{"title":"Comparative Analysis of Inferencing in Low-Reading and Average-Reading Comprehenders: Utilizing the Think-Aloud Protocol","authors":"Woori Kim, Mikyung Shin, Yongseok Yoo","doi":"10.12963/csd.23976","DOIUrl":null,"url":null,"abstract":"Objectives: This is a conceptual replication aiming to investigate the cognitive processes of students with reading comprehension difficulties using the think-aloud protocol. Methods: Among 72 third- and fourth-grade participants, 28 poor comprehenders and 44 average students were identified based on screening criteria and standardized tests. The think-aloud protocol was used to monitor comprehension processes during reading. The participants verbalized their thoughts as they read expository and narrative texts. Those responses were transcribed and coded according to inference rates, correctness, and inferential types (explanation, prediction, or association). Results: First, poor comprehenders showed significantly lower rates and accuracies of inferences for both expository and narrative texts than average students did. Second, there were significant differences between poor comprehenders and average students in the proportions of the three types of inferences. Poor comprehenders generated significantly lower rates of explanatory, predictive, and associative inferences. Third, the inference types differed for different type of texts. Both groups made more predictive inferences when reading the narrative text than when reading the expository text. Conclusion: Differences in inference patterns between poor comprehenders and average students were identified using the think-aloud protocol. Different types of inferences were involved in processing different types of texts. Future research directions for developing learning strategies for encouraging solid inference are discussed.","PeriodicalId":45124,"journal":{"name":"Communication Sciences and Disorders-CSD","volume":"38 1","pages":"0"},"PeriodicalIF":0.4000,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communication Sciences and Disorders-CSD","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12963/csd.23976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: This is a conceptual replication aiming to investigate the cognitive processes of students with reading comprehension difficulties using the think-aloud protocol. Methods: Among 72 third- and fourth-grade participants, 28 poor comprehenders and 44 average students were identified based on screening criteria and standardized tests. The think-aloud protocol was used to monitor comprehension processes during reading. The participants verbalized their thoughts as they read expository and narrative texts. Those responses were transcribed and coded according to inference rates, correctness, and inferential types (explanation, prediction, or association). Results: First, poor comprehenders showed significantly lower rates and accuracies of inferences for both expository and narrative texts than average students did. Second, there were significant differences between poor comprehenders and average students in the proportions of the three types of inferences. Poor comprehenders generated significantly lower rates of explanatory, predictive, and associative inferences. Third, the inference types differed for different type of texts. Both groups made more predictive inferences when reading the narrative text than when reading the expository text. Conclusion: Differences in inference patterns between poor comprehenders and average students were identified using the think-aloud protocol. Different types of inferences were involved in processing different types of texts. Future research directions for developing learning strategies for encouraging solid inference are discussed.