عنوان مقاله
ارزیابی تلفیق منطق فازی با مدل دانش آموزی محیط یادگیری بر مبنای وب
فهرست مطالب
مقدمه
کارهای مرتبط
مدل دانش آموزی
تلفیق منطق فازی با مدل دانش آموزی
ارزیابی
نتیجه گیری
بخشی از مقاله
مدل دانش آموز
به منظور دستیابی به انطباق پذیری، سیستم باید از دانش ، نیازها، ویژگیها و مفاهیم نادرست هر فراگیر مطلع باشد. بنابراین، لازم است مدل دانش آموزی ساخته شود که در هر سیستم آموزشی انطباقی یا هوشمند مولفه اصلی به شمار می رود، این سیستم ها بسیاری از ویژگیهای دانش آموزان مثل دانش و صفات فردی را نمایش می دهند و کلیدی برای آموزش بر مبنای دانش فردی نامیده شده اند.
در محیط یادگیری اینترنتی،هدف مدلسازی حالات ادراکی هر فراگیر می باشد، بنابراین، سیستم فراگیری یا عدم فراگیری فراگیر ، فراموش شدن مفاهیم دانش حوزه را می تواند بازشناسی کند. به منظور مدلسازی دانش کاربر، از یک مدل جایگذاشت استفاده می گردد. (شکل 1)
کلمات کلیدی:
Evaluating the integration of fuzzy logic into the student model of a web-based learning environment Konstantina Chrysafiadi ⇑ , Maria Virvou Department of Informatics, University of Piraeus, Karaoli & Dimitriou Str., 80, GR-18534 Piraeus, Greece article info Keywords: Student model Fuzzy logic Adaptive systems Evaluation abstract In this paper, we evaluate the effectiveness and accuracy of the student model of a web-based educational environment for teaching computer programming. Our student model represents the learner’s knowledge through an overlay model and uses a fuzzy logic technique in order to define and update the student’s knowledge level of each domain concept, each time that s/he interacts with the e-learning system. Evaluation of the student model of an Intelligent Tutoring System (ITS) is an aspect for which there are not clear guidelines to be provided by literature. Therefore, we choose to use two well-known evaluation methods for the evaluation of our fuzzy student model, in order to design an accurate and correct evaluation methodology. These evaluation models are: the Kirkpatrick’s model and the layered evaluation method. Our system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus, in order to learn how to program in the programming language C. The results of the evaluation were very encouraging. 2012 Elsevier Ltd. All rights reserved. 1. Introduction The last decades the interest on web-based learning environments and tools has been witnessed a rapid growth. However, web-based learning environments deal with the varying backgrounds and heterogeneous needs of learners. Student’s individual differences play a central role in web-based learning (Graf & Kinshuk, 2010), and a way to deal with these is the Intelligent Tutoring Systems (ITS), which belong to an advanced generation of computer-based instruction systems that provide students with highly personalized learning experience by adapting the content and its presentation to the student’s needs and preferences (Jeremic´, Jovanovic´, & Gaseˇvic´, 2012). Therefore, the need of developing a web-based educational system that can offer dynamic adaptation to each individual student is arisen. Adaptive e-learning is suitable for teaching heterogeneous student populations in higher education (Schiaffino, Garcia, & Amandi, 2008). Creating an adaptive learning system that meets students’ requirements can be challenging since students learn with not only different needs, but also different learning characteristics (Lo, Chan, & Yeh, 2012). So, when creating an adaptive webbased educational application, we have to focus on the student model, which is a core component in any intelligent or adaptive tutoring system that represents many of the student features such as knowledge and individual traits (Brusilovsky & Millán, 2007). Student modeling can be defined as the process of gathering relevant information in order to infer the current cognitive state of the student, and to represent it so as to be accessible and useful to the ITS for offering adaptation (Thomson & Mitrovic, 2009). The most widely used technique in the field of user modeling is the overlay model. The main idea of the overlay modeling is that the learner model is a subset of the domain model (Martins, Faria, Vaz de Carvalho, & Carrapatoso, 2008; Vélez, Fabregat, Nassiff, Petro, & Fernandez, 2008). However, student modeling, in many cases, deals with uncertainty and one possible approach to encounter this is fuzzy logic. Integrating fuzzy logic into the student model of an ITS is a good idea, since the fuzzy logic based methods are more consistent with the human-being decision-making processes (Shakouri & Tavassoli, 2012). Although, the adaptation generated by user modeling techniques often tend to improve the user-system interaction, most of the time the exploitation of such techniques makes the system more complex and consequently, it should be evaluated whether the adaptivity really improves the system and whether the user really prefers the adaptive version of it (Gena, 2005). The evaluation of adaptive systems is a difficult task due to the complexity of such systems, as shown by many studies (Lavie, Meyer, Beugler, & Coughlin, 2005; Markham et al., 2003; Missier & Ricci, 2003). Thereby, evaluators need to ensure that correct evaluation methods and measurement metrics are used (Mulwa, Lawless, Sharp, & Wade, 2011). In Intelligent Tutoring Systems community, the common practice of evaluation is to perform experiment with a 0957-4174/$ - see front matter 2012 Elsevier
