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А lifespan developmental perspective on strategic processing

Strategic processing: from a conditioned state to a growth perspective

From the time humans first appeared on earth, we have faced a desire to understand our world and learn what we need to know to survive (Weinstein, Jung, & Acee, 2011). However, it is only from the 1970s onwards that the psychological study of strategies began in earnest. By then, educational research and practice was dominated by the behavioristic theory on learning and instruction, which considered learning merely as a result of certain environmental contingencies (i.e., trial and error, rewards and punishments). Accordingly, the research at that time demonstrated that learners’ knowledge could significantly be modified through training (Bryant, Vincent, Shaqlaih, & Moss, 2013). Behavioristic studies, in this respect, targeted observable learning behavior and took a main interest in instructional techniques that contributed to better performance (Bryant et al., 2013). In essence, behaviorists perceived learning as a conditioned state, failing to acknowledge the potential of a growth or developmental orientation on learning.

From the late 1990s onwards, researchers gradually started to recognize learning as a lifelong process, thereby emphasizing the changing nature of individuals’ learning behavior with increasing expertise (Alexander, 2003). The work of Alexander and colleagues (1998, 2004) in particular has contributed to our knowledge on the development of learning. As a result, current research on strategic processing (i.e., processing information strategically) continues to extend its focus beyond the initial phases of learning during childhood. From a developmental perspective, individualsare perceived as continuously evolving in the process of learning. Consequently, learning is no longer merely related to young learners, but is rather associated with learners of all ages, including adolescents and adults. In the current 21st century and knowledge society, where lifelong learning is pivotal for active societal participation (Cornford, 2002), this developmental focus started to thrive and found its way into the educational research community. From this perspective, the focus increasingly lies on the complex evolution in strategic processing across the lifespan. This expanded view on learning becomes evident in, for instance, the more recent and increased attention for adolescent and adult learning and education (Alexander & Fox, 2004).

The purpose of this chapter is to elaborate further on this developmental orientation by presenting a framework for strategic processing that encompasses changes in learners’ strategy use across the lifespan. A first prerequisite, in this respect, concerns deconstructing how the concept ‘strategy’ is defined and described in the literature. Surprisingly, although it has been widely used in cognitive research since the 1970s, attempts to unravel the concept ‘strategy’ mainly stem from the 1990s (e.g., Alexander, Schallert, & Hare, 1991; Dole, Duffy, Roehler, & Pearson, 1991). Even in the current empirical literature, it remains muddled and vague what a strategy precisely entails (Alexander, 2006; Harris, Alexander, & Graham, 2008). This lack of conceptual clarity can be considered as a substantial roadblock for strategy research, a concern that was already strongly expressed in the late 1980s by Alexander and Judy (1988). Therefore, we begin this particular chapter by providing an operational definition of what we constitute as a strategy.

As Harris and colleagues (2008) justly state, the history of strategies in the educational research literature since the mid-1990s has been a story of conceptualization and reconceptualization. Accordingly, differences in the categorization of strategies came to the fore (Harris et al., 2008). Building on the conceptualization of strategies presented by Weinstein and Mayer (1986), we define strategies as mental activities selected by learners to acquire, organize, and elaborate information, as well as to reflect upon and to guide their learning. Specifically, strategies should be understood as procedural, purposeful, effortful, willful, essential, and facilitative by nature (Alexander, Graham, & Harris, 1998). This implies that strategies should be interpreted as procedures or techniques that are employed by learners to bridge the gap between their actual and their potential or desired level of learning, understanding, and performance (Alexander, Grossnickle, Dumas, & Hattan, 2018; Pressley, Graham, & Harris, 2006; Weinstein & Mayer, 1986). Consequently, and taking into account the scope of the present chapter, any consideration of instructional or pedagogical strategies is precluded in this conceptualization (Alexander et al., 2018). While fully acknowledging the importance and added value of instructional strategies for facilitating students’ learning process and performance (Alexander et al., 1998), our focus in the remainder of this chapter will be on learners and their applied strategies.

Next to various conceptualizations of the term ‘strategies’, differences in their categorization also occur in the literature. For instance, they have been distinguished according to their nature, perceptibility, level of depth, and domain of application. First, regarding their nature, strategies can be categorized as either cognitive

(e.g., paraphrasing), metacognitive (e.g., planning), or motivational-affective (e.g., using positive self-talk; Pintrich, 2004). Second, some strategies can be applied overtly and are consequently easily observable (e.g., schematizing), whereas others take the form of covert mental strategies (e.g., monitoring; Wade, Trathen, & Schraw, 1990). Third, a distinction can be made between deep-level strategies, aimed at profound understanding and active transformation of information (e.g., elaborating), and more surface-level strategies that merely aim at basic comprehension without integrating information (e.g., applying read-and-repeat techniques; Dinsmore & Alexander, 2012; see also Chapter 3). Finally, we discern general or domain-independent strategies that are applicable in a wide variety of learning contexts (e.g., planning) from domain-specific strategies (see also Chapter 2) whose range of applicability is restricted to a particular learning domain (e.g., using problem solving steps for mathematics; Alexander et al., 1998; Weinstein et al., 2011). Taking into account these different conceptualizations, the same strategy can be placed within different categorizations. For example, the read-and-repeat strategy is cognitive, overt, surface-level, as well as a domain-in-dependent strategy.

Notwithstanding the value of each separate strategy, it is particularly the ability to flexibly and selectively use a variety of apt strategies that has been shown to be crucial for learning, understanding, and performance across the lifelong journey toward proficiency (Alexander, 2018; Pressley & Harris, 2006). Indeed, effective learning, understanding, and performance requires the orchestration of strategies from different categorizations (Alexander, 2018). Consequently, in view of handling and solving a variety of tasks and problems, having access to a strategic repertoire and being able to efficiently make use of it, is indispensable. Different theoretical learning strategy models developed within the 2010s point attention to this strategic repertoire. Four overarching theoretical models are especially relevant here, that is the Good Strategy User Model (Pressley, Borkowski, & Schneider, 1987), the Model of Strategic Learning (Weinstein et al., 2011), the Overlapping Waves Model (Siegler, 1996), and the Model of Domain Learning (Alexander, 1998).

Pressley and colleagues (1987) focus in their Good Strategy User (GSU) Model on five identified components in good strategy users. According to this model, the good strategy user (1) can exert many strategies to attain goals, (2) has metacognitive knowledge about specific strategies, that is knowing how, when, and where to apply these strategies, (3) understands that good performance is tied to personal effort expended in carrying out appropriate strategies, (4) possesses a non-strategic knowledge base (e.g., the existence of categorizations), and (5) has automatized the first four components and their coordination (Pressley et al., 1987). According to the GSU model, novice learners possess very limited strategy knowledge and tendencies, whereas more proficient learners thoroughly understand and apply a wide range of strategies.

Weinstein and colleagues’ (2011) Model of Strategic Learning (MSL) summarizes three interacting components of strategic learning that are connected causally with performance (i.e., skill, will, and self-regulation). Skill refers to the knowledge of a variety of strategies, and how, when, and where to apply them (i.e., respectively declarative, procedural, and conditional knowledge). Will refers to the motivational-affective component within strategic learning, referring to learners’ attitudes, beliefs, and goals that drive their learning. Self-regulation, the third component according to the MSL, enables learners to monitor and manage their learning process.

Siegler’s (1996) Overlapping Waves Model (OWM) is predicated on three assumptions: (1) learners employ a variety of strategies when solving a problem, (2) this variety of strategies coexists not only during brief transition periods but also over prolonged periods of time, and (3) gradual changes in the frequency of these applied strategies and more advanced applications of these strategies manifest through experience. Further, this model postulates that the typical pattern of strategy development includes five overlapping dimensions of learning: (1) acquiring the strategy of interest, (2) transferring the strategy to new, unfamiliar problems, (3) strengthening the strategy to assure consistent use across a given type of problem, (4) refining choices among alternative strategies or alternative forms of a single strategy, and (5) executing the strategy of interest increasingly effectively (Chen & Siegler, 2000; Siegler, 1996, 2000).

Finally, the Model of Domain Learning (MDL) was described by Alexander (1998) and approaches strategic processing through a developmental lens. In particular, the MDL describes strategic processing through three different stages, that is how learners progress from acclimation through competence to proficiency-expertise. Knowledge, strategies, and interest are identified as three interplaying factors, configuring differently during progression through these stages (Alexander, 1998, 2003). As learners progress from one stage to another, their strategy knowledge increases, and their strategy repertoire extends.

Despite their slightly different focus, the GSU model, MSL, OWM, and MDL show important parallels. On the one hand, all theoretical models point to the importance of having a diverse amount of strategies available (i.e., quantitative dimension). On the other, they also emphasize the efficient and adaptive use of this strategy repertoire (i.e., qualitative dimension). In this respect, all models entail a quantitative and qualitative dimension wherein improvements in strategy use can take place. Furthermore, all models highlight the key role of motivational-affective aspects in strategic processing by considering learners’ personal effort (i.e., GSU), will to learn (i.e., MSL), and interest (i.e., MDL and OWM). Next to the abovementioned parallels between the theoretical models, the MDL explicitly distinguishes itself by the predominant focus on strategic processing from a developmental perspective. Before presenting an overarching framework on strategic processing, we therefore take a closer look at this developmental view in the next section.

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