Profiles of warm engagement and cold evaluation in multiple-document comprehension

Abstract

We explored potential profiles of interest, attitudes, and source evaluation by performing cluster analysis in a sample of Norwegian upper-secondary students. Differences among the profile groups with regard to multiple-document use were examined. The profile groups were partly consistent with the default stances described by the cognitive-affective engagement model of multiple-source use (List & Alexander, 2017), resulting in critical analytic, evaluative, and disengaged profiles. However, the model's assumption that interest and attitude constitute one affective engagement dimension was not confirmed. There were no statistically significant differences between the profile groups in the processing of a set of multiple documents; yet there was a tendency for students who adopted a critical analytic stance to engage in a more thorough text selection process. Those students also included more information units from the selected texts in their written products and integrated information units across the texts more frequently compared to the other profile groups.

Predictors and outcomes of behavioral engagement in the context of text comprehension: when quantity means quality

“…Also as expected, the path analysis indicated that both cognitive and motivational constructs may underlie both behavioral engagement components, with prior knowledge, working memory, and intrinsic motivation uniquely predicting response length, and with two of these constructs (working memory and intrinsic motivation) uniquely predicting writing time. While the finding that behavioral engagement needs to be underpinned by both cognitive and motivational individual differences is consistent with prior empirical work (Bråten et al, 2014;Kammerer et al, 2021;List, 2020;List et al, 2019), our findings provide new insights by showing how prior knowledge, working memory, and intrinsic motivation may uniquely predict specific aspects of behavioral engagement in the context of written comprehension assessment. In regard to this, the finding that prior knowledge was more strongly related to response length than to writing time may indicate that students with higher levels of prior knowledge can produce relevant text more efficiently, without necessarily spending much time on the writing task.…”

“…Likewise, Bråten et al (2018), using hierarchical multiple regression analysis in a study of upper-secondary students, found that both the time used for reading the texts and the time used for completing the post-reading writing task used to measure comprehension contributed to performance over and above reading comprehension skills, topic knowledge, and topic interest. These findings were corroborated by List et al (2019), who also used hierarchical multiple regression analysis to demonstrate that behavioral engagement in terms of the time undergraduate students devoted to text use uniquely predicted the quality of their post-reading written products. Moreover, List (2020), who performed structural equation modeling with undergraduate students, found that time on texts directly affected memory for textual information and integrated understanding when cognitive and motivational differences as well as deeper level strategy use were controlled for.…”

“…For example, Bråten et al (2014) found that both prior topic knowledge and interest in the topic were positively related to students' time use, with time use also mediating the effect of interest on their comprehension performance. Likewise, List (2020) found that behavioral engagement as indicated by time on task was affected by topic interest and thus mediated the effects of topic interest on comprehension performance, and List et al (2019) found that the time students devoted to text use mediated the effects of situational (i.e., text-based) interest on the written comprehension products. Moreover, List (2020) found that a cognitive factor based on students' scores on a general, decontextualized measure of perceived strategy use predicted the time devoted to text use and, in turn, their comprehension performance.…”

“…Both these studies used a multidimensional measure of student engagement based on teacher ratings. Other work has linked both prior topic knowledge and interest, which is a construct closely related to intrinsic motivation (Schiefele et al, 2012), to comprehension performance via behavioral engagement, in particular (e.g., Bråten et al, 2014;Kammerer et al, 2021;List, 2020;List et al, 2019). For example, Bråten et al (2014) found that both prior topic knowledge and interest in the topic were positively related to students' time use, with time use also mediating the effect of interest on their comprehension performance.…”

“…Regarding direct effects of behavioral engagement on comprehension performance, several researchers have found that the time students invest in reading and comprehension tasks uniquely predicts their comprehension performance (Bråten et al, 2014(Bråten et al, , 2018Du & List, 2020;List, 2020;List et al, 2019). For example, Bråten et al (2014), in a path analytic study with upper-secondary students, found that reading time predicted comprehension performance even when several individual differences (e.g., prior topic knowledge) and the use of deeper level comprehension strategies were controlled for.…”