دانلود مقاله انگلیسی رایگان:ادغام یادگیری ماشین در تئوری پاسخ به موارد برای پرداختن به مشکل شروع سرما در سیستم های یادگیری انطباقی - 2019
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  • Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems
    Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems


    ترجمه فارسی عنوان مقاله:

    ادغام یادگیری ماشین در تئوری پاسخ به موارد برای پرداختن به مشکل شروع سرما در سیستم های یادگیری انطباقی


    منبع:

    Sciencedirect - Elsevier - Computers & Education, 137 (2019) 91-103: doi:10:1016/j:compedu:2019:04:009


    نویسنده:

    Konstantinos Pliakosa,c, Seang-Hwane Joob,c, Jung Yeon Parkb,c,∗, Frederik Cornillieb,c, Celine Vensa,c, Wim Van den Noortgateb


    چکیده انگلیسی:

    Adaptive learning systems aim to provide learning items tailored to the behavior and needs of individual learners. However, one of the outstanding challenges in adaptive item selection is that often the corresponding systems do not have information on initial ability levels of new learners entering a learning environment. Thus, the proficiency of those new learners is very difficult to be predicted. This heavily impairs the quality of personalized items recommendation during the initial phase of the learning environment. In order to handle this issue, known as the cold-start problem, we propose a system that combines item response theory (IRT) with machine learning. Specifically, we perform ability estimation and item response prediction for new learners by integrating IRT with classification and regression trees built on learners’ side information. The goal of this work is to build a learning system that incorporates IRT and machine learning into a unified framework. We compare the proposed hybrid model to alternative approaches by conducting experiments on two educational data sets. The obtained results affirmed the potential of the proposed method. In particular, the obtained results indicate that IRT combined with Random Forests provides the lowest error for the ability estimation and the highest accuracy in terms of response prediction. This way, we deduce that the employment of machine learning in combination with IRT could indeed alleviate the effect of the cold start problem in an adaptive learning environment
    Keywords: Item response theory | Decision tree learning | Machine learning | Adaptive learning system | Cold-start problem


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 13
    حجم فایل: 717 کیلوبایت

    قیمت: رایگان


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