TOWARDS A KNOWLEDGE-BASED FREE-TEXT RESPONSE ASSESSMENT SYSTEM Panagiotis Blitsas Interdisciplinary Program of Graduate Studies in Basic & Applied Cognitive Science National & Kapodistrian University of Athens Panepistimioupolis, Ilissia, Athens 15784, Greece Maria Grigoriadou Department of Informatics & Telecommunications National & Kapodistrian University of Athens Panepistimioupolis, Ilissia, Athens 15784, Greece ABSTRACT In the present work, the architecture of a knowledge-based free-text response assessment system is presented, which can assess free-text responses on open-ended questions based on text comprehension theories. Its main advantages are the facts of extending the knowledge base and assessing responses on questions of different types. It is constituted from three basic modules: The first module is the Normalization Module (NoM), which is responsible for converting free-text responses into normalized responses, as well as, converting technical text into “functional system”. The second module is the Functional System Module (FSM), which depicts all entities of a technical text and the relations among them, and is presented, by the representation of microstructure and macrostructure of a Computer Networks domain technical text, according to Denhiere-Baudet Text Comprehension Model. The third module is the Assessment Module (AM), responsible for assessing the normalized responses. It is based on data, obtained by experimental studies on microstructure and macrostructure, constructed by secondary school students, during reading technical texts and responding to questions. KEYWORDS Assessment, Functional system, Microstructure, Macrostructure. 1. INTRODUCTION Nowadays, much effort is put in creating models to simulate psycholinguistic theories with regard to human processes of text comprehension, as the Construction-Integration Model (Kintsch, 2001; Kintsch, 1992), the Theory of Latent Semantic Analysis of Knowledge Representation (Landauer & Dumais 1997), and Denhiere-Baudet Text Comprehension Model (Baudet & Denhiere 1992). Text comprehension models use cognitive representations, which focus in deeper levels of understanding, such as conceptual conclusions based on knowledge, inductive reductions and world knowledge, combined with the surface levels of understanding, which involve lexical processing, syntactic analysis and text interpretation. Our research is focused on Denhiere-Baudet Text Comprehension Model, because it is the most appropriate for elaborating technical text, after analyzing the cognitive categories that are involved in such a kind of texts. According to this model, the individual, while reading a technical text, manufactures, progressively, its representation, namely atoms, states, events and actions of the world that is described in the text, as well as, temporal and causal relations, which connect these cognitive categories. The term 'atom' is used for the entities that participate in the knowledge representation. The term 'state' is static and describes a situation where no change happens during a period of time. The term 'event' describes an action that causes changes, not evoked by a person, but by a non-human action e.g. a machine. An 'action' causes changes and is evoked by a person. IADIS International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2008) 37