Predicting Students’ Skills in the Context of Scientific Inquiry with Cognitive, Motivational, and Sociodemographic Variables

Research on predictors of achievement in science is often targeted on more traditional content-based assessments and single student characteristics. At the same time, the development of skills in the field of scientific inquiry constitutes a focal point of interest for science education. Against this background, the purpose of this study was to investigate to which extent multiple student characteristics contribute to skills of scientific inquiry. Based on a theoretical framework describing nine epistemological acts, we constructed and administered a multiple-choice test that assesses these skills in lower and upper secondary school level (n = 780). The test items contained problem-solving situations that occur during chemical investigations in school and had to be solved by choosing an appropriate inquiry procedure. We collected further data on 12 cognitive, motivational, and sociodemographic variables such as conceptual knowledge, enjoyment of chemistry, or language spoken at home. Plausible values were drawn to quantify students’ inquiry skills. The results show that students’ characteristics predict their inquiry skills to a large extent (55%), whereas 9 out of 12 variables contribute significantly on a multivariate level. The influence of sociodemographic traits such as gender or the social background becomes non-significant after controlling for cognitive and motivational variables. Furthermore, the performance advance of students from upper secondary school level can be explained by controlling for cognitive covariates. We discuss our findings with regard to curricular aspects and raise the question whether the inquiry skills can be considered as an autonomous trait in science education research.

[1]  Gender preferences in learning science , 1999 .

[2]  Margaret Wu The Role of Plausible Values in Large-Scale Surveys. , 2005 .

[3]  L. Hoffmann,et al.  An intervention study to enhance girls' interest, self-concept, and achievement in physics classes , 2002 .

[4]  A. Lazonder,et al.  Children’s acquisition and use of the control-of-variables strategy: effects of explicit and implicit instructional guidance , 2014 .

[5]  Stacey Lowery Bretz,et al.  Development of the enzyme–substrate interactions concept inventory , 2012, Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology.

[6]  Wendy K. Adams,et al.  Development and Validation of Instruments to Measure Learning of Expert‐Like Thinking , 2011 .

[7]  D. McNamara,et al.  The Impact of Science Knowledge, Reading Skill, and Reading Strategy Knowledge on More Traditional “High-Stakes” Measures of High School Students’ Science Achievement , 2007 .

[8]  William Elliott,et al.  Suppressor Variables in Social Work Research: Ways to Identify in Multiple Regression Models , 2010, Journal of the Society for Social Work and Research.

[9]  Corinne Zimmerman The development of scientific reasoning skills. , 2000 .

[10]  Mariana G. Hewson,et al.  Effect of Instruction Using Students' Prior Knowledge and Conceptual Change Strategies on Science Learning. Part II: Analysis of Instruction. , 1981 .

[11]  Lena Osterhagen,et al.  Multiple Imputation For Nonresponse In Surveys , 2016 .

[12]  Howard Greisdorf,et al.  Exploring Science: The Cognition and Development of Discovery Processes , 2003, J. Documentation.

[13]  Franz Emanuel Weinert,et al.  Concept of competence: A conceptual clarification , 2001 .

[14]  G. Lienert,et al.  Testaufbau und Testanalyse , 1962 .

[15]  S. Tobias Interest, Prior Knowledge, and Learning , 1994 .

[16]  J. Sweller COGNITIVE LOAD THEORY, LEARNING DIFFICULTY, AND INSTRUCTIONAL DESIGN , 1994 .

[17]  Ann L. Brown,et al.  How people learn: Brain, mind, experience, and school. , 1999 .

[18]  P. Lachenbruch Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[19]  Norman G. Lederman,et al.  Meaningful assessment of learners' understandings about scientific inquiry—The views about scientific inquiry (VASI) questionnaire , 2014 .

[20]  M. Segers,et al.  The Relation Between Assessment Practices and Outcomes of Studies: The Case of Research on Prior Knowledge , 1999 .

[21]  R. Tiemann,et al.  Assessing students’ abilities in processes of scientific inquiry in biology using a paper-and-pencil test , 2013 .

[22]  Nicholas D. Myers,et al.  Student and school predictors of high‐stakes assessment in science , 2010 .

[23]  D. Kuhn,et al.  Direct instruction vs. discovery: The long view , 2007 .

[24]  J. Shea National Science Education Standards , 1995 .

[25]  R. Justi Learning how to model in science classroom: key teacher's role in supporting the development of students' modelling skills , 2009 .

[26]  F. Paas,et al.  Cognitive Load Theory and Instructional Design: Recent Developments , 2003 .

[27]  Detlef Urhahne,et al.  The relationship in biology between the nature of science and scientific inquiry , 2014 .

[28]  Ronald D. Anderson Reforming Science Teaching: What Research Says About Inquiry , 2002 .

[29]  Raymond J. Adams,et al.  PISA 2000 technical report , 2002 .

[30]  Manfred Prenzel,et al.  Research on Interest in Science: Theories, methods, and findings , 2011 .

[31]  Eckhard Klieme,et al.  PISA 2006: Skalenhandbuch. Dokumentation der Erhebungsinstrumente , 2009 .

[33]  M. Lipsey,et al.  Performance Trajectories and Performance Gaps as Achievement Effect-Size Benchmarks for Educational Interventions , 2008 .

[34]  S. Allie,et al.  Point and Set Reasoning in Practical Science Measurement by Entering University Freshmen. , 2001 .

[35]  N. Reid,et al.  Gender and physics , 2003 .

[36]  Norman G. Lederman Syntax Of Nature Of Science Within Inquiry And Science Instruction , 2006 .

[37]  John B. Black,et al.  The Development of Cognitive Skills To Support Inquiry Learning , 2000 .

[38]  Thomas Andre,et al.  Gender, prior knowledge, interest, and experience in electricity and conceptual change text manipulations in learning about direct current , 1997 .

[39]  Stacey Lowery Bretz,et al.  Development and Assessment of A Diagnostic Tool to Identify Organic Chemistry Students’ Alternative Conceptions Related to Acid Strength , 2012 .

[40]  Elke Sumfleth,et al.  Students' knowledge about chemical reactions – development and analysis of standard-based test items , 2011 .

[41]  S. Simon,et al.  Attitudes towards science: A review of the literature and its implications , 2003 .

[42]  Jack Barbera,et al.  Psychometric analysis of the thermochemistry concept inventory , 2014 .

[43]  B. Crawford Learning to Teach Science as Inquiry in the Rough and Tumble of Practice , 2007 .

[44]  J. van Braak,et al.  The Role of Students’ Home Language in Science Achievement: A multilevel approach , 2014 .

[45]  John T. Guthrie,et al.  Predicting Conceptual Understanding With Cognitive and Motivational Variables , 1999 .

[46]  Eckhard Klieme,et al.  Current Issues in Competence Modeling and Assessment , 2008 .

[47]  Ulrich Trautwein,et al.  Large-scale student assessment studies measure the results of processes of knowledge acquisition: Evidence in support of the distinction between intelligence and student achievement , 2009 .

[48]  J. Arnold,et al.  Understanding Students' Experiments—What kind of support do they need in inquiry tasks? , 2014 .

[49]  Michelle Cook Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles , 2006 .

[50]  André A. Rupp,et al.  An NCME Instructional Module on Booklet Designs in Large‐Scale Assessments of Student Achievement: Theory and Practice , 2009 .

[51]  Craig K. Enders,et al.  An introduction to modern missing data analyses. , 2010, Journal of school psychology.

[52]  J. Cromley,et al.  Reading Comprehension of Scientific Text: A Domain-Specific Test of the Direct and Inferential Mediation Model of Reading Comprehension. , 2010 .

[53]  J. Bransford How people learn , 2000 .

[54]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[55]  Françoise Delamare Le Deist,et al.  What Is Competence? , 2005 .

[56]  M. R. Espejo Applying the Rasch Model: Fundamental Measurement in the Human Sciences , 2004 .

[57]  M. Eckhardt Instruktionale Unterstützung beim Lernen mit Computersimulationen im Fach Biologie , 2010 .

[58]  Amy M. Shapiro,et al.  How Including Prior Knowledge As a Subject Variable May Change Outcomes of Learning Research , 2004 .

[59]  Leona Schauble,et al.  Students' Understanding of the Objectives and Procedures of Experimentation in the Science Classroom , 1995 .

[60]  Vincent N. Lunetta,et al.  The Laboratory in Science Education: Foundations for the Twenty-First Century , 2004 .

[61]  Ana Taboada,et al.  Relationships of general vocabulary, science vocabulary, and student questioning with science comprehension in students with varying levels of English proficiency , 2012 .

[62]  J. Lagowski National Science Education Standards , 1995 .

[63]  S. Duggan,et al.  Intermediate General National Vocational Qualification (GNVQ) Science: A missed opportunity for a focus on procedural understanding? , 2000 .

[64]  C. Fox,et al.  Applying the Rasch Model: Fundamental Measurement in the Human Sciences , 2001 .

[65]  Eckhard Klieme,et al.  PISA 2006 : Die Ergebnisse der dritten internationalen Vergleichsstudie , 2007 .

[66]  Johannes Hartig,et al.  Multidimensional IRT models for the assessment of competencies , 2009 .

[67]  L. Hoffmann,et al.  A curricular frame for physics education: Development, comparison with students' interests, and impact on students' achievement and self‐concept , 2000 .

[68]  J. Kelley,et al.  Family scholarly culture and educational success: Books and schooling in 27 nations , 2010 .

[69]  D. Klahr,et al.  All other things being equal: acquisition and transfer of the control of variables strategy. , 1999, Child development.