Retrieval of Authentic Documents for Reader-Specific Lexical Practice

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.