Mining Formative Evaluation Rules Using Web-based Learning Portfolios for Web-based Learning Systems
نویسندگان
چکیده
Learning performance assessment aims to evaluate what knowledge learners have acquired from teaching activities. Objective technical measures of learning performance are difficult to develop, but are extremely important for both teachers and learners. Learning performance assessment using learning portfolios or web server log data is becoming an essential research issue in web-based learning, owing to the rapid growth of e-learning systems and real application in teaching scenes. The traditional summative evaluation by performing examinations or feedback forms is usually employed to evaluate the learning performance for both the traditional classroom learning and the web-based learning. However, summative evaluation only considers final learning outcomes without considering learning processes of learners. This study presents a learning performance assessment scheme by combining four computational intelligence theories, i.e., the proposed refined K-means algorithm, the neuro-fuzzy classifier, the proposed feature reduction scheme, and fuzzy inference, to identify the learning performance assessment rules using the web-based learning portfolios of an individual learner. Experimental results indicate that the evaluation results of the proposed scheme are very close to those of summative assessment results of grade levels. In other words, this scheme can help teachers to assess individual learners precisely utilizing only the learning portfolios in a web-based learning environment. Additionally, teachers can devote themselves to teaching and designing courseware since they save a lot of time in evaluating learning. This idea can be beneficially applied to immediately examine the learning progress of learners, and to perform interactively control learning for elearning systems. More significantly, teachers could understand the factors influencing learning performance in a web-based learning environment according to the obtained interpretable learning performance assessment rules.
منابع مشابه
Optimizing Membership Functions using Learning Automata for Fuzzy Association Rule Mining
The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since t...
متن کاملA User-Centred Personalised e-Learning System
The paper proposes a framework for understanding the factors that affect usability of e-learning. The framework can be applied to the development of (1) a formative usability evaluation method for e-learning systems and (2) personalisation rules for e-learning systems interface. The formative usability evaluation method is intended for the evaluation of e-learning systems during its development...
متن کاملRRLUFF: Ranking function based on Reinforcement Learning using User Feedback and Web Document Features
Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the rank...
متن کاملDevelopment of a Combined System Based on Data Mining and Semantic Web for the Diagnosis of Autism
Introduction: Autism is a nervous system disorder, and since there is no direct diagnosis for it, data mining can help diagnose the disease. Ontology as a backbone of the semantic web, a knowledge database with shareability and reusability, can be a confirmation of the correctness of disease diagnosis systems. This study aimed to provide a system for diagnosing autistic children with a combinat...
متن کاملDevelopment of a Combined System Based on Data Mining and Semantic Web for the Diagnosis of Autism
Introduction: Autism is a nervous system disorder, and since there is no direct diagnosis for it, data mining can help diagnose the disease. Ontology as a backbone of the semantic web, a knowledge database with shareability and reusability, can be a confirmation of the correctness of disease diagnosis systems. This study aimed to provide a system for diagnosing autistic children with a combinat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Educational Technology & Society
دوره 9 شماره
صفحات -
تاریخ انتشار 2006