IntroductionGame-based learning is an innovative technique that utilizes the educational potential of videogames in general, and serious games in particular, to enhance training processes and make it simpler for users to attain motivated learning.MethodsIn this study, we propose game based learning for primary school students in computer science terminology learning. Primary school students often engage in game-based learning. Academic accomplishment motivation consequences have been researched extensively. The purpose of this research was to see how successful Game-Based Learnings are in motivating primary school kids to attain academic success. Fifty primary school students in two focus groups participated in the experiment during 10 weeks to test the involvement of game based learning to pupils.ResultsThere are two kind of measurements were applied in identifying benefits of game based learning. First, one is the questionnaire that students answered to questions in three categories as Impression, Usability, and User Interface. Second part of measurement is downloading and uploading of hometasks, and their academic performance. Approximately 90% of the students found the game based learning to be beneficial in their studies and remembering computer science terminologies. The children were satisfied with its functionality and ease of use.DiscussionThe results can be useful for educators, instructional and game designers, and researchers from implementation, design and research perspectives.

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Depressive disorders and suicide are one of the leading causes of death in most countries around the world; it is one of the three most common causes of death in a group of young people, but so far, no methods have been developed for diagnosing suicidal tendencies. In this connection, the problem of developing methods for identifying people prone to suicidal behavior is becoming especially topical. One of the directions of such research is the search for typological features of the speech related to depression using the methods of mathematical linguistics, automatic text processing and machine learning. In foreign science, the texts of people that were motivated by depression are studied using methods of automatic text processing, machine learning methods, and models that are constructed to allow to classify whether the text is related to depression or not. It seems obvious that in order to develop methods for identifying people who are prone to depression and suicide, it is necessary to analyze not only suicide notes, but also other texts created by people who have committed suicide.