Enhancing Learning from Informatics Texts

Abstract

Previous studies have demonstrated that high-knowledge readers learn more from low-coherence than high-coherence texts in the domain of Informatics and specifically in the domain of Local Network Topologies. This study explored deeply the research hypothesis that this characteristic is due to the use of knowledge to fill in the gaps in the text, resulting in an integration of the knowledge of the text with background knowledge. Participants were 65 eighth semester undergraduate students of the department of Informatics and Telecommunications, University of Athens, who had been taught and successfully completed the “Data Transmission and Networks Communications” course in the fourth semester of their studies, so they were considered as high-knowledge readers. Participants’ comprehension was examined through free-recall measure, text-based questions, elaborative-inference questions, bridging-inference questions, problem-solving questions, and a sorting task. We found that readers with high background knowledge performed better after reading the low-coherence text. We support that this happens because the low-coherence text forces the readers with high background knowledge to engage in compensatory processing to infer unstated relations in the text.