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Science strategy interventions

History of strategies and strategic processing in science education

Strategic processing involves the use of cognitive and metacognitive strategies in learning. In science learning, learners use cognitive and metacognitive strategies to understand (a) scientific content in physics, chemistry, biology, Earth science, and other natural science domains; and (b) how scientists construct their understanding within these various domains (e.g., the practices of science or nature of what constitutes valid scientific knowledge). Thus, in science learning strategic processing is related to both the scientific domain or topic (see, for example, Greene et al., 2015) and the scientific tasks of investigating, explaining, and evaluating (see, for example, Dinsmore & Alexander, 2016).

Both scientific topics (e.g., force and motion) and tasks (e.g., conducting an experiment) are found at the task level per Dinsmore’s (2017) Adaptive Model of Strategic Processing. Other levels in Dinsmore’s model also apply to science learning. At the core of the model are strategic processing factors relating to quantity (i.e., how much deep- or surface-level processing), quality (i.e., how well was a strategy executed), and conditional use (i.e., how appropriate was it to use a particular strategy). Strategic processing interacts dynamically with the person-level characteristics, such as an individual’s (a) level of interest in science (or a particular science topic, e.g., interest in astronomy), (b) prior knowledge of scientific content, (c) goal orientation toward learning science (e.g., mastery vs. performance orientation), and (d) epis-temic thinking about scientific content (e.g., evaluations about coherency of scientific explanations). The person level also interacts with the task level (nature of the science learning task and scientific domain), which in turn interacts with the broader learning environment (e.g., the instructional climate that shapes a classroom, such as use of normative behaviors and instructional scaffolds promoting use of particular learning strategies).

For the purposes of this chapter, we view strategies as composed of an array of tactics that fit a broader task category. Thus, strategies are at a larger grain size than tactics (Winne, Jamieson-Noel, & Muis, 2002; Winne & Perry, 2000), with tactics being simple actions taken by a learner based on a specific task feature (e.g., using Newton’s Second Law to calculate an objects acceleration when a physics problem gives the applied force and object’s mass). So, an example of a physics learning strategy that is composed of categorically consistent tactics would be the “working-backward” strategy that learners would use to solve all or most physics problems (Taasoobshirazi & Farley, 2013). Furthermore, we will operate under the well-established and researched position that learners construct their knowledge both cognitively and socially (see, for example, Bransford, Brown, & Cocking, 2000; National Academies of Sciences, Engineering, & Medicine, 2018). This chapter will, therefore, focus on strategy interventions that specifically help promote science learning and/or have been used extensively by educational researchers to understand science learning and by practitioners to facilitate science learning. Certainly, there are generalized strategies that science learners and teachers use, such as those associated with reading comprehension (Dinsmore & Alexander, 2016), motivation (Muis, Ranellucci, Franco, & Crippen, 2013; Taasoobshirazi & Farley, 2013), and self-efficacy beliefs (Kiran & Sungur, 2012). However, other authors discuss these generalized strategies in more detail elsewhere in this volume (see, for example, Dumas, this volume).

Concept Development and Conceptual Change Strategies

Research into science concept development and conceptual change widely emerged in the early 1970s. Piaget’s (1954, 1964) notions of cognitive knowledge construction were foundational to this research vein. In particular, Piaget’s idea that thinking processes (he called them operations) act to form mental structures (i.e., concepts) and constitute the basis of a person’s knowledge was highly influential. Concepts form and change through processes of assimilation (incorporating new information into existing cognitive structures) and accommodation (changing existing cognitive structures in response to new information). Furthermore, Piaget pointed out that the processes of assimilation and accommodation can happen spontaneously, which he termed the development of knowledge, or through provocation, which he termed learning.

Much early conceptual change research was also based on the notion that individuals’ conceptual knowledge formed and changed similarly to how scientists constructed and changed scientific explanations (e.g., explanatory hypotheses and theories). In particular, some conceptual change researchers (see, for example, Posner, Strike, Hewson, & Gertzog, 1982) used T. Kuhn’s (1962) notion of a paradigm shift as an analogy to laypeople’s conceptual change learning. With this view, conceptual change is seen as a relatively radical knowledge construction process, where some kind of cognitive conflict or dissonance provokes the learner to be dissatisfied with their existing conceptual understanding (Posner et al., 1982). These early conceptual change researchers posited that once dissatisfied, a learner could proceed through a rational process of conceptual change if they constructed a replacement conception that was intelligible and understandable, more plausible than the prior conception, and open to the possibility of fruitfully solving future problems and opening new lines of inquiry. Therefore, science learning based on Piagets (1954) notion of accommodation and Posner et al.’s (1982) rational conceptual change model included strategies that moved learners linearly and rationally through steps that addressed these conditions (i.e., individuals progressing through each step—intelligibility, plausibility, and fruitfulness—through reasoned logic based on evidence).

Strategies that focus on only one part of the linear and rational conceptual change process, however, are generally ineffective. For example, strategies that promote contradiction of misconceptions through dissonance and cognitive conflict (e.g., presenting a discrepant event, such as a demonstration that air has mass and exerts a force) have little likelihood of promoting conceptual change because of a variety of psychological responses, such as ignoring or rejecting novel information that is causing cognitive conflict (Chinn & Brewer, 1993). Furthermore, learner characteristics, such as motivation, interest, and epistemological beliefs, may limit engagement when the conceptual change strategy focuses solely on cognitive conflict (Dole & Sinatra, 1998; Limon, 2001). Use of analogies that endeavor to increase the coherence and comprehensibility of the new conception have also shown limited effectiveness. For example, Harrison and Treagust (1993) found that students took transfer analogs from the base to the target too literally (e.g., thinking that atoms are alive and divide like cells).

Given the general ineffectiveness in one single strategy promoting conceptual change, some researchers have called for combination of strategies. Such combined strategies generally promote more gradual and sustained conceptual change and conceptual development. For example, Clement (2008) proposed that learners engage in repeated criticism and revision of explanatory models to engage in all elements of the linear and rational conceptual change sequence. This process, called model evolution, is more gradual and involves a series of iterative dissonance and analogy strategies to help the learner construct a scientific model of a phenomenon over time. Gradual model evolution may involve empirical investigation strategies (e.g., labs) that have the potential to help learners deeply engage in critiquing and revising their explanatory models over time.

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