Research

Spatial Skills in Chemistry – More Than Just Mental Rotation?

Initial Situation & Theoretical Background

In chemistry, visual representations play a key role in understanding subject-specific concepts (Kozma & Russell, 2007) and in problem-solving (Rau, Zahn, Misback, Herder, & Burstyn, 2021). The use of diagrams, molecular structures, and reaction mechanisms in 2D and 3D are just some examples of how representations are employed in chemistry (Harle & Towns, 2011). This requires specific competencies, such as the ability to translate between representations and recognize connections between them (Kozma & Russell, 2007).

In this context, spatial skills also play an important role: they positively influence the handling and learning with representations (Carter, LaRussa, & Bodner, 1987; Buckley, Seery, & Canty, 2018). Spatial skills are one of 16 facets of general intelligence according to the Cattell-Horn-Carroll model (Carroll, 1993; Schneider & McGrew, 2012). They can be understood as the ability to create, mentally store, and manipulate abstract visual images (Lohman, 1979). Spatial skills can be differentiated into 11 factors (Fig. 1), for example, mental rotation, spatial visualization, and spatial orientation (Buckley, Seery, & Canty, 2018).

In most STEM studies that consider spatial skills, usually only one factor of spatial abilities is used, e.g., mental rotation. However, how different factors of spatial skills affect the performance on representation-based chemistry tasks is still little researched and requires further investigation.

Research Questions & Methodology

1. Which factors of spatial abilities play a role in solving representation-based chemistry tasks?

A test to assess spatial abilities is being compiled, consisting of self-developed and adapted test items (Averbeck, 2021; Taskin, Bernholt, & Parchmann, 2015) that cover various subfields of chemistry. Construct validity will be determined through interrater reliability (assignment of test items by subject matter experts). The factors of spatial abilities will be measured using validated test instruments according to Ekstrom, French, Harman, and Derman (1976), such as the Hidden Figure Test and the Paper Folding Test. After validating the developed test, the data will be collected using a multi-matrix sampling approach (Childs & Jaciw, 2002). Data will be gathered in a quasi-longitudinal design with Bachelor of Science students in chemistry or related fields at the beginning and end of their undergraduate studies. The relationship between spatial ability factors and specific representations and representation competencies will be analyzed through correlation and regression analyses.

2. To what extent does the development of specific spatial ability factors influence the learning success of chemistry students?

Based on the results of the first research question, an intervention study will be designed to specifically promote the spatial ability factors identified as relevant. A pre-post design with intervention group(s) and a control group is planned to examine whether this targeted training can improve learning outcomes in representation-based chemistry tasks.

Project outcome

The results of the project contribute to the investigation of the mechanisms by which factors of spatial abilities affect learning success with external representations in chemistry. This can reveal opportunities to improve students’ learning outcomes through targeted development of specific spatial ability factors.

Sebastian Nickel

Wissenschaftlicher Mitarbeiter

Literature

• Averbeck, D. (2021). Zum Studienerfolg in der Studieneingangsphase des Chemiestudiums – Der Einfluss kognitiver und affektiv-motivationaler Variablen. Berlin: Logos.
• Buckley, J., Seery, N., & Canty, D. (2018). A Heuristic Framework of Spatial Ability: a Review and Synthesis of Spatial Factor Literature to Support its Translation into STEM Education. Educational Psychology Review, 30(3), S. 947–972.
• Carroll, J. B. (1993). Human cognitive abilities – A survey of factor-analytic studies. Cambridge: Cambridge University Press.
• Carter, C. S., LaRussa, M. A., & Bodner, G. M. (1987). A study of two measures of spatial ability as predictors of success in different levels of general chemistry. Journal of Research in Science Teaching, 24(7), S. 645-657.
• Childs, R. A., & Jaciw, A. P. (2002). Matrix Sampling of Items in Large-Scale Assessments. Practical Assessment, Research, and Evaluation, 8(Artikel 16).
• Ekstrom, R. B., French, J. W., Harman, H. H., & Derman, D. (1976). Coverbild für Manual for kit of factor-referenced cognitive tests. Princeton, New Jersey: Education Testing Service.
• Harle, M., & Towns, M. (2011). A Review of Spatial Ability Literature, Its Connection to Chemistry, and Implications for Instruction. Journal of Chemical Education, 88(3), S. 351-360.
• Kozma, R., & Russell, J. (2007). Students Becoming Chemists: Developing Representational Competence. In J. K. Gilbdert (Hrsg.), Visualization in Science Education (S. 121-146). Dordrecht: Springer.
• Lohman, D. F. (1979). Spatial ability: A review and reanalysis of the correlational literature . Stanford (CA): Aptitudes Research Project, School of Education und Stanford University.
• Rau, M. A., Zahn, M., Misback, E., Herder, T., & Burstyn, J. (2021). Adaptive support for representational competencies during technology-based problem solving in chemistry. Journal of the Learning Sciences, 30(2), S. 163-203.
• Schneider, J., & McGrew, K. (2012). The Cattell-Horn-Carroll (CHC) Model of Intelligence. In D. P. Flanagan, & E. M. McDonough, Contemporary Intellectual Assessment – Theories, Tests, and Issues (3. Ausg., S. 99-144). New York: Guilford Publications.
• Taskin, V., Bernholt, S., & Parchmann, I. (2015). An inventory for measuring student teachers’ knowledge of chemical representations: design, validation, and psychometric analysis. Chemistry Education Research and Practice, 16(3), S. 460-477.

Activity-Linked Assessment of Motivational Factors in the Student Laboratory 

Student laboratories have long been an established part of the German education landscape and are considered a promising approach to increasing interest in STEM subjects, which tend to be less popular, due to their positive influence on affective and motivational characteristics of participating students (Tillmann & Wegner, 2021; Eilks & Hofstein, 2015).

Interest, according to Krapp’s person-object theory (1992; 1998), is defined as the relationship between a person and an object of interest (e.g., an object, idea, or activity). From the interaction between the person and the object of interest, situational interest can arise, which is described as a temporally unstable and context-dependent psychological state (state) (Kelava et al., 2020). Through internalization, this state can be transformed into individual interest, which is considered a relatively stable personality trait over time (trait) (Krapp, 1992; Kelava et al., 2020).

Student laboratories often aim to foster the emergence of situational interest, as it can be triggered by activities or the environment (Su et al., 2019). At this point, a crucial question arises: Is the prevailing interest of students in a student laboratory truly situational, or does individual interest dominate? Depending on the outcome, comprehensive restructuring measures for such laboratory offerings might be necessary.

To break down the components of interest measurement attributable to the person (individual interest) versus the situation/interaction between situation and person (situational interest), latent-state-trait analyses are suitable (Kelava et al., 2020). This is to be repeated in relation to the little-researched motivational costs (Rosenzweig et al., 2019).

These considerations lead to the following research questions:

FF1: To what extent do … change  

1. situational interest 
2. motivational costs 

depending on the activity phase at the extracurricular learning site (student laboratory)?
 

FF2: To what extent  

1. is the interest  
2. are the motivational costs 

in the respective activity phase, dispositionally (trait) or situationally (state) determined? 

For the study, a student laboratory program on the topic of ocean acidification (target group: lower secondary level of Realschule) was developed, which includes seven ranking situations derived from the RIASEC+N model of interest dimensions during scientific activities (Dierks et al., 2016).

The ranking of the respective activities takes place after each activity phase via a questionnaire on a tablet, in the form of a quantitative survey study with repeated measurements. The constructs of “situational interest,” “motivational costs,” and “cognitive load” are assessed using a total of 8 items on a Likert scale. Additionally, individual interest related to the subject of chemistry, other individual learning prerequisites (e.g., chemistry self-concept), and demographic data are collected before the laboratory program and after the theoretical introduction.

Following a pilot phase, Study Phase I is currently in the final stages of data collection, aiming for a sample size of approximately 350 participants.

The activity-based rankings are analyzed not only descriptively but also through extreme group analyses, correlation and regression analyses, a repeated measures ANOVA, and a latent-state-trait analysis (including confirmatory analysis as well as variance decomposition and the resulting determination of consistency and specificity coefficients). This is done to answer the research questions, draw implications for student laboratory operations, and identify further research needs, among others for Study Phase II (e.g., an intervention study for differentiated interest enhancement).

Literature: 

•  Dierks, P., Höffler, T.; Blankenburg, J., Peters, H. & Parchmann, I. (2016) Interest in science: a RIASEC-based analysis of students’ interests, International Journal of Science Education, 38:2, 238-258 
• Eilks, I., & Hofstein, A. (2015). Relevant Chemistry Education – From Theory to Practice (1-10). Rotterdam: Sense Publishers. 
• Kelava, A., Schermelleh-Engel, K. & Mayer, A. (2020). Latent-State-Trait-Theorie (LST-Theorie). In H. Moosbrugger & A. Kelava (Hrsg.), Testtheorie und Fragebogenkonstruktion (3., vollständig überarbeitete und ergänzte Auflage). Heidelberg: Springer  
• Krapp, A. (1992). Das Interessenskonstrukt. Bestimmungsmerkmale der Interessenshandlung und des individuellen Interesses aus der Sicht einer Person-Gegenstands-Konzeption. In A. Krapp & M. Prenzel (ed.), Interesse, Lernen, Leistung. Neuere Ansätze einer pädagogisch-psychologischen Interessensforschung, (26, 297-330). Münster: Aschendorff. 

• Krapp, A. (1998). Entwicklung und Förderung von Interessen im Unterricht. Psychologie in Erziehung und Unterricht, 44(3), 185-201. 
• Rosenzweig, E. Q., Wigfield, A., & Eccles, J. S. (2019). Expectancy-value theory and its relevance for student motivation and learning.
  In Renninger, K. A., & Hidi, S. E. (Eds.), The Cambridge handbook of motivation and learning. 617–644. Cambridge University Press. 
• Su R., Stoll G., Rounds J. (2019). The nature of interests: Toward a unifying theory of trait–state interest dynamics. In Nye C. D., Rounds J. (Eds.), Vocational interests: Rethinking their role in understanding workplace behavior and practice. SIOP organizational Frontiers series. New York, NY: Routledge. 
• Tillmann, J., & Wegner, C. (2021). Weiterentwicklung eines klassischen Schülerlabors – Darstellung des aktuellen Forschungsstandes. Progress in Science Education (PriSE), 4(2) 

Xenia Schäfer