method is able to accurately identify study descriptors.

Drahomira Herrmannova, Petr Knoth

With the growing amount of published research, automatic evaluation of scholarly publications is becoming an important task. In this paper we address this problem and present a simple and transparent approach for evaluating the importance of scholarly publications. Our method has been ranked among the top performers in the WSDM Cup 2016 Challenge. The first part of this paper describes our method. In the second part we present potential improvements to the method and analyse the evaluation setup which was provided during the challenge. Finally, we discuss future challenges in automatic evaluation of papers including the use of full-texts based evaluation methods.