استخراج مسير يادگيري بدون نظارت در سيستم هاي آنلاين فراگير

unsupervised learning path extraction in massive online course systems (MOOC)

با كمي تعمق و بررسي پيرامون رفتار انسان در زمينه انجام فعاليت هاي گوناگون مي توان نتيجه گرفت هر فرد رويكردي متفاوت براي انجام يك فعاليت دارد. بررسي هاي انجام شده نشان مي دهد اين رويكرد متفاوت بين افراد با ويژگي هاي مشابه تا حدودي شباهت داشته و مي توان الگوي رفتاري افراد مشابه را استخراج كرد. اين رويكرد در زمينه كسب دانش نيز وجود داشته و قابل بررسي است چرا كه سلسله مراتبي كه هر فرد براي كسب دانش پيرامون يك موضوع اتخاذ مي كند با وجود تفاوتها، قابل بررسي و بهبود است. هدف اصلي در واقع امكان سنجي و يافتن روشي براي ارائه مسير يادگيري بدون نظارت با توجه به مشابهت الگوي رفتاري افراد در زمينه كسب دانش است.

With a little reflection on human behavior in various activities, one can conclude that each person has a different approach to doing stuff. Studies have shown that this different approach is in fact similar to those with similar characteristics, and can be derived from a pattern of behavior among similar individuals. This approach also exists in the context of knowledge acquisition, because the path that each person can take for knowledge acquisition can be different, but still patterns of behavior can be found between people and improve the process. The main goal of this research is to provide a proposing system that can create and recommend a personalized learning path by comparing the likeness of the individual to other users in order to improve the learning process. Our emphasis here is on providing a way to focus on the learning process and path suggestion that a person has to follow in order to get the knowledge that he is looking for and try to identify the user using the machine learning methods to identify the person. So the slope of the learning curve will be improved, and the user will have the most immediate way to reach the desired point.