Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task that is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers. This article focuses only on plagiarism detection and presents a detailed systematic review of the field of source-code plagiarism detection in academia. This review gives an overview of definitions of plagiarism, plagiarism detection tools, comparison metrics, obfuscation methods, datasets used for comparison, and algorithm types. Perspectives on the meaning of source-code plagiarism detection in academia are presented, together with categorisations of the available detection tools and analyses of their effectiveness. While writing the review, some interesting insights have been found about metrics and datasets for quantitative tool comparison and categorisation of detection algorithms. Also, existing obfuscation methods classifications have been expanded together with a new definition of “source-code plagiarism detection in academia.”

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In this work, it is shown that student access time series generated from Moodle log files contain information sufficient for successful prediction of student final results in blended learning courses. It is also shown that if time series is transformed into frequency domain, using discrete Fourier transforms (DFT), the information contained in it will be preserved. Hence, resulting periodogram and its DFT coefficients can be used for generating student performance models with the algorithms commonly used for that purposes. The amount of data extracted from log files, especially for lengthy courses, can be huge. Nevertheless, by using DFT, drastic compression of data is possible. It is experimentally shown, by means of several commonly used modelling algorithms, that if in average all but 5–10% of most intensive and most frequently used DFT coefficients are removed from datasets, the modelling with the remained data will result with the increase of the model accuracy. Resulting accuracy of the calculated models is in accordance with results for student performance models calculated for different dataset types reported in literature. The advantage of this approach is its applicability because the data are automatically collected in Moodle logs.

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Abstract.Research in Web quality has addressed quality in use as the most important factor affecting the wide acceptance of software applications. It could be seen as comprised of two complementary concepts, usability and user experience, thus focusing on the employment of more user-centered evaluations. Nevertheless, in the context of Web 2.0 applications, this topic has still not attracted enough attention from the HCI community. This paper addresses quality in use of Web 2.0 applications on the case of mind mapping services. The evaluation methodology brings together three complementary methods. Estimated quality in use was measured by means of logging actual use method while the perceived quality in use was evaluated with both retrospective thinking aloud (RTA) method and questionnaire. The contribution of our work is twofold. Firstly, we provided empirical evidence that proposed methodology altogether with model, set of attributes and measuring instruments is appropriate for evaluating quality in use of Web 2.0 applications. Secondly, the analysis of qualitative data revealed that performance and effort based attributes considerably contribute to the success of mind mapping services.

Abstract-This paper discusses the effect of a gamified learning system for students of the master course on Web Design and Programming performed at the Faculty of Organization and Informatics. A new set of usability metrics was derived from web-based learning usability, user experience and instructional design literature and incorporated into the questionnaire which consists of three main categories: Usability, Educational Usability and User Experience. The main contribution of this paper is the development and validation of a questionnaire for measuring the usability of a gamified e-learning course from students' perspective. Usability practitioners can use the developed metrics with confidence when evaluating the design of a gamified e-learning course in order to improve students' engagement and motivation.

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