Translating advances in reading comprehension research to educational practice

The authors review five major findings in reading comprehension and their implications for educational practice. First, research suggests that comprehension skills are separable from decodingprocesses and important at early ages, suggesting that comprehension skills should be targeted early, even before the child learns to read. Second, there is an important distinction between readingprocesses and products, as well as their causal relationship: processes lead to certain products. Hence, instructional approaches and strategies focusing on processes are needed to improve students’reading performance (i.e., product). Third, inferences are a crucial component of skilled comprehension. Hence, children need scaffolding and remediation to learn to generate inferences, even when they know little about the text topic. Fourth, comprehension depends on a complex interaction between the reader, the characteristics of the text, and the instructional task, highlighting the need for careful selection of instructional materials for individual students and specific groups of students. Finally, educators may benefit from heightened awareness of the limitations and inadequacies of standardized reading comprehension assessments, as well as the multidimensionality of comprehension to better understand their students’ particular strengths and weaknesses

Text-based recall and extra-textual generations resulting from simplified and authentic texts

This study uses a moving windows self-paced reading task to assess text comprehension of beginning and intermediate-level simplified texts and authentic texts by L2 learners engaged in a text-retelling task. Linear mixed effects (LME) models revealed statistically significant main effects for reading proficiency and text level on the number of text-based propositions recalled: More proficient readers recalled more propositions. However, text level was a stronger predictor of propositional recall than reading proficiency. LME models also revealed main effects for language proficiency and text level on the number of extra-textual propositions produced. Text level, however, emerged as a stronger predictor than language proficiency. Post-hoc analyses indicated that there were more irrelevant elaborations for authentic texts and intermediate and authentic texts led to a greater number of relevant elaborations compared to beginning texts

iSTART: Interactive Strategy Training for Active Reading and Thinking

Interactive Strategy Training for Active Reading and Thinking (iSTART) is a Web-based application that provides young adolescent to college-age students with high-level reading strategy training to improve comprehension of science texts. iSTART is modeled after an effective, human-delivered intervention called self-explanation reading training (SERT), which trains readers to use active reading strategies to self-explain difficult texts more effectively. To make the training more widely available, the Web-based trainer has been developed. Transforming the training from a human-delivered application to a computer-based one has resulted in a highly interactive trainer that adapts its methods to the performance of the students. The iSTART trainer introduces the strategies in a simulated classroom setting with interaction between three animated characters—an instructor character and two student characters— and the human trainee. Thereafter, the trainee identifies the strategies in the explanations of a student character who is guided by an instructor character. Finally, the trainee practices self-explanation under the guidance of an instructor character. We describe this system and discuss how appropriate feedback is generated

Text readability and intuitive simplification: A comparison of readability formulas

Texts are routinely simplified for language learners with authors relying on a variety of approaches and materials to assist them in making the texts more comprehensible. Readability measures are one such tool that authors can use when evaluating text comprehensibility. This study compares the Coh-Metrix Second Language (L2) Reading Index, a readability formula based on psycholinguistic and cognitive models of reading, to traditional readability formulas on a large corpus of texts intuitively simplified for language learners. The goal of this study is to determine which formula best classifies text level (advanced, intermediate, beginner) with the prediction that text classification relates to the formulas’ capacity to measure text comprehensibility. The results demonstrate that the Coh-Metrix L2 Reading Index performs significantly better than traditional readability formulas, suggesting that the variables used in this index are more closely aligned to the intuitive text processing employed by authors when simplifying texts

What's so simple about simplified texts? A computational and psycholinguistic investigation of text comprehension and text processing

This study uses a moving windows self-paced reading task to assess both text comprehension and processing time of authentic texts and these same texts simplified to beginning and intermediate levels. Forty-eight second language learners each read 9 texts (3 different authentic, beginning, and intermediate level texts). Repeated measures ANOVAs reported linear effects of text type on reading time (normalized for text length) and true/false comprehension scores indicating that beginning level texts were processed faster and were more comprehensible than intermediate level and authentic texts. The linear effect of text type on comprehension remained significant within an ANCOVA controlling for language proficiency (i.e., TOEFL scores), reading proficiency (i.e., Gates-MacGinitie scores), and background knowledge, but not for reading time. Implications of these findings for materials design, reading pedagogy, and text processing and comprehension are discussed

iSTART 2: Improvements for Efficiency and Effectiveness

iSTART (interactive strategy training for active reading and thinking) is a Web-based reading strategy trainer that develops students\u27 ability to self-explain difficult text as a means to improving reading comprehension. Its curriculum consists of modules presented interactively by pedagogical agents: an introduction to the basics of using reading strategies in the context of self-explanation, a demonstration of self-explanation, and a practice module in which the trainee generates self-explanations with feedback on the quality of reading strategies contained in the self-explanations. We discuss the objectives that guided the development of the second version of iSTART toward the goals of increased efficiency for the experimenters and effectiveness in the training. The more pedagogically challenging high school audience is accommodated by (1) a new introduction that increases interactivity, (2) a new demonstration with more and better focused scaffolding, and (3) a new practice module that provides improved feedback and includes a less intense but more extended regimen. Version 2 also benefits experimenters, who can set up and evaluate experiments with less time and effort, because pre- and post testing has been fully computerized and the process of preparing a text for the practice module has been reduced from more than 1 person-week to about an hour\u27s time

ReaderBench: An Integrated Cohesion-Centered Framework

Dascalu, M., Stavarache, L.L., Dessus, P., Trausan-Matu, S., McNamara, D.S., & Bianco, M. (2015). ReaderBench: An Integrated Cohesion-Centered Framework. In G. Conole, T. Klobucar, C. Rensing, J. Konert & É. Lavoué (Eds.), 10th European Conf. on Technology Enhanced Learning (pp. 505–508). Toledo, Spain: Springer.ReaderBench is an automated software framework designed to support both students and tutors by making use of text mining techniques, advanced natural language processing, and social network analysis tools. ReaderBench is centered on comprehension prediction and assessment based on a cohesion-based representation of the discourse applied on different sources (e.g., textual materials, behavior tracks, metacognitive explanations, Computer Supported Collaborative Learning – CSCL – conversations). Therefore, Reader‐Bench can act as a Personal Learning Environment (PLE) which incorporates both individual and collaborative assessments. Besides the a priori evaluation of textual materials’ complexity presented to learners, our system supports the identification of reading strategies evident within the learners’ self-explanations or summaries. Moreover, ReaderBench integrates a dedicated cohesion-based module to assess participation and collaboration in CSCL conversations.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains

Changing How Students Process and Comprehend Texts with Computer-based Self-Explanation Training

This study assessed whether and how self-explanation reading training, provided by iSTART (Interactive Strategy Training for Active Reading and Thinking), improves the effectiveness of comprehension processes. iSTART teaches students how to self-explain and which strategies will most effectively aid comprehension from moment-to-moment. We used RSAT (Reading Strategy Assessment Tool) to assess how iSTART changes the relation between important selfexplanation reading strategies—bridging and elaboration—and online comprehension, and how often they are produced. College and high school students received iSTART and were administered RSAT prior to and post-training. Results from three experiments showed that iSTART primarily benefits bridging inferences when self explaining. The frequency of bridging inferences was higher post training than prior to training, but only in the experiments involving college students. Additionally, prior to exposure to iSTART, RSAT bridging scores did not predict comprehension performance, whereas they did after iSTART, suggesting that iSTART may improve comprehension processes by teaching students how to appropriately use selfexplanation to address comprehension difficulties. Finally, the results from this study suggest that RSAT may provide a valuable computer-based assessment of the effectiveness of selfexplanations that could be used in conjunction with iSTART and in future research on selfexplanation