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When are strategies deployed during multiple text use?

While prior work has provisionally considered where students direct strategies during multiple text use, when during text use strategies may be deployed has received considerably more limited attention in the literature. Nevertheless, some studies indicate that when during text use students deploy particular strategies matters for their efficacy. For instance, Wineburg (1991), in describing experts’ use of the sourcing strategy, specifically notes that experts looked to document information first, prior to reading, with sourcing serving as the interpretive lens for the textual information to follow. Emphasizing sourcing prior to reading a text seems in line with research identifying the activation of prior knowledge as a central strategy to support reading comprehension, more generally (Alvermann & Hynd, 1989; Spires & Donley, 1998; Wetzels, Kester, & Van Merrienboer, 2011). In addition to certain strategies needing to be activated prior to multiple text use, other strategies may be more effective when deployed during or after engagement with multiple texts. For instance, across two experiments, Britt and Sommer (2004) examined the effectiveness of intervening tasks on students’ integration of multiple texts. They found that composing brief summaries after reading a first text (Experiment 1) improved its integration with a second text, as did answering questions related to text macro-structure (i.e., questions about why events occurred), rather than micro-structure (i.e., questions asking where and when events occurred; Experiment 2).

Within the context of learning from multiple, multimedia documents, Lee and List (2019) compared students’ strategic processing when learning information that was presented either as two texts or as two videos. An interesting finding to emerge from this work is that, in addition to some similar and some different strategies engaged during reading vis-à-vis video viewing, video viewing resulted in strategic front-loading in ways that reading did not. That is to say, when viewing videos students engaged disproportionately more strategies during the first quintile of viewing, with strategic engagement during reading found to be more proportionally distributed in nature. While Lee and List attribute these distributional differences in strategy use to the linear nature of videos, rendering non-sequential navigation difficult, these findings point to the need to consider the when of strategy deployment both when students read texts and when they seek to process other media, including videos. A further consideration in determining the when of strategy use may be not only the point of strategy initiation but also its duration.

Who benefits from strategy use during multiple text use?

A wide variety of individual difference factors have been examined within the context of learning from multiple texts. These include cognitive factors, like epistemic beliefs and need for cognition (e.g., Braten, Stromso, & Samuelstuen, 2008; Ferguson & Braten, 2013), as well as affective factors, like interest and attitudes (e.g., Braten & Stromso, 2006; McCrudden & McTigue, 2018), and, more recently, psychophysiological factors like heart rate variability (Mason, Scrimin, Zaccoletti, Tornatora, & Goetz, 2018; see Brante & Stromso, 2018 for a review; List & Alexander, 2017a, 2018b for a central framework).

In one research tradition, individual differences on various learner characteristics have been associated with the differential ability to take advantage of the various affordances or features of multiple text environments. For instance, Gil, Braten, Vidal-Abarca, and Stromso (2010), while not examining strategy use per se, did find that students with high prior knowledge were able to take advantage of a task assignment asking them to compose an argument, rather than a summary, while students low in prior knowledge were not able to do so. Le Bigot and Rouet (2007) similarly distinguished the performance of students comparatively low and high in prior knowledge, assigned to write either arguments or summaries within hypertext environments that varied according to their format of text presentation (i.e., by topic or by author and topic). Results included that students high in prior knowledge performed better on comprehension questions than their low knowledge counterparts and that argument tasks resulted in students composing more sophisticated written responses than did summary tasks. However, interactions among prior knowledge and hypertext affordances were limited.

A second research approach has examined how individual difference factors may function more directly in interaction with students’ strategy use during learning from multiple texts. For instance, Braten, Stromso, Brandmo, and Anmarkrud (2014) found prior knowledge, epistemic beliefs, and need for cognition to be associated with students’ deeper level strategy use (i.e., engagement in cross-textual elaboration), resulting in improved multiple text comprehension. List, Stephens, and Alexander (2019) found the relation between situational interest and students’ multiple text task performance to be mediated by the time that students devoted to text access, considered to be a proxy for persistence and the more effortful processing of multiple texts. Beyond these investigations, few studies have examined how strategies might interact with individual difference factors to result in variations in performance. In particular, there has been limited consideration of the extent to which different strategies may be beneficial for learners differing in prior knowledge, multiple text use skills, or motivation.

Within the context of multimedia learning, a wider range of individual difference factors may need to be considered (Moreno, 2002). These include verbal and visuospatial working memory (Gyselinck, Jamet, & Dubois, 2008; Schuler, Scheiter, & van Genuchten, 2011 for a review), visuospatial reasoning (Hegarty & Waller, 2005; Hoffler, 2010; Wu & Shah, 2004), and multimedia/graphic comprehension knowledge and skills (Canham & Hegarty, 2010; Cromley et al., 2013; delMas, Garfield, & Ooms, 2005). Indeed, these individual difference factors in conjunction with affordances in multimedia learning environments have been found to result in differences in performance. For instance, Brucker, Scheiter, and Gerjets (2014) examined how students with high versus low visuospatial reasoning learned from graphics that varied in realism (i.e., realistic versus schematic) and dynamism (i.e., dynamic versus static). Learners with high visuospatial abilities were found to perform better when presented with realistic visualizations, while learners low in visuospatial ability were found to benefit from more schematic graphics. Cook, Wiebe, and Carter (2008) compared the diagrammatic reasoning of students low and high in prior knowledge, learning about diffusion and osmosis. While low knowledge students tended to focus on the surface features of diagrams (e.g., noticing differences in color and shape), high knowledge students were better able to interpret these features, forming inferences based on prior knowledge. Moreover, when asked how diagrams can be improved, students low in prior knowledge expressed a desire for more labeling and verbal explanation, while students high in prior knowledge were satisfied with the more parsimonious diagrams provided. These findings suggested that differences in prior knowledge result not only in attentional differences during processing but in strategic differences as well, with prior knowledge supporting greater inferencing during diagrammatic learning. These results further echo findings from expert-novice studies which have found novice learners to treat diagrams more discretely, grouping them based on surface features, while experts reason more holistically and, crucially, are able to reason across multiple diagrams to develop understanding (Chi, Feltovich, & Glaser, 1981; Kozma, 2003). The next step within this body of work, then, is to further consider how learners’ individual differences alongside design features in various multimedia environments might jointly result in differences in strategic processing and ultimately, in improvements in task performance.

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