In the United States, children are found to score lower in science and reading in comparison to the international level. This is partly because of their poor performance as readers. Even when students learn, they lack the depth to clearly understand the topic. However, even unfamiliar scientific texts can be comprehended by increasing self explanation skills. Therefore, the need for improving comprehension skills among students has become a necessity. iSTART (Interactive Strategy Trainer for Active Reading and Thinking) is a web-based automated tutor designed to help students become better readers via multi-media technologies, while R-SAT (Reading Strategy Assessment Tool) is a web-based assessment that monitors students' strategy usage. Different reading strategies included in this program are comprehension monitoring, paraphrasing, elaboration, predictions, and bridging. The system then evaluates and scores the self-explanation given by the student. The feedback is given by computations using Latent Semantic Analysis (LSA) and Word Matching Algorithms.This research focuses on an elaboration strategy, which encourages a reader to connect the information or text that they just read to their prior knowledge and to predict what the text will say next. For each text, elaboration benchmarks were created by chunking the text. The chunking was done by three methods: chunking target sentence method, chunking paragraph tag method, and entire subsequent sentence method.