Measuring strategy use in context with multiple-choice items

Abstract

A number of authors have presented data that challenge the validity of self-report of strategy use or choice of strategy. We created a multiple-choice measure of students' strategy use based on the work of Kozminsky, E., and Kozminsky, L. (2001), and tested it with three samples as part of a series of studies testing the fit of the DIME model of reading comprehension. One study was conducted at the high school level (N=175) and two at the undergraduate level (N=185 and 737). Over the three studies with three different samples, we found good evidence for the internal consistency reliability and concurrent validity of this type of measure. Commonality analysis suggested that strategy use mainly makes a shared contribution to comprehension with other predictors, especially inference, and to some extent vocabulary, background knowledge, and word reading. The measure was relatively easy to construct and easy to administer to large numbers of students, and showed much higher evidence of concurrent validity than self-ratings of frequency of use of strategies. Measuring strategy use in context with multiple-choice itemsAs noted by other authors in this special issue, skilled comprehenders use a multitude of cognitive and metacognitive strategies while reading. For example, they may set goals before reading, ask themselves questions while reading, summarize, and monitor their level of understanding as they read. Alexander and Judy (1988) define strategies as "goaldirected procedures that are planfully or intentionally evoked . . . . [that] aid in the regulation, execution, or evaluation of [a] task" (p. 376). The effect of reading strategies on comprehension has been a major research program since the 1980s. Two main types of evidence are brought to bear to support the role of strategies: descriptive (questionnaire, observation, and think-aloud studies) and experimental studies in which students are taught how to enact specific strategies.Briefly, reading strategy use is associated with better reading comprehension in

The Development of Scientific Strategy Knowledge Across Grades

“…Within the regression models, content knowledge significantly predicted strategy knowledge across grade levels. This finding supports the results of a study conducted by Cromley and Azevedo (2011), who found that the application of domain-specific strategies is determined by students' background knowledge. Also, the findings of H. Kim and Pedersen (2011) can be supported.…”

Metacognitive Knowledge and Metacognitive Regulation in Self-Regulatory Learning Style, and in Its Effects on Performance Expectation and Subsequent Performance across Diverse School Subjects

“…As Efklides (2014) proposes, it is important to examine the various cognitive, affective and motivational strategies that students apply for regulating their learning in various learning domains. For example, students' knowledge, perceptions of the task-difficulty, perceptions of self-efficacy might influence performance expectations and performance, as literature suggests (e.g., Benbenutty et al, 2014;Cera et al, 2013;Cromley & Azevedo, 2011;Schunk & Zimmerman, 2006;Smit et al, 2017;Stephanou, 2008). The Jr. MAI might be another influential factor of these findings, as the extent of correlations between metacognition and academic indicators depends considerably on how metacognition is assessed, with favoring the content-task specific method (Tobias & Everson, 2009;Young & Fry, 2008).…”

“…Most, of the previous researches examining the inter-correlations or inter-effects of the above concepts have focused on mathematics or/and language, while other school subjects have almost ignored, although features of the task affect learners' beliefs and, subsequent application, about skills and strategies are required, and competence and control beliefs (see Cromley & Azevedo, 2011;Efklides, 2014;Kurman, 2001;Ommundsen, Haugen, & Lund, 2005;Pressley & Gaskins, 2006;Schunk & Zimmerman, 2006;Wang & Holcombe, 2010). This study focuses on language, mathematics and physical education.…”

Towards efficient measurement of metacognition in mathematical problem solving

“…Secondly, students can differ in their frame of reference as to which situations they have in mind when answering the questions and interpreting the scales (McNamara 2011;Schellings 2011). Thirdly, the way students answer self-report questionnaires may be biased by triggers in the questions which prompt them to wrongly label their own behavior or by social desirability (Cromley and Azevedo 2011;Veenman 2011a). Therefore, students are typically quite inaccurate in reporting their own metacognitive behavior.…”