Exploring the effect of background knowledge and text cohesion on learning from texts in computer science |
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| Authors: | Alexandra Gasparinatou Maria Grigoriadou |
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| Institution: | Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimiopolis, Ilissia, Greece. |
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| Abstract: | 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. |
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| Keywords: | text comprehension text cohesion open-ended questions multiple-choice questions situational understanding |
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