This article explores learner-generated drawing, a strategy in which learners construct representative illustrations in support of learning goals. Both applied and empirical literature is reviewed with the purpose of stimulating research on this strategy. Clear from this review is the gap that exists between prescriptive readings on learner-generated drawing and research-based understandings. To make sense of inconsistent empirical evidence, the research review is organized around a series of hypotheses grounded in current underA hallmark of sophisticated, expert-like performance is the ability to think flexibly and to transfer knowledge across contexts. In part, this process is reliant on an underlying cognitive structure in which knowledge is integrated across varying representations (de Jong and Ferguson-Hessler, 1986;Silver, 1979) 286Van Meter and Garner (Van Someren et al., 1998). When a student can translate a data table to a linear function (Haverty et al., 2000) or use illustrated text to solve transfer problems (Mayer and Sims, 1994), that student is assessed as functioning at a higher level relative to a student who struggles with these activities. As critical as integration is, learners have a difficult time working with more than one format and integrating verbal and nonverbal representations of complex content (e.g., Scanlon, 1998;Tabachneck-Schiif and Simon, 1998). Given both the value and challenge of this process, strategies that facilitate the integration of different representations, particularly those that cross modalities, have great potential for improving student learning. Learner-generated drawing is one such strategy because drawing involves the construction of an internal, nonverbal representation that is mapped onto elements of the alternative, provided representation. The purpose of this article is to make the case that learner-generated drawing is a strategy that warrants thorough, systematic study.Although learner-generated drawing received some attention in the mid to late 1970s, research interest dried up by the mid 1980s. We believe the loss of interest is partially attributable to inconsistent findings and a body of research which, on balance, is rather disappointing. Along with the obvious value briefly stated above, there are two additional reasons for renewing interest in learner-generated drawing. First, an abundance of prescriptive publications available to classroom teachers tout learnergenerated drawing as a strategy that can meet a number of educational objectives. This is true despite a lack of evidence to support most applications. It is not that these prescriptions are necessarily wrong; rather, the research evidence addressing drawing as a learning process is inconsistent, silent, or qualifying. To highlight the distance between research and practice, the review section of this article begins with an overview of the applied literature before synthesizing the empirical research. We hope the juxtaposition of an array of implementations against scant research evidence ...
Second-grade, low-achieving students experienced a year of either transactional strategies instruction or highly regarded, more conventional second-grade reading instruction. By the end of the academic year, there was clear evidence of greater strategy awareness and strategy use, greater acquisition of information from material read in reading group, and superior performance on standardized reading tests by the transactional strategies instruction students. This is the clearest validation to date of educator-developed transactional strategies instruction.
Classroom use of student-generated drawings has been encouraged for a number of purposes (e.g., R. Hubbard & K. Ernst, 1996). The present study examined the use of drawing as a learning strategy for 5thand 6th-grade students reading science text. Three experimental drawing conditions and a reading control tested the hypothesis that drawing is effective only when students are supported during the construction process. Drawing (draw) participants constructed drawings only, whereas illustration comparison participants compared drawings with a provided illustration. Prompted illustration comparison (PIC) participants answered prompting questions to guide this comparison process. Dependent measures included a free-recall and recognition posttest, drawing accuracy, on-line self-monitoring behaviors, and time on task. PIC participants constructed the most accurate drawings and also scored significantly higher on the free-recall posttest. No differences were found on recognition posttest items. Although all drawing conditions spent significantly more time on task, these participants also engaged in significantly more self-monitoring events than did reading control participants. PIC participants also engaged in more events than did draw participants. Revision
Background Even as expectations for engineers continue to evolve to meet global challenges, analytical problem solving remains a central skill. Thus, improving students' analytical problem solving skills remains an important goal in engineering education. This study involves observation of students as they execute the initial steps of an engineering problem solving process in statics. Purpose (Hypothesis) (1) What knowledge elements do statics students have the greatest difficulty applying during problem solving? (2) Are there differences in the knowledge elements that are accurately applied by strong and weak statics students? (3) Are there differences in the cognitive and metacognitive strategies used by strong and weak statics students during analysis? Design/Method These questions were addressed using think‐aloud sessions during which students solved typical textbook problems. We selected the work of twelve students for detailed analysis, six weak and six strong problem solvers, using an extreme groups split based on scores on the think‐aloud problems and a course exam score. The think‐aloud data from the two sets of students were analyzed to identify common technical errors and also major differences in the problem solving processes. Conclusions We found that the weak, and most of the strong problem solvers relied heavily on memory to decide what reactions were present at a given connection, and few of the students could reason physically about what reactions should be present. Furthermore, the cognitive analysis of the students' problems solving processes revealed substantial differences in the use of self‐explanation by weak and strong students.
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