مقالههای Ahmad A. Kardan
توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقالههای نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده میشوند.
اطلاعات انتشار: چهارمین کنفرانس ملی و اولین کنفرانس بین المللی آموزش الکترونیک، سال ۱۳۸۸
تعداد صفحات: ۶
Recommender systems play an important role in learning process by predicting user preferences. Learning process needs dynamic interactions between the learner and the learning system to recognize learner abilities, behaviors or other learner characteristics. Recommender systems have become increasingly popular in entertainment and e–commerce domains, but they have a little success in the elearning domains. Recommender systems learn about user preference over time, automatically finding things of similar interest. It reduces the burden of creating explicit queries during the learning process. Recommender systems use some techniques to recognize learners' preferences, such as filtering, machine learning techniques or hybrid techniques. In e–learning, some of these techniques can cause some problems or may be impossible to implement. This paper investigates a technique for recommender systems suitable for the learning environments to recognizing learners' preferences in the learning process. This technique predicts user preferences in order to identify a useful set of items and to be recommended in response to the learners specific information need. We propose a hybrid technique based on machine learning to recognize learner preferences and predict theirs required contents with high accuracy.<\div>
اطلاعات انتشار: اولین کنفرانس دانشجویی آموزش الکترونیکی، سال ۱۳۸۸
تعداد صفحات: ۵
The World–Wide Web is becoming the most important media for collecting, sharing and distributing information. Distance education is a field where web–based technology was very quickly adopted and used for course delivery and knowledge sharing. Educators, using Web–based learning environments, are in desperate need for implicit and automatic ways to get objective feedback from learners in order to better follow the learning process. Moreover, these educators have very little support to evaluate learners’ activities and distinguish between different learners’ online behaviors. In this paper, we discus some important research contributes in web mining techniques in e–learning, with the goal of providing a broad overview rather than an in–depth analysis. We also have briefly compared four e–learning systems using different techniques<\div>
اطلاعات انتشار: دوازدهمین کنفرانس ملی سیستمهای هوشمند، سال ۱۳۹۲
تعداد صفحات: ۵
Part of Speech (POS) tagging is one of the fundamental steps in various speech and text processing applications. POS tagging is the process of assigning the words ininput sentences with their categories according to their contextual and grammatical properties. In addition to the generalPOS tagging difficulties such as the disambiguation of multicategorywords and unknown words, the Persian language,unlike the English language, is a free order language and it has its own characteristics. These challenges can greatly affect the quality of the part–of–speech tagging process. An efficient POStagging process has been developed for some languages, especially for the English language, but just a few researches have been done on the Persian language. To address these issues and achieve high POS tagging accuracy, we chose features which can show the important characteristics of words in a sentence, aswell as maximum entropy as a machine learning classifier. Experimental results show that the proposed Persian POStagging system outperforms the other state–of–the–art Persian taggers.<\div>
اطلاعات انتشار: International Journal Information and Communication Technology Research، هفتم،شماره۱، winter ۲۰۱۴، سال ۰
تعداد صفحات: ۱۰
The learner model represents essential information about characteristics of learner. The Adaptive Educational Systems and Intelligent Torturing Systems use learner model to adapt required learning services according to characteristics of each learner. Hence, the accuracy of learner model is an important issue. A learner model is called “open” if its parameters could be inspected, discussed or changed by users. In this paper a novel method is proposed to improve accuracy of learner model based on learner knowledge and learner belief about his\her model. For this purpose the overlay learner modeling with Bayesian networks is used to represent learner knowledge. Then according to nature of open learner model, the learner model is presented as skill meter and learner could state his\her belief about it. Then the model is updated through proposed method. Finally the method is evaluated by use of a comprehensive test and t–student test. The results show our method improves accuracy of learner model.
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