The paper deals with problems that people with special needs have when using information technology products. Many software developers are not even aware of the problem, although people with disabilities nowadays represent a considerably market share. In recent times, more accessible software design is encouraged also by the national laws. The aim of this paper is to point out the difficulties and obstacles that most software applications pose to the disabled people and to seek for the guidelines on accessible software design

In the last years, numerous papers were published comparing different learning management systems (LMS). Some of them dealt with only few comparison criteria, while others included almost every imaginable feature. When faced to do a comparison ourselves, we came across many of such papers and did a research of what authors considered relevant in an LMS. By comparing papers written in different years, we tried to find out if there is a pattern of features linked to a certain time period, how a demand for new features was evolving through time, and how did LMS developers respond to this demand. We also tried to figure out the present demands and which new features will be included in future versions of LMSs.

The article presents a method for segmentation of ethnomusicological field recordings. Field recordings are integral documents of folk music performances captured in the field, and typically contain performances, intertwined with interviews and commentaries. As these are live recordings, captured in non-ideal conditions, they usually contain significant background noise. We present a segmentation method that segments field recordings into individual units labelled as speech, solo singing, choir singing, and instrumentals. Classification is based on convolutional deep networks, and is augmented with a probabilistic approach for segmentation. We describe the dataset gathered for the task and the tools developed for gathering the reference annotations. We outline a deep network architecture based on residual modules for labelling short audio segments and compare it to the more standard feature based approaches, where an improvement in classification accuracy of over 10% was obtained. We also present the SeFiRe segmentation tool that incorporates the presented segmentation method.

Self-regulated learning (SRL) plays an important role in successful learning with hypertexts. The use of appropriate SRL strategies helps students acquire new knowledge more efficiently. We investigated the use of SRL strategies in individual learning from expository science hypertext, the correlations between different measures of self-regulation, and the differences in SRL between more and less successful students. A sample of 443 ninth graders from 15 different schools participated in the study. A variety of off- and online measures were used to measure SRL. Data were collected from student traces, questionnaires, tests scores, and notes. Low correlations between the off- and online measures of SRL suggest that they measure different aspects of SRL use in learning digital science texts. Student achievement in science positively correlated with their use of SRL strategies. Students with higher knowledge gains reported the higher use of deep cognitive strategies, higher motivation for learning, and used a higher number of strategies in note-taking while learning. The results of this study may have practical implications for teachers to support student SRL and for developers of digital learning materials to incorporate SRL scaffolding into learning topics.

V prispevku so predstavljeni zasnova in uresničitev narečnega tematskega slovarja, ki mu nove tehnološke možnosti omogočajo, da je spletni oziroma e-slovar, interaktivni (z multimedijskimi vsebinami) in rastoči (dopolnjujoči),...