In this paper we present an ongoing research project which aims at developing an electronic vocabulary acquisition system for the German and the Italian language. To ensure maximum effectiveness of the learning process, modern psycholinguistic methods are applied alongside with new media and technologies including adaptive hypermedia. In this paper we focus on adaptive features and we will outline first ideas about the use of adaptation technologies for vocabulary acquisition. 1

This paper describes the design and evaluation of BRIX, an environment for online learning of second languages. A needs analysis identified specific requirements of online language learning. Commercial course management systems were determined to be inadequate with respect to these requirements. BRIX was developed to address the need for a generic language learning environment that fulfills language educators’ requirements focusing on reading, writing, and listening activities and can easily be customized for different language courses. The design of BRIX is based on pedagogic approaches and theories of teaching and learning second languages and on the results of analytic and empirical evaluation of test versions of the software. In this paper we describe the needs analysis and the design of BRIX, and present an evaluation that compares the use and usability of a Chinese course in BRIX to a previous handcrafted version of the same course.

With increasing popularity of web based learning, it is required to design the web layout to reduce cognitive load. Cognitive load theory is widely used to predict the effectiveness of the web based and multimedia learning. The cognitive load induced by instructional and multimedia modes are measured by indirect or subjective methods. Questionnaires are one common form of measuring cognitive load indirectly. In this paper, a questionnaire is prepared to identify the cognitive load of the student and his website preferences in a web learning environment. The cognitive attributes are used as the training input for the Naïve Bayes, Classification Regression Tree(CART), Random Forest and Random Tree for classification. Based on the response of the user, areas for improvement in layout of the web learning system are identified.