When a teacher gives a reading assignment in today s language learning classrooms, all of the students are almost always reading the same text. Although students have different reading levels, it is impractical for a single teacher to seek out unique texts matched to each student s abilities. In this paper, we describe REAP, a system designed to assign each student individualized readings by combining detailed student and curriculum modelling with the large amount of authentic materials on the Web. REAP is designed to be used as an additional resource in teacher-led classes, as well as to be used by reading comprehension researchers for testing hypotheses on how to improve reading skills for L1 as well as L2 learners. Vocabulary acquisition is the primary factor we use in matching texts to a student s abilities. The system can also prioritise different criteria during the search. For instance, the system can retrieve documents based solely on the vocabulary terms needed to progress toward the next level, thereby focusing on curriculum. REAP can take into account other goals, such as student interests, special topics decided by the teacher, or an upcoming test, all represented as word histograms. This allows teachers to decide what they want the students to focus on each day. We also describe the contributions of this project, including an open-corpus, authentic-materials approach to reading practice and word-level modelling of norms and student skills. Finally, we describe how learning researchers can use this tool to get fine-grained control over the selection of reading materials, so that they can more easily test a variety of new learning hypotheses.

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.

In this paper we consider the problem of building a system to predict readability of natural-language documents. Our system is trained using diverse features based on syntax and language models which are generally indicative of readability. The experimental results on a dataset of documents from a mix of genres show that the predictions of the learned system are more accurate than the predictions of naive human judges when compared against the predictions of linguistically-trained expert human judges. The experiments also compare the performances of different learning algorithms and different types of feature sets when used for predicting readability. 1

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In today's language-learning classrooms, all of the students in a class almost always have the same text to read. Although students have different reading levels, it is impractical for a single teacher to find unique texts matched to each student's abilities. The REAP system was developed to make the process of providing students with individualized texts practical. The texts come in the form of authentic documents retrieved from the Web, and the system tracks and assesses students ’ knowledge as they use the system. The system is able to find documents that meet various and individualized criteria. In this paper, we describe our work on modeling lexical familiarity. In particular, we detail the approaches taken for modeling the student's vocabulary knowledge, the contents of documents in the corpus, and the components of the curriculum. We also address related and future work. KEY WORDS User modeling and adaptation, computer-based learning, language-learning, and reading-level personalization. 1.

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