Mapping and Drawing to Improve Students’ and Teachers’ Monitoring and Regulation of Students’ Learning from Text: Current Findings and Future Directions

For (facilitating) effective learning from texts, students and teachers need to accurately monitor students’ comprehension. Monitoring judgments are accurate when they correspond to students’ actual comprehension. Accurate monitoring enables accurate (self-)regulation of the learning process, i.e., making study decisions that are in line with monitoring judgments and/or students’ comprehension. Yet, (self-)monitoring accuracy is often poor as the information or cues used are not always diagnostic (i.e., predictive) for students’ actual comprehension. Having students engage in generative activities making diagnostic cues available improves monitoring and regulation accuracy. In this review, we focus on generative activities in which text is transformed into visual representations using mapping and drawing (i.e., making diagrams, concept maps, or drawings). This has been shown to improve monitoring and regulation accuracy and is suited for studying cue diagnosticity and cue utilization. First, we review and synthesize findings of studies regarding (1) students’ monitoring accuracy, regulation accuracy, learning, cue diagnosticity, and cue utilization; (2) teachers’ monitoring and regulation accuracy and cue utilization; and (3) how mapping and drawing affect using effort as a cue during monitoring and regulation, and how this affects monitoring and regulation accuracy. Then, we show how this research offers unique opportunities for future research on advancing measurements of cue diagnosticity and cue utilization and on how effort is used as a cue during monitoring and regulation. Improving measures of cue diagnosticity and cue utilization can provide us with more insight into how students and teachers monitor and regulate students’ learning, to help design effective interventions to foster these important skills.

Original brands in competition against high quality copycats

Purpose Copycat brands offering improved product quality pose serious challenges to original brands. This paper aims to provide a better understanding of why consumers prefer copycat brands with superior product attributes and how original brands can shift this preference back by strategically leveraging brand identity cues. Design/methodology/approach Four experimental studies test different types of brand identity cues that original brands can use to influence consumer preferences. Logistic and linear regression analyses analyze the effects. Findings The results systematically show the power of brand identity cues in helping original brands reduce share loss to copycat brands using superior product attributes. They also reveal the role of brand equity, conspicuous consumption and consumers’ tendency of using brands as status symbols in enhancing the effect of brand identity cues in the face of superior copycats. Research limitations/implications This paper extends cue diagnosticity theory and the brand identity literature by showing the power of brand identity cues in predicting consumer choices of original brands. Practical implications This paper provides useful guidelines for managers of original brands on how to effectively use brand identity cues to compete against copycats. Originality/value Prior research focuses on how copycat brands’ characteristics influence consumers’ evaluations of copycats. These studies are limited, however, by their focus on cheap and low-quality copycats. The current paper examines the effects of brand identity cues and draws attention to the trade-offs consumers make when choosing between original brands and copycats offering superior product features.

Computational Models as a Knowledge Management Tool: A Process Model of the Critical Judgments Made during Audit Planning

Effective management of knowledge is essential for a CPA firm to remain competitive. Use of computational models of judgment processes and outcomes causes knowledge to be available for use and analysis. We present a comprehensive and integrated computational model of the difficult and knowledge-intensive judgments needed for successful audit planning. The model concludes on a client's going-concern status, applicable levels of inherent, control, and planned detection risk, and appropriate levels of statement- and account-level materiality. Most importantly, the model validly identifies the cause of significant fluctuations given causal hypotheses. The context is the sales and collection cycle of a manufacturing client. The model consistently replicates causal hypothesis judgments generated by the modeled auditor who exhibits considerable judgment expertise, i.e., his judgments typically coincide with actual causes. Concerning judgment expertise, the model reveals numerous linkages among judgments, subtle interdependencies in cue importance across judgments, and new findings concerning cue diagnosticity.