Browsing by Author "Tolbassy B."

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    Analysis of effective machine learning techniques for improvement of student performance
    (2023) Tolbassy B.
    It is advantageous to use machine learning (ML) algorithms to assess student performance based on their prior performances and current behaviour because they can project both positive and negative outcomes at different educational levels. Learning outcomes can be improved by early performance prediction for students. The prediction of a student’s academic performance is important because it shapes changes in university academic policies, guides instructional strategies, evaluates the efficacy and efficiency of learning, provides teachers and students with pertinent feedback, and modifies learning environments. All of these elements support higher graduation rates. There is currently no clear winner among the various machine learning techniques for predicting student performance while enhancing learning outcomes. Therefore, this study is going to present the most effective machine learning techniques using and analysing open-source data from the Kaggle platform.
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    CLASSIFICATION OF REVIEWS, ERROR REPORTS AND PRODUCT FEATURE REQUESTS USING MACHINE LEARNING METHODS
    (СДУ хабаршысы - 2023, 2023) Tolbassy B.
    Abstract. This article proposes a solution for filtering and categorizing user feedback on software products, which can be overwhelming in quantity and often includes uninformative or fake reviews. The proposed approach involves using machine learning methods for classifying reviews into categories such as error reports, product feature requests, and other reviews. The article compares the performance of different classification ML algorithms and investigates the impact of preprocessing options on classification accuracy. Additionally, the article addresses the task of identifying groups of similar reviews in each category, which can be useful for detecting duplicates and identifying patterns. The proposed solution is tested on a dataset and compared with existing solutions. The article concludes by highlighting the novelty and potential benefits of the proposed approach for improving the quality of user feedback and enhancing the reputation of software products.