Large-scale longitudinal studies spanning the past 50 years provide convincing evidence that spatial ability in adolescence predicts later science, technology, engineering, and mathematics (STEM) achievement (Lubinski & Benbow, 2006; Wai, Lubinski, & Benbow, 2009). In addition to often cited examples of scientific discoveries resulting from creative spatial thought, a growing body of research with adults and adolescents highlights a more specific link between spatial ability and various aspects of science learning (e.g., Kozhevnikov & Thornton, 2006). However, in contrast to the spatial ability and mathematics literature (e.g., Mix et al., 2016), the relationship between spatial ability and science learning in younger children has been largely neglected.
A deeper understanding of this relationship at an earlier stage of development is important because it has implications for early curriculum design, informs the development of spatial training interventions, and has the potential to support learners when they are at more advanced stages of science education. The focus of this study was therefore on the relationship between different aspects of spatial ability and scientific achievement in primary-school children. Below, we present a summary of current knowledge of spatial ability and science learning before discussing the relationship between these two domains.
Overview of spatial ability
Spatial ability, which relates to ‘the location of objects, their shapes, their relation to each other, and the paths they take as they move’ (Newcombe, 2010, p. 30), has long been recognized as an ability partly independent of general intelligence, reasoning, and verbal ability (Hegarty, 2014; Rimfeld et al., 2017). As well as being distinct from other cognitive abilities, spatial thought itself has often been conceptualized in a multidimensional fashion: as consisting of several separate but correlated skills.
Two broad categories of multidimensional models have emerged: ones based in the psychometric tradition (Carroll, 1993; Lohman, 1988) and other more theoretically driven models (e.g., Uttal et al., 2013). This study adopts a theoretical model, proposed by Uttal and colleagues (Newcombe & Shipley, 2015; Uttal et al., 2013), based on top-down understanding of spatial skills, drawing upon developments in cognitive neuroscience. The model primarily distinguishes between intrinsic and extrinsic spatial abilities, mapping onto a within-object and between-object classification, respectively. Intrinsic/extrinsic skills are further categorized as either static or dynamic abilities; dynamic abilities include transformation or movement.
Intrinsic–static skills involve the processing of objects or shapes, or parts of objects or shapes, without further transformation. Tasks that measure this skill often require this processing to occur amidst distracting background information. For example, in disembedding tasks, participants search for a specified 2D shape in a larger distracting image. Intrinsic–dynamic skills, in contrast, involve the processing and manipulation or transformation of objects or shapes. Mental folding and mental rotation fit into this category. Extrinsic–static skills require the processing and encoding of the spatial relations between objects, without further transformation of these relations. The extrinsic–static category includes spatial alignment or reasoning about spatial correspondence, an example of which is the ability to find corresponding locations between shapes of equal proportion but differing sizes (scaling and map use). Extrinsic–dynamic skills involve the transformation of the relationship between objects, or the relationship between objects and frames of reference. Spatial perspective taking, in which a participant visualizes a change in their relationship to an object and is asked what an object or objects would look like from a different viewpoint, is an extrinsic–dynamic skill.
The model is supported by research indicating that object-based spatial ability (intrinsic) is partially dissociated from environmental (extrinsic) spatial ability (Hegarty, Montello, Richardson, Ishikawa, & Lovelace, 2006). The intrinsic–extrinsic dimension is also supported by the finding that mental rotation (intrinsic–dynamic) and perspective taking (extrinsic–dynamic) are associated with different patterns of brain activation (Zacks, Vettel, & Michelon, 2003) and are also psychometrically distinct (Hegarty & Waller, 2004).
The goal of science is to extend our knowledge of the world. ‘Science’ therefore refers to both the existing body of knowledge that we have about the world and the activities and processes by which this knowledge comes about (Zimmerman, 2000). Engaging in science in part involves understanding and applying factual knowledge and conceptual understanding of the theories that exist about the phenomena around us. Scientific knowledge is commonly organized by discipline, for example, physics, and specific subtopics within these domains, such as the subtopic of electricity. In addition to this, science involves specific reasoning, strategies, and investigation skills which are directed towards discovery and changes to the theories we have about the world (Zimmerman, 2000). The ability to form and evaluate scientific hypotheses is one example of an important scientific reasoning skill.
In this study, a curriculum-based approach to science assessment was adopted. The UK science curriculum includes the previously outlined aspects of factual knowledge, conceptual understanding, and scientific investigation (Department for Education, 2013). It also emphasizes that ’working scientifically … must always be taught through and clearly related to substantive science content in the programme of study’ (Department for Education, 2013, p. 5.). Science achievement was therefore assessed using a composite assessment of factual knowledge, conceptual understanding, and investigation skills taught in the age range of interest. A curriculum-based approach has the advantage that it covers the breadth of knowledge and skills children learn in the classroom. Such an approach has also been successfully adopted in the past, for example, in studies investigating the role of executive functions on children's performance in standardized science assessments (Jarvis & Gathercole, 2003; St Clair-Thompson & Gathercole, 2006).
Spatial skills and science
Spatial skills may particularly support learning, problem-solving, and reasoning within conceptual science areas that have a clear spatial–relational basis (e.g., astronomy and mechanics). Table 1 provides other hypothetical examples of how the different spatial skills as outlined by Uttal et al. (2013) might be applied to different scientific activities (Rule, 2016).
Table 1. Examples of Uttal et al.'s (2013) spatial skill categories in relation to scientific activities, Rule (2016)
|Intrinsic–static||Processing of objects/shapes without transformation||Geology||Identifying rocks and rock formations by colour, texture, grain size, and visual patterns|
|Intrinsic–dynamic||Processing and manipulation or transformation of objects/shapes||Chemistry||Checking the symmetry of atoms in a crystal structure by imagining them moving across mirror planes or rotating around an axis|
|Extrinsic–static||Encoding of the spatial relations between objects without transformation||Chemistry||Comparing the crystal structures of a compound with and without a substituted element|
|Extrinsic–dynamic||Transformation or updating of the relationship between objects||Astronomy||Locating a near-earth asteroid's path through time and its distances from the earth as both move along different paths|
Most prior research with adults points to spatial visualization skills as being related to science learning. Spatial visualization involves mentally transforming object-based spatial information and is assessed through intrinsic–dynamic spatial skills such as mental rotation. Existing research with adults suggests a link between intrinsic–dynamic spatial skills and conceptual understanding in aspects of biology (Garg, Norman, Spero, & Maheshwari, 1999), chemistry (Stull, Hegarty, Dixon, & Stieff, 2012), and physics (Kozhevnikov & Thornton, 2006). For example, in Stull et al. (2012) spatial ability, as measured through 3D object visualization, correlated with undergraduate students’ ability to translate between different diagrammatic representations of chemical structures. There is also some evidence linking adults’ chemistry performance to disembedding (intrinsic–static) spatial skills (Bodner & McMillen, 1986) and undergraduate's geoscience understanding to multiple-object (extrinsic–dynamic) spatial skills (Sanchez & Wiley, 2014). However, no research to-date has addressed other skills, such as extrinsic–static scaling ability, in relation to science learning.
Spatial skills and science in children
Research relating spatial ability and science learning in younger children is sparse, and some studies that have addressed this have done so only in relation to visual–spatial working memory (VSWM) or a limited range of spatial skills. Two studies (Jarvis & Gathercole, 2003; St Clair-Thompson & Gathercole, 2006) focused on 11-year-olds’ achievement in UK national standardized science tests in relation to working memory. The findings of both studies pointed towards the VSWM task as being predictive of performance in science. However, because these tasks are designed to test both the visual and spatial aspects of spatial cognition, complex working memory span tasks often confound object/visual, and location/spatial skills. It is therefore not possible to determine the extent to which the associations reported relate to the more intrinsic and extrinsic, or static and dynamic, aspects of the spatial task.
A few studies to date have examined children's science performance and learning in relation to other spatial skills (e.g., Harris, 2014; Mayer, Sodian, Koerber, & Schwippert, 2014; Tracy, 1990). Tracy (1990), for example, found that 10- to 11-year-olds in a higher spatial ability grouping outperformed those in a lower spatial ability grouping on a standardized science measure. However, this study did not include any other non-spatial cognitive measures and therefore did not discount such cognitive factors as an alternative explanation. It also used a composite spatial measure. One more recent study that did compare different spatial ability measures found that mental folding accuracy, but not mental rotation accuracy, predicted 5-year-old's understanding of force and motion, but this finding was limited to intrinsic–dynamic skills (Harris, 2014).
Changes in the relationship between spatial ability and science at different stages of learning
Spatial skills may be more important for individuals at an earlier stage of learning than those in later stages (Uttal & Cohen, 2012). During initial learning, or for individuals with lower levels of domain-specific knowledge, a learner may use spatial processing to establish mental maps and models, or to problem solve (Mix et al., 2016). In line with this, for example, Hambrick et al. (2012) found that spatial ability interacted with adults’ level of geological knowledge in a geology task in which participants inferred the geologic structure of a mountain range. Specifically, spatial ability was more predictive of performance for participants who had lower levels of geologic knowledge, whereas for those with more domain-specific knowledge, spatial skills were less important.
Developmentally, this hypothesis is also supported by the finding that mental folding ability, an intrinsic–dynamic skill, predicts children's, but not adult's, understanding of forces (Harris, 2014). One possible interpretation of this finding is that younger children must actively visualize the effects of forces to make predictions, whereas adults rely more on knowledge of forces and their effects, which has accumulated over time. The above findings suggest that spatial skills may therefore play a more important role in science achievement for younger compared with older children; however, this has yet to be addressed empirically.
The aim of this study was to examine the relationship between various dimensions of 7- to 11-year-old's spatial skill and their performance in a science assessment, which covered aspects of biology, chemistry, and physics knowledge as well as scientific investigation skills within these areas. School year groups in the UK are further grouped into larger curriculum-linked ‘key stages’. Children in years 3 to year 6 (aged 7–11) are grouped into ‘Key Stage 2’. We therefore sampled children from each year group within Key Stage 2, which meant that the children in the sample were working towards the same overall curriculum objectives. Using a range of ages, we also aimed to determine whether this relationship was moderated by age. Given the dearth of literature on the relationship between children's spatial skills and science reasoning, it is difficult to make specific predictions. Based on the findings of Harris (2014), we predicted that, minimally, intrinsic–dynamic skills would be related to science performance, and this relationship may be stronger for younger children.
A total science score was calculated by totalling the participants’ scores across both paper 1 and paper 2. A total for biology, chemistry, and physics questions across both papers was also calculated. Mean accuracy on the individual spatial ability tasks, mean reaction time, and accuracy for the mental rotation task and mean science scores are reported in Table 4.
Table 4. Descriptive statistics for science total scores, British Picture Vocabulary Scale raw scores, and spatial measures
|Correct overall science score (100)||43.97||14.60||7–75|
|Correct overall science score, Y3 (100)||35.75||10.87||7–51|
|Correct overall science score, Y4 (100)||41.42||14.78||14–72|
|Correct overall science score, Y5 (100)||47.26||14.31||18–71|
|Correct overall science score, Y6 (100)||52.24||13.31||21–75|
|Correct overall biology score (36)||18.63||6.17||3–33|
|Correct overall chemistry score (32)||13.11||5.03||1–26|
|Correct overall physics score (32)||12.91||5.56||2–29|
|I-D (mental rotation accuracy) (40)||33.06||5.8||6–40|
|I-D (mental rotation reaction time)||4059.77||1186.1||892.16–6644.95|
|I-D (mental folding accuracy) (14)||9.36||2.71||0–14|
|I-S (children's embedded figures accuracy) (25)||13.64||4.26||5–23|
|E-S (scaling task accuracy) (18)||11.59||3.23||4–18|
|E-D (spatial perspective taking accuracy) (18)||12.22||3.77||5–18|
Reaction times for correct responses only were considered for mental rotation. This type of rotation task is a variation of a chronometric mental rotation task where children are shown pairs of objects and asked whether they are the same or mirror images. Accuracy and response time is typically considered as a marker of individual differences for these types of mental rotation task (Jansen, Schmelter, Quaiser-Pohl, Neuburger, & Heil, 2013). Response times 2.5 SDs above or below the mean of each cell (angle of rotation) were excluded from the analysis (Whelan, 2008). Values for each participant were calculated by finding the overall mean reaction time for each degree of rotation (45°, 90°, 135°, 180°).
Bivariate correlations were also analysed between the predictive variables (BPVS, age, and spatial ability measures) and the dependent variables (total science score and biology, chemistry and physics subscores), which are reported in Table 5. Partial correlations, controlling for age and BPVS raw scores, between each of the spatial measures and each of the science totals, are reported in the lower triangle of Table 5.
Table 5. Bivariate and partial correlations between study variables
|1. British Picture Vocabulary Scale (BPVS) raw score||–||436**||.747**||.636**||.656**||.630**||.272**||.109||.291**||.197*||.401**||.420**|
|3. Science overall total||–||–||–||.881**||.866**||.880**||.289**||.006||.466**||.366**||.507**||.504**|
|4. Biology total||–||–||–||–||.756**||.710**||.264**||.074||.418**||.307**||.460**||.480**|
|5. Chemistry total||–||–||–||–||–||.714**||.238**||−.032||.351**||.319**||.429**||.423**|
|6. Physics total||–||–||–||–||–||–||.278**||−.043||.395**||.351**||.425**||.469**|
|7. Mental rotation (acc)||–||–||.117||.117||.066||.119||–||.274**||.294**||.068||.221*||.417**|
|8. Mental rotation (RT)|| || ||−.092||.015||−.119||−.112||–||–||−.041||−.100||−.027||−.005|
|9. Mental folding||–||–||.384**||.311**||.211*||.276*||–||–||–||.407**||.408**||.456**|
|10. Embedded figures||–||–||.308**||.230*||.227*||.250**||–||–||–||–||.308**||.360**|
|12. Perspective taking||–||–||.280*||.295**||.178||.229*||–||–||–||–||–||–|
Controlling for these covariates, neither mental rotation accuracy nor response time correlated with any science variables. The mental folding task, the embedded figures task, and the scaling task had small to moderately sized partial correlations (range: .211 < r < .384) with total science scores and biology, chemistry, and physics scores. Perspective taking scores also had small to moderately sized positive partial correlations (range: .229 < r < .295) with all science variables other than chemistry scores, where there was no significant correlation.
Regression analyses were run for overall science scores and for biology, chemistry, and physics scores. There were no significant gender differences in any science scores (p > .05 for all); therefore, participants were treated as one group in the subsequent regression analyses. A hierarchical and stepwise approach was taken to determine the amount of variance in science outcomes that was accounted for by participants’ spatial ability, taking into account the covariates (age and BPVS raw score). In all regression models, covariates were added hierarchically first. Betas reported refer to the final models (Tables 6–9).
Table 6. Multiple regression analysis predicting science total score
|Step (1) Age (months)||.130||.122||.044||31.27||<.001||.21||.21|
|Step (2) British Picture Vocabulary Scale raw score||.412||.567||<.001||106.16||<.001||.58||.37|
|Step (3) Folding (I-D)||1.135||.211||.001||20.62||<.001||.64||.06|
|Step (4) Scaling (E-S)||.735||.162||.010||6.79||.010||.66||.02|
Table 7. Multiple regression analysis predicting biology score
|Step (1) Age (months)||.015||.034||.648||15.10||<.001||.11||.11|
|Step (2) British Picture Vocabulary Scale raw score||.152||.495||<.001||60.38||<.001||.41||.30|
|Step (3) Folding (I-D)||.448||.197||.008||12.77||.001||.47||.06|
|Step (4) Scaling (E-S)||.331||.173||.025||5.13||.025||.49||.02|
Table 8. Multiple regression analysis predicting chemistry score
|Step (1) Age (months)||.045||.122||.103||26.09||<.001||.18||.18|
|Step (2) British Picture Vocabulary Scale raw score||.129||.517||<.001||60.52||<.001||.45||.28|
|Step (3) Embedded Figures (I-S)||.167||.141||.046||6.47||.012||.48||.03|
|Step (4) Scaling (E-S)||.229||.147||.049||3.95||.049||.50||.02|
Table 9. Multiple regression analysis predicting physics score
|Step (1) Age (months)||.121||.297||<.001||47.28||<.001||.28||.28|
|Step (2) British Picture Vocabulary Scale raw score||.121||.439||<.001||44.98||<.001||.48||.20|
|Step (3) Folding (I-D)||.428||.209||.002||9.78||.002||.52||.04|
Entered in the first step of each model, age in months significantly predicted overall scores and scores for individual science areas. Age remained a significant predictor in the final model for overall science scores and physics scores. However, age was not significant in the final model for biology or chemistry. Participants’ BPVS raw score was entered in the second step of each model and was a significant predictor of all science outcomes. BPVS scores remained a significant predictor in all of the final models.
Following entry of age and BPVS scores, we then considered the predictive role of the spatial ability measures. All spatial predictors found to be significantly associated with the respective science score in the prior partial correlation analysis were entered together as a block using forward stepwise entry. Forward stepwise entry was used due to the inter-relatedness of the spatial variables, and because we had no strong theoretical predictions about the basis for a hierarchical ordering of variables within this block.
The forward entry of spatial measures predicting overall science score retained mental folding and spatial scaling. Mental folding accounted for an additional 6% of the variance in total science score, ∆F(1,119) = 20.62, p = <.001, and the scaling task then accounted for a further 2% of the variance in total science scores, ∆F(1,118) = 6.79, p = .010, above the covariates. In the final model, which accounted for 65% of the variance in total science scores (adjusted r2), mental folding was a stronger predictor (β = .211) than scaling (β = .162).
Forward entry of the spatial measures predicting biology scores also retained mental folding and spatial scaling. After step 2, mental folding accounted for an additional 6% of the variance in biology scores, ∆F(1,119) = 12.77, p = .001, and the spatial scaling task accounted for an additional 2% of the variance in biology scores ∆F(1,118) = 5.13, p = .025. The overall model accounted for 47% of the variance in biology science scores (adjusted r2). Mental folding was a stronger predictor (β = .197) than scaling (β = .173) in the final model.
The embedded figures task was retained as a significant spatial predictor of chemistry scores accounting for a further 3% of the variance in chemistry scores, ∆F(1,119) = 6.47, p = .012, above the covariates. In addition, the scaling task was also retained as a predictor of chemistry scores, which accounted for an additional 2% of the variance, ∆F(1,118) = 3.95, p = .049. The final model accounted for 48% of the variance in participants’ chemistry total score (adjusted r2). The two spatial skills in this model had similarly sized β coefficients: embedded figures, β = .141; scaling β = .147. Mental folding was the only retained predictor of the physics scores. It was entered in step 3, and it accounted for an additional 4% of the variance in physics scores, ∆F(1,119) = 9.78, p = .002. The final model accounted for 51% of the variance in physics scores (adjusted r2).
To determine whether age interacted with any of the spatial ability measures, and therefore whether this pattern varied across the age groups, a further four models were constructed in which the covariates were again entered in step 1, followed by the spatial ability measures found to be significant for that science score, followed by an interaction term (age in months × spatial measure). No significant age interactions were found (p > .05 for all).
The aim of the current study was to examine the contribution of spatial skills to primary-school children's performance in a curriculum-based science assessment. The study revealed overall that spatial ability is a predictor of 7- to 11-year-olds’ science achievement. After controlling for receptive vocabulary, which provided an estimate of general intelligence, spatial ability accounted for an additional 8% of the variance in total science scores. This builds upon longitudinal research linking spatial ability to STEM outcomes in adults (Lubinski & Benbow, 2006; Wai et al., 2009) as well as correlational research associating spatial ability to various aspects of science learning in adults (e.g., physics problem-solving: Kozhevnikov & Thornton, 2006). It also builds on research linking VSWM to general science performance in 11-year-olds (Jarvis & Gathercole, 2003; St Clair-Thompson & Gathercole, 2006) and spatial skills to 5-year-olds’ force and motion understanding (Harris, 2014) in two main ways. First, it investigated a broader range of spatial skills and science topic areas. Second, it sampled a wider age range of children within one study to investigate possible developmental changes.
It is first interesting to note that both an intrinsic and an extrinsic spatial skill uniquely predicted overall science scores. This suggests that both within-object and between-object spatial skills support children's science reasoning and supports the broad dissociation between intrinsic and extrinsic spatial skills (Hegarty et al., 2006). Considering the role of specific spatial skills, the results revealed that mental folding, an intrinsic–dynamic spatial skill, was the strongest spatial predictor of total science scores. This general finding builds on past research linking mental folding ability to adult science outcomes (e.g., Baker & Talley, 1972).
Mental folding also emerged as the strongest spatial predictor of biology scores. This is the first study to date linking mental folding ability to biology with children. The ability to flexibly visualize, maintain, and manipulate spatial information may be related to mental model construction and utilization (Lohman, 1996). A mental model (Johnson-Laird, 1983; Zwaan & Radvansky, 1998) is a structural analog that contains spatial and conceptual relations of a process or situation. Children may construct spatially grounded mental models of problem-solving questions, which include relational aspects of the problem, and then manipulate these mental models to solve them. This has been proposed in mathematics research with children (e.g., Rasmussen & Bisanz, 2005). Additionally, the representations children have for domain-specific concepts within biology may be spatially grounded. For example, many of the plant-related questions involve knowledge and understanding of plant anatomy and function, which may be related to one another in mental model format. When recalling the function of roots, children may recall a spatial mental model of a plant, which includes spatial–relational information about the location and structure of different parts of the plant.
Mental folding also predicted physics scores, a finding which builds on the work of Harris (2014), who found that mental folding predicted 5-year-olds’ force and motion understanding. Recall that the mental folding task requires non-rigid, dynamic visualization. The spatial skills required to accurately visualize paper folds may support children in, for example, visualizing and predicting the dynamic effects of forces acting on objects, or the general dynamic transfer of energy, which is central to physics topics. More specifically, spatial visualization skills may enable children to mentally simulate actions and processes, such as reasoning about the way two magnets react to each other.
After controlling for BPVS scores, mental rotation was not a predictor of science achievement, despite it falling into the same Uttal et al. (2013) category as mental folding; this was also found by Harris (2014) in relation to children's force and motion understanding in 5-year-olds. There are several plausible reasons for this. First, as previously described, rotation is a rigid transformation and folding is a non-rigid transformation. In contrast to rotation, where the relationship between all points of the object is preserved, folding creates two separate areas, and the spatial relations between these areas must be maintained as the shape is folded. It is plausible that the additional spatial requirements of the folding task supported more complex visualization between multiple elements in the science assessment. In addition, there are also possible limitations with the rotation task itself. The task uses the same monkey stimuli throughout, with the choice stimuli having the same pattern of blue and red hands, rather than using a range of animals, as is the case with other 2D rotation tasks (e.g., Neuburger, Jansen, Heil, & Quaiser-Pohl, 2011). It is possible that this resulted in children of this age range using a rule-based strategy (i.e., if the monkey's right hand is red in one stimuli, then it will appear to be on the left side on the rotated version), rather than an analog, rotation-based strategy. Finally, research to date with adults and adolescents linking mental rotation to science achievement uses abstract 3D cube mental rotation, in contrast to the 2D animal stimuli used in the current study. Although children up to the age of 10 have difficulty with 3D rotation in its traditional format (Jansen et al., 2013), a 3D mental rotation task with tangible objects has more recently been developed which is suitable from 4 years (Hawes, LeFevre, Xu, & Bruce, 2015). Future work could further investigate the possible influence of stimuli type and test format.
Spatial scaling, an extrinsic/static skill, also emerged as a predictor of total scores, biology scores, and chemistry scores. To our knowledge, this is the first study to link extrinsic–static spatial skills with science achievement. The National Research Council's report ‘A Framework for K-12 Science Education’ (National Research Council, 2012) also identifies scaling within the core theme ‘scale, proportion, and quantity’. It emphasizes that understanding relative magnitude and scale is essential for science; for instance, children must learn to appreciate how systems and processes vary significantly in size (e.g., a cell vs. an organism). Taking a chemistry topic example from the current study, when understanding states of matter, children link how a liquid behaves at the observable macroscopic scale with the molecular processes at the microscopic scale. The report also identifies that children need to confidently move back and forth between representational models of different scales (e.g., for biology: a diagrammatic representation and a life-sized human skeleton model). Switching between scaled models is a central component of the scaling task used in the current study.
The embedded figures task, an intrinsic–static spatial skill, was a significant predictor of chemistry scores only. This builds on prior work which found a relationship between this task and adults’ chemistry performance (Bodner & McMillen, 1986). Intrinsic–static spatial skills relate to form perception and the processing of objects without further transformation. Several of the chemistry items include diagrams which require processing subparts of objects (e.g., three beakers, each with four ice cubes, which either have 1, 2, or 3 layers of insulation). The visual discrimination between the diagrams may support problem-solving needed for this type of question.
Interestingly, biology emerged as the discipline area which was most strongly predicted by spatial ability generally, despite the fact that it is not generally thought of as a spatially demanding area, relative to physics, for example. Although there are examples of spatial ability being related to biology learning in adults (e.g., learning anatomy: Lufler, Zumwalt, Romney, & Hoagland, 2012), in the Wai et al. (2009) longitudinal study, spatial ability in adolescence was predictive of outcomes in physics, engineering and chemistry, but not biology. Although biological concepts may not immediately appear as spatial as other areas, the abstract spatial representations used to organize and classify (e.g., classification keys: binomial, branching tree diagrams used to identify species) may be spatially demanding. It is possible that there is a greater utilization of these kinds of spatial representations for children than for adults.
Models predicting overall science score and performance in each area of science were consistent across development. It had been predicted that spatial skills may contribute more to science performance for younger children, suggesting that as domain-specific knowledge increases, spatial abilities play less of a role in science (e.g., Hambrick et al., 2012); however, this was not upheld in the data. Such a hypothesis is based on the idea that older or more experienced learners can apply knowledge more readily without having to process spatially. For example, this prediction would suggest that spatial visualization would not be a strong predictor of questions where children determined the direction of a force acting on an object because they would simply ‘know’ the answer, without having to visualize it. However, this was not the case. The assessment covered a wide range of topics and it may be that, although the older children were indeed more experienced in science, their in-depth knowledge (i.e., knowledge they could recall at the time of doing the assessment) may have been restricted to the topic or topics they have recently covered in class, for example. Furthermore, the children were all in the same academic Key Stage; with a wider age range, above 12 years possibly, developmental changes may have been observed.
There are also limitations with the study. First, although we included the BPVS as a measure of verbal ability, we did not include a measure of non-verbal reasoning ability. It is possible that the relationships observed may be partly accounted for by aspects of the task that involve fluid intelligence or non-verbal reasoning, in addition to the spatial skill measured. Second, the nature of the composite science assessment used includes aspects of factual knowledge, conceptual understanding, and problem-solving. Dividing outcome measures into these subskills is a possibility for future research.
Relatedly, items also differed in the extent to which they required participants to use overtly spatial representations, such as diagrams. The observed relationship between spatial skills and science achievement may be driven by items which included spatial representations such as these. This is supported by a prior study demonstrating the effectiveness of a science curriculum which included spatial skills training in the form of diagram reading instruction (Cromley et al., 2016). The training was most effective for science post-test items in which interpretation of the diagram was particularly important in answering the question because the diagrams had been used to relate novel curriculum content. That is, the students had not been exposed to the topic or diagram previously in class and the question answer could therefore be derived from interpretation of the diagram alone. Many diagrams in the current study also had a degree of novelty because they were often included to accompany previously unseen problems and scenarios. Future research could compare the contribution of spatial skills to performance on items which rely on diagrams to varying degrees.
The results observed in the current study have implications for interventions to support children's science learning. Given evidence that spatial skills are malleable (Uttal et al., 2013), the finding that spatial scaling, mental folding, and disembedding predict children's science achievement suggests that they are good candidates for spatial training. Long-term interventions involving the training of multiple spatial skills, embedded within the curriculum, may be a particularly effective approach (see Hawes, Moss, Caswell, Naqvi, and MacKinnon (2017) for a mathematics example). Furthermore, interventions to support children's spatial thinking skills could lead to additional long-term benefits for science achievement and engagement.