In the REAP system, users are automatically provided with texts to read targeted to their individual reading levels. To find appropriate texts, the user’s vocabulary knowledge must be assessed. We describe an approach to automatically generating questions for vocabulary assessment. Traditionally, these assessments have been hand-written. Using data from WordNet, we generate 6 types of vocabulary questions. They can have several forms, including wordbank and multiple-choice. We present experimental results that suggest that these automatically-generated questions give a measure of vocabulary skill that correlates well with subject performance on independently developed humanwritten questions. In addition, strong correlations with standardized vocabulary tests point to the validity of our approach to automatic assessment of word knowledge. 1

This report discusses in detail methods of plagiarism and its detection in both natural and programming languages. The increase of material now available in electronic form and improved access to this via the Internet is allowing, with greater ease than ever before, plagiarism that is either intentional or unintentional. Due to increased availability of On-line material, people checking for plagiarism are finding the task increasingly harder. Techniques for detecting both plagiarism in natural and programming languages are discussed in this report to provide the reader with a comprehensive introduction to this area. Also provided are examples of common techniques used in natural language plagiarism.

Abstract—Skilled readers are able to derive meaning from a stream of visual input with remarkable efficiency. In this article, we present the first evidence that statistical information latent in the linguistic environment can contribute to an account of reading behavior. In two eyetracking studies, we demonstrate that the transitional probabilities between words have a measurable influence on fixation durations, and using a simple Bayesian statistical model, we show that lexical probabilities derived by combining transitional probability with the prior probability of a word’s occurrence provide the most parsimonious account of the eye movement data. We suggest that the brain is able to draw upon statistical information in order to rapidly estimate the lexical probabilities of upcoming words: a computationally inexpensive mechanism that may underlie proficient reading. The exploitation of redundancy in the input is a pervasive processing strategy; research in areas of visual perception such as motion detection

Process modelling has over the years become an essential skill in Information Systems and Business Process Management practice. Consequently, more and more training programs have evolved, teaching different process modelling languages. Two popular process modelling languages are being compared in this experimental study. Experiment participants received extensive training in one language but not the other, leading to the expectation that learning outcomes would be better in the case of the familiar language. Our study provides empirical evidence that this is not the case. In fact, it is shown that participants achieved similar learning outcomes when confronted with the unfamiliar language. Our results lead to a fundamental question, namely whether it is actually an important teaching decision what sort of process modelling language is being taught. Our findings suggest that education and research in process modelling should focus on aspects other than the style, nature or features of languages and tools.

The principles of readability are in every style manual. Readability formulas are in every word processor. What is missing is the research and theory on which they stand.

Suregen-2 applications are intended for use as add-on modules for clinical infor- mation systems. Currently, Suregen-2 permits refinement of the predefined medical ontology, specification of text plans and description knowledge for objects of the ontology. It has built-in constructs for referential expressions, aggregation, enumeration and recurrent semantic constellations. A first application built with Suregen-2, which currently supports German only, is in routine use.

financial [1] and learning [2] benefits of enabling students in resource-constrained schools to share one computer. In India, the lack of Multiple Mice authoring tools coupled with teachers ‘ low computer proficiency means little or no customized content is created. This is problematic as the capability to create digital content enables teachers to prepare digital lessons that address the particular learning needs of their students [3], [4], [5]. In this paper, we report on a 34-week field study in three Indian peri-urban schools. We identify key issues impeding digital content creation by low computer proficiency teachers. We also present an authoring framework, Cloze, which successfully enables these teachers to create content for MultiPoint applications. Finally, we recommend guidelines for designing authoring tools for teachers with low computer proficiency. Index Terms — Authoring tools, computers and education, teachers, user interfaces I.

This article presents SERIF, a new model of eye movement control in reading that integrates an established stochastic model of saccade latencies (LATER; R. H. S. Carpenter, 1981) with a fundamental anatomical constraint on reading: the vertically split fovea and the initial projection of information in either visual field to the contralateral hemisphere. The novel features of the model are its simulation of saccade latencies as a race between two stochastic rise-to-threshold LATER units and its probabilistic selection of the target for the next saccade. The model generates simulated eye movement behavior that exhibits important characteristics of actual eye movements made during reading; specifically, simulations produce realistic saccade target distributions and replicate a number of critical reading phenomena, including the effects of word frequency on fixation durations, the inverted optimal viewing position effect, the trade-off between first and second fixation durations of refixated words, and the dependence of parafoveal preview benefit on eccentricity.

The paper describes a computational approach for guessing the meanings of previously unaccounted words in an implemented system for natural language processing. Interested in comparing the results to what is known about human guessing, it reviews a largely educational approach, partially based on cognitive psychology, to teaching humans, mostly children, to acquire new vocabulary from contextual clues, as well as the lexicographic efforts to account for neologisms. It then goes over the previous NLP efforts in processing new words and establishes the difference—mostly, much richer semantic resources—of the proposed approach. Finally, the results of a computer experiment that guesses the meaning of a non-existent word, placed as the direct object of 100 randomly selected verbs, from the known meanings of these verbs, with methods of the ontological semantics technology, are presented and discussed. While the results are promising percentage-wise, ways to improve them within the approach are briefly outlined.

Many textbooks written in emerging countries lack clear and adequate coverage of important concepts. We propose a technological solution for algorithmically identifying those sections of a book that are not well written and could benefit from better exposition. We provide a decision model based on the syntactic complexity of writing and the dispersion of key concepts. The model parameters are learned using a tune set which is algorithmically generated using a versioned authoritative web resource as a proxy. We evaluate the proposed methodology over a corpus of Indian textbooks which demonstrates its effectiveness in identifying enrichment candidates.