گروه‌بندی یادگیرندگان در سامانه‌های تطبیق یادگیری به کمک روش خوشه‌بندی پیوندی فازی
محورهای موضوعی : مهندسی برق و کامپیوتر
محمدصادق رضایی
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غلامعلی منتظر
2
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1 - دانشگاه تربیت مدرس
2 - دانشگاه تربیت مدرس
کلید واژه: خوشه‌بندی پیوندی سبک یادگیری گروه‌بندی یادگیرندگان مجموعه فازی یادگیری الکترونیکی یادگیری تطبیقی,
چکیده مقاله :
کیفیت سامانه‌های یادگیری تطبیقی و مشارکتی به نحوه توصیف مناسب یادگیرندگان و نیز دقت و صحت تفکیک یادگیرندگان در گروه‌های همگن و غیر همگن وابسته است. در روش‌های ارائه‌شده برای گروه‌بندی یادگیرندگان در محیط یادگیری الکترونیکی، محققان اغلب به دنبال بهبود روش‌های پایه از طریق ترکیب آنها با روش‌های بهینه‌سازی هستند. این امر موجب پیچیده‌تر شدن روش‌های گروه‌بندی می‌شود و کیفیت نه چندان مناسب گروه‌های حاصل را به دنبال دارد. در این مقاله روشی مبتنی بر نظریه فازی برای انتخاب خوشه‌های مناسب در روش خوشه‌بندی پیوندی معرفی شده است. در این روش هر خوشه به صورت مجموعه‌ای فازی مدل‌سازی شده و خوشه‌های متناظر تعیین گردیده و بهترین آنها به عنوان خوشه مناسب انتخاب شده است. این روش موجب افزایش دقت روش خوشه‌بندی پیوندی می‌شود. نتایج ارزیابی تجربی روش پیشنهادی بر اساس دو شاخص "Davies-Bouldin" و "خلوص و تجمع" نشان می‌دهد این روش دقت بیشتری نسبت به سایر روش‌های خوشه‌بندی در شناسایی گروه‌ها داشته است.
Quality of adaptive and collaborative learning systems is related to appropriate specifying learners and accuracy of separation learners in homogenous and heterogeneous groups. In the proposed method for learners grouping, researchers effort to improving basic clustering methods by combination of them and improving methods. This work makes the complexity of grouping methods increased and quality of result’s groups decreased. In this paper, new method for selection appropriate clusters based on fuzzy theory is proposed. In this method, each cluster is defined as a fuzzy set and the corresponding clusters are determined. So the best cluster is selected among each corresponding clusters. The results of an empirical evaluation of the proposed method based on two criteria: “Davies-Bouldin” and “Purity and Gathering” indicate that this method has better performance than other clustering methods such as FCM, K-means, hybrid clustering method (HCM), evolutionary fuzzy clustering (EFC) and ART neural network.
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