Business applications are difficult to use for the average user. An adaptive user interface improves employees’ productivity and is presented as a solution to this problem. However, developing user interfaces that are adapted to the needs and culture of the enterprise is time-consuming and expensive. We developed a software prototype for generating adaptive user interfaces that makes this process less time-consuming and more efficient. We propose an extension to the Cameleon Reference Framework project by Information Society Technologies, on the implementation level by adding an additional step for defining the Area of Business Operations. That way the prototype can extract business tasks for the selected industry therefore, presenting to the developer a more intelligent selection of predefined tasks. In this article, we also present a programming approach for transforming a task model, as defined by the ConcurTaskTrees notation, into an abstract user interface.
This paper describes a computer algebra-aided generation of two types of English language tests, which further develops our recent work in this domain. The computer algebra system Wolfram Mathematica significantly advances the process of English language testing and assessment. The automatic generation of questions allows us to create a large set of equivalent questions of a certain topic based on a small amount of input values. This reduces authoring time during test creation, avails application of equal criteria and a fair assessment, and decreases the influence of subjective factors. In our previous work, we proposed methods for automatic generation of English language test questions. These were aimed at evaluating the students' knowledge of lexical and grammatical structures found in the text using test questions that involved matching words and their meaning, matching parts of the whole, and finding synonyms, antonyms, and generalizations or specializations of words. This paper provides new methods for the automatic generation of English language test questions. This includes generating questions for testing the students' knowledge of adverbs and adjectives, as well as word formation, especially with negative forms of adjectives.UDC Classification: 004.42 DOI: http://dx.doi.org/10.12955/cbup.v4.794
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