Exploring the effect of background knowledge and text cohesion on learning from texts in computer science

In this study, we examine the effect of background knowledge and local cohesion on learning from texts. The study is based on construction-integration model. Participants were 176 undergraduate students who read a Computer Science text. Half of the participants read a text of maximum local cohesion and the other a text of minimum local cohesion. Afterwards, they answered open-ended and multiple-choice versions of text-based, bridging-inference and elaborative-inference questions. The results showed that students with high background knowledge, reading the low-cohesion text, performed better in bridging-inference and in elaborative-inference questions, than those who read the high-cohesion text. Students with low background knowledge, reading the high-cohesion text, performed better in all types of questions than students reading the low-cohesion text only in elaborative-inference questions. The performance with open-ended and multiple-choice questions was similar, indicating that this type of question is more difficult to answer, regardless of the question format.