GI-tutor: grammar-checking for (Italian) students of German

Computers have played a significant role in second language learning as they can offer different types of language activities and provide learners with immediate and appropriate feedbacks. In ICALL systems, learning is activated by focusing the learner's attention to the correct form and comparing it to the wrong one. Feedback offers an explicit explanation of the mistakes made by the student. The focus of this paper is a grammar checker designed for Italian native speakers learning German - GI-tutor. The system's structure was taken from a previous study [28] and enforced to analyse a conspicuous number of sentences, with different sentence and phrase structures. The lexicon used has been manually organized at the beginning, with some 8,000 entries overall; then, an enlargement has been obtained through an adaptation of the lexicon made available by Hamburg University Constraint Dependency Grammar (JWCDG) and downloaded from their website . A corpus containing wrong sentences was expanded by extracting data from exams written by first-year students of the German course at the University Ca' Foscari. The errors were then classified in order to obtain a general statistical analysis of the main problems encountered when learning German. Attention was given also to parsers and their use and functionality in language learning. Furthermore, the performance of the constituency grammar checker was evaluated to determine the types and frequencies of errors it can successfully diagnose. This was done by comparing it to ParZu - a generic German dependency parser developed at the University of Zürich and to Stanford Parser for German

Design and Implementation of an Intelligent Part of Speech Generator

The aim of this paper is to report on an attempt to design and implement an intelligent system capable of generating the correct part of speech for a given sentence while the sentence is totally new to the system and not stored in any database available to the system. It follows the same steps a normal individual does to provide the correct parts of speech using a natural language processor. It uses both morphological and syntactic analysis of the input to arrive at correct part of speech. It, then, checks to see if the    correct part of speech is provided. If not, it displays the    correct part of speech with a short note referring to the specific rule responsible for the selection of correct part of speech. This tool can be used to help learners master English parts of speech syste

Bayesian expert systems and multi-agent modeling for learner-centric Web-based education

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (leaves 140-150).Online distance education provides students with a wealth of information. When students submit course-related search term queries, the search engine returns the search hits based on keyword and topic match. A student's particular learning style is not taken into consideration. For instance, a visually oriented student may benefit more than others from viewing videos and interacting with simulations. We address this problem by designing and developing a knowledge-based system for the initial assessment of students' learning styles. Each student's membership in a learning style dimension (e.g. visual or verbal) is estimated probabilistically. We reach this probability value by using a sequential Bayesian approach to administer a dynamic questionnaire that aims to attain a desired confidence level estimate with the minimal number of questions. A multi-agent online tutoring system uses this initial learning style model to start suggesting learning material matching the student's style. Each agent is an expert in a learning style dimension and can suggest the learning materials matching the student's style. In addition, these agents closely follow the student's evolving preferences and continuously update the stochastic model based on the student's online activities. When the student searches for course material, the multi-agent system delivers the search matches in a cycle-free preference order influenced by the students' multi-dimensional learning style model.by Ralph Rizkallah Rabbat.Ph.D

An adaptive domain-independent agents-based tutor for Web-based supplemental learning environments

Thesis (Ph. D .)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2003.Includes bibliographical references (p. 170-185).The Physics Interactive Video Tutor (PIVoT) is a Web-based multimedia resource for college-level Newtonian mechanics. The Personal Tutor (PT) is an intelligent tutoring system (ITS) integrated into PIVoT, assisting students and teachers in navigating through, understanding, and assessing PIVoT's educational media. PT is adaptive in that it personalizes its functionality to the preferences of its user. The combined PIVoT / PT system was designed to be domain-independent with respect to the style of pedagogy, models of user learning, and instructional algorithms. Thus, this design is easily adapted for use beyond the tested domain of introductory college physics. PT is designed in the object-oriented paradigm, building upon the recent work in multi-agent systems (MAS). This agents-based approach, along with innovations in negotiating student-agent control and communication, allow current and future competing pedagogical strategies and cognitive theories to coexist harmoniously. New efficient, domain-independent techniques for discovering, updating, and presenting students' contextual interests improve information retrieval and site navigation. Unlike other computer-based instruction systems used as a tool for primary learning and assessment, PIVoT is used as a supplementary resource focusing on providing formative assessment to both student and educator alike. The PIVoT / PT system leverages reusability and system independence, two often-overlooked strengths of agent-based approaches to intelligent tutoring systems. Combined, PIVoT and the Personal Tutor provide an effective proving ground for innovations in intelligent tutoring system design that also reduces the cost of making such software.by Steven Niemczyk.Ph.D