Gamification of language learning is a clear trend of recent years. Widespread use of smartphones and the rise of mobile gaming as a popular leisure activity contribute to the popularity of gamification, as application developers can rely on an unprecedented reach of their products and expect acceptance of game-like elements by the users. In terms of content, however, most mobile apps implement traditional language learning activities, such as reading, listening, translating, and solving quizzes. This article discusses gamification of learning natural language grammar with a mobile app WordBricks, based on a concept of more user-centric lab-style experimental activities. WordBricks challenges users to create syntactically accurate sentences by arranging jigsaw-like colored blocks. Users receive instantaneous feedback on the syntactic compatibility each time any two blocks are placed together. This Scratch-inspired virtual language lab harnesses grammar models used in computational linguistics and allows users to discover underlying grammatical constructions through experimentation. The system was evaluated in a number of diverse settings and shows how the principles of gamification can be applied to second-language acquisition. We discuss general features that enable the users to engage in game-playing behavior and analyze open challenges, relevant for a variety of language learning systems.

The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to evaluation and comparison of plagiarism detection tools revealed that these contain limitations in detecting complex forms of plagiarism such as extensive paraphrasing and use of technical tricks, such as replacing original characters with similar-looking characters from foreign alphabets. This article investigates limitations in automatic detection of student plagiarism and proposes ways on how these issues could be tackled in future systems by applying various natural language processing and information retrieval technologies. A classification of types of plagiarism is presented, and an analysis is provided of the most promising technologies that have the potential of dealing with the limitations of current state-of-the-art systems. Furthermore, the article concludes with a discussion on legal and ethical issues related to the use of plagiarism detection software. The article, hence, provides a “roadmap” for developing the next generation of plagiarism detection systems.

Plagiarism has become a serious problem in education, and several plagiarism detection systems have been developed for dealing with this problem. This study provides an empirical evaluation of eight plagiarism detection systems for student essays. We present a categorical hierarchy of the most common types of plagiarism that are encountered in student texts. Our purpose-built test set contains texts in which instances of several commonly utilized plagiaristic techniques have been embedded. While Sherlock was clearly the overall best hermetic detection system, SafeAssignment performed best in detecting web plagiarism. TurnitIn was found to be the most advanced system for detecting semi-automatic forms of plagiarism such as the substitution of Cyrillic equivalents for certain characters or the insertion of fake whitespaces. The survey indicates that none of the systems are capable of reliably detecting plagiarism from both local and Internet sources while at the same time being able to identify the technical tricks that plagiarizers use to conceal plagiarism.

Plagiarism in universities has always been a difficult problem to overcome. Various tools have been developed over the past few years to help teachers detect plagiarism in students' work. By being able to categorize the multitude of plagiarism detection tools, it is possible to estimate their capabilities, advantages and disadvantages. In this article I consider modern plagiarism software solutions, paying attention mostly to desktop systems intended for plagiarism detection in program code. I also estimate the speed and reliability of different plagiarism detection systems that are currently available.