پیشنهاددهنده تطبیقی منابع آموزشی بر اساس سبک یادگیری، بازخورد کاربر و الگوریتم اتوماتای یادگیر
محورهای موضوعی : مهندسی برق و کامپیوترمحمد طهماسبي 1 , فرانک فتوحی 2 , مهدی اسماعیلي 3
1 - دانشگاه قم
2 - دانشگاه قم
3 - دانشگاه آزاد اسلامي واحد کاشان
کلید واژه: سیستم پیشنهاددهندهسبک یادگیرییادگیری بر اساس منابع آموزشییادگیری پیشرفته آموزشیرتبه‌بندی صفحاتشخصی‌سازی,
چکیده مقاله :
سیستم‌های پیشنهاددهنده شخصی‌سازی شده، در کنار موتورهای جستجو، راهکاری برای غلبه بر مشکل گرانباری اطلاعات هستند به گونه‌ای که در آنها سعی می‌شود با استفاده از تکنیکهای هوشمند و تعاملات کاربران در وب، محیطی شخصی‌سازی شده جهت تولید پیشنهادات مناسب فراهم آید. یکی از زمینههای کاربردی برای این سیستم‌ها، محدوده یادگیری تطبیقی است. یک زیرسیستم پیشنهاددهنده، ماژول مهمی از سیستم‌های تطبیقی آموزشی است که وظیفه ارائه اشیای آموزشی مناسب به کاربر خود را دارد. کاربران گوسفند خاکستری، یکی از چالش‌های مطرح‌شده در این دسته از سیستم‌ها هستند. این کاربران تشابه کمی با دیگر فراگیران دارند لذا پیشنهادات ارائه‌شده برای دیگران و یا بر اساس عادات قبلی آنها، لزوماً مناسب این نوع از کاربران نیست. برای حل این چالش، راهکار مطرح‌شده در این مقاله، استخراج فراداده صفحات آموزشی وب و تطابق آنها با خصوصیت سبک یادگیری کاربر در قالب یک فرمول رتبه‌بندی، جهت ارائه مناسب‌ترین پیشنهاد منبع آموزشی برای یک فراگیر است. تعیین سبک یادگیری کاربر، بر اساس مدل فلدر- سیلورمن انجام می‌گیرد. سپس بر طبق میزان آن، صفحات مناسب برای آموزش و یادگیری بر حسب پارامترهای صریح و پنهان تعریف‌شده در آن صفحات، استخراج و پیشنهاد می‌گردند. پاسخ سیستم به پرس و جوی کاربر در قالب خروجی ایده مطرح‌شده، به وی نمایش داده می‌شود. همچنین کاربر می‌تواند جواب پیشنهادات برای سؤال خود را با خروجی الگوریتم لوسین که در اکثر موتورهای جستجو مورد استفاده است، برای مقایسه میزان مناسب‌بودن آنها مشاهده نماید. کاربر میزان مفیدبودن پیشنهادات مطرح‌شده را به سیستم بازخورد می‌دهد. این بازخورد برای الگوریتم اتوماتای یادگیر تعریف‌شده برای تولید نتایج و پیشنهادات بعدی، مورد استفاده قرار می‌گیرد. نمونه‌ای از سیستم پیاده‌سازی شده، در محیط آموزشی دانشگاهی، مورد ارزیابی دانشجویان قرار گرفته که نتایج بازخورد کاربران نشان از بهبود عملکرد سیستم نسبت به الگوریتم‌های متعارف جستجوی عمومی دارد. این سیستم را می‌توان به صورت یادگیری رسمی و غیر رسمی بر پایه منبع در محیط وب، مورد استفاده قرار داد.
Personalized recommender systems and search engines, are two effective key solutions to overcome the information overloading problem. They use the intelligent techniques on users’ interactions to extract their behavioral patterns. These patterns help in making a personalized environment to deliver accurate recommendations. In the technology enhanced learning (TEL) field and in particular resource-based learning, recommendation systems have attracted growing interest. Specially, they are an important module of Adaptive Educational Systems that deliver the appropriate learning objects to their users. Gray-sheep users are a challenge in these systems. They have a little similarity with their peers. So the recommendations to others are not suitable for them. To overcome this problem, we propose the idea of accommodating the user’s learning style to web page features. The user's learning style will be computed according to Felder-Silverman theory. On the other hands, the necessary implicit and explicit meta data will be extracted from OCW web pages. By matching the extracted information, the system delivers the appropriate educational links to user. The user can compare the proposed links, based of our algorithm, to the output of Lucene algorithm. A user’s opinion about every web page as a recommended result would be submitted to the system. The system uses a learning automata algorithm and user’s feedback to deliver best recommendations. The system has been evaluated by a group of engineering students to evaluate its accuracy. Results show that the proposed method outperforms the general search algorithm. This system can be used at formal and informal learning and educational environments for Resource-based learning.
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