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September 18, 2026
When Pictures Hinder Understanding: Illustrations May Impede Learning of Abstract Ideas
Illustrations can help remember specific actions but do not always make abstract ideas easier to learn. Researchers from HSE University and Humboldt University compared how people learn from texts with different levels of abstractness. They found that participants remembered illustrations better and performed better on related tasks after reading a multimedia text about yoga asanas than after reading an abstract text about the Nash equilibrium. The findings could help improve the selection of illustrations for educational and informational materials. The study has been published in Learning and Instruction.
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?

Generation of a Set of Event Logs with Noise

P. 88–95.
Shugurov I., Mitsyuk A. A.

Process mining is a relatively new research area aiming to extract process models from event logs of real systems. A lot of new approaches and algorithms are developed in this field. Researches and developers usually have a need to test end evaluate the newly constructed algorithms. In this paper we propose a new approach for generation of event logs. It serves to facilitate the process of evaluation and testing. Presented approach allows to generate event logs, and sets of event logs to support a large scale testing in a more automated manner. Another feature of the approach is a generation of event logs with noise. This feature allows to simulate real-life system execution with inefficiencies, drawbacks, and crashes. In this work we also consider other existing approaches. Their forces and weaknesses are shown. The approach presented as well as the corresponding tool can be widely used in the research and development process.

Language: English
Full text
Text on another site
Keywords: Petri netsprocess miningProMevent logs event log generation
Publication based on the results of:
Process-aware Information Systems (2014)

In book

Proceedings of the 8th Spring/Summer Young Researchers’ Colloquium on Software Engineering (SYRCoSE 2014)
M.: -, 2014.
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