About the Authors

W. Richards Adrion is a Professor Emeritus in the College of Information and Computer Sciences at the University of Massachusetts Amherst. His research is on teaching, learning and broadening participation in the STEM disciplines. [email protected]

John Airey is a Reader in Physics Education at the Department of Physics and Astronomy, Uppsala University, Sweden. He is also a Senior Lecturer in English for Specific Purposes at the Department of Languages, Linnæus University, Sweden. John’s research interests focus on disciplinary learning and its relationship to language and other semiotic resources. [email protected] http://www.physics.uu.se/research/physics-education-research/

Carl Angell is professor of physics education at the Department of Physics, University of Oslo, Norway. He taught physics and mathematics for many years in upper secondary school before his doctorate in physics education. He works with science teacher education and in-service and continued education for teachers. His research interests are teaching and learning physics, students’ understanding of, and attitudes to, physics and physics learning and recruitment to science and physics education. [email protected] http://www.mn.uio.no/fysikk/english/people/aca/carla/index.html

Youngkwan Cha is a graduate student at the University of Massachusetts Amherst. His research interests include metaphor theory in cognitive linguistics and how conceptual metaphor applies to second language teaching. [email protected]

© Springer International Publishing AG 2017 311 D.F. Treagust et al. (eds.), Multiple Representations in Physics Education, Models and Modeling in Science Education 10, DOI 10.1007/978-3-319-58914-5 312 About the Authors

Mei-Hung Chiu is Professor of Science Education at the Graduate Institute of Science Education of the National Taiwan Normal University, Taipei, Taiwan. She taught secondary chemistry for 3 year in schools in Taiwan before her doctorate at Harvard Graduate School of Education, USA. Her research interests include chemistry education, students’ conceptions construction and conceptual change in science, nature of models and modelling processes, augmented reality in science learning, and alternative assessment and evaluation of science education programs. [email protected] http://science.gise.ntnu.edu.tw/chiu/index.html

Hye-Eun Chu is a lecturer in science education at Macquarie University in Sydney, Australia. Before joining Macquarie University she was an assistant professor in science education for 6 years at Nanyang Technological University, Singapore. Her research interests include the investigation of students’ conceptual development in science learning with a current focus on students’ explanatory models, the influence of learner belief on science learning, and formative assessment in the context of inquiry based teaching. [email protected]

Reinders Duit is Prof. Emeritus for Physics Education at the national centre for science education research at the IPN (Institute for Science and Mathematics Education) in Kiel/Germany. He studied physics and mathematics at the University of Kiel. His Masters thesis (1968) investigated the role of students’ conceptions of the simple electric circuit, his PhD (1973) dealt with long term retention of physics taught in a spiral physics curriculum. His Habilitation (second doctorate in Germany) investigated the long-term development of the energy concept (1985). His major still lasting research fields include conceptual change (in close cooperation with David Treagust), studies on the long-term development of the energy concept, and the design of the “Model of Educational Reconstruction” (in close cooperation with Ulrich Kattmann and his colleagues). [email protected]

Noah Finkelstein is a Professor of Physics at the University of Colorado Boulder. He is a PI in the Physics Education Research (PER) group, a do-director of CU’s Center for STEM Learning, and co-director of the network of STEM education Centers in the United States. His research in physics education focuses on studying the conditions that support students’ interests and abilities in physics – developing models of context; he also conducts research on institutional change in higher education. [email protected] http://spot.colorado.edu/~finkelsn

Hans E. Fischer is Professor Emeritus at the University Duisburg-Essen in Essen, Germany. He taught secondary physics and mathematics for 10 years in schools in Germany before his doctorate and his habilitation at the University of Bremen, Germany. Following this, he worked as Professor in Dortmund and accepted the About the Authors 313 offer of a research professorship of the German Research Foundation (DFG) in Essen. His research interests include students’ concept building and the organisa- tion of learning processes in lesson settings at schools and universities. [email protected] https://www.uni-due.de/fischer

John K. Gilbert is Emeritus Professor of The University of Reading, UK, and an Adjunct Professor of the Australian National University. After 9 years of teaching science in secondary (high schools) he has devoted the rest of his career to research and development in schools and universities. His research interests now focus on the role of models in the learning and teaching of science in both formal and informal contexts and in particular on the creation and interpretation of scientific explanations. [email protected]

Brian E. Gravel is an Assistant Professor of Education at Tufts University, where he also received his Ph.D. in science education. Brian serves as the Director of Elementary STEM Education within the Department of Education at Tufts. His research focuses on students’ representational practices while engaging with educational technologies and in technology-rich learning environments like Makerspaces. [email protected] http://ase.tufts.edu/education/people/gravel.htm

Øystein Guttersrud is Associate Professor at the University of Oslo, Norway. He has years of experience with assessments and is currently employed in the Norwegian Centre for Science Education. His research interests include assessments in physics, science and health. [email protected]

Dave Hart directs the Enterprise Systems & Development group in Information Technology at the University of Massachusetts, Amherst. He has a long-standing research interest in the uses of instructional technology in education. [email protected]

Rosa Hettmannsperger is research associate in the department of Educational Science at the University of Frankfurt. She received her PhD in psychology from the University of Koblenz-Landau and has been employed at the University of Geneva, the University of Education in Ludwigsburg and the University of Heidelberg. Her research interests and publications are in the field of educational psychology and science education with a focus on learning with multiple representations and conceptual change. [email protected]

Christopher N. Hill is a Principal Research Engineer in the Earth, Atmospheric and Planetary Sciences program at Massachusetts Institute of Technology. He is interested in the application of large-scale computation to all aspects of understanding Earth and planetary systems. [email protected] 314 About the Authors

Peter Hubber is Associate Professor in Science Education at Deakin University, Australia. He taught secondary physics, science and mathematics for 22 years in schools in rural Victoria, Australia before entering Deakin University. His research interests are in the role of representations in teaching and learning science and ICT in science education. [email protected] https://www.deakin.edu.au/about-deakin/people/peter-hubber

Per Morten Kind is Senior Lecturer at Durham University in the UK. He has pre- viously worked as a science teacher in secondary schools and as a teacher educator and researcher in two higher education institutions in Norway. His main research interest is in teaching and learning of scientific inquiry and reasoning. Currently he is engaged in research to improve the pedagogy of physics teaching in developing nations. [email protected] https://www.dur.ac.uk/education/staff/profile/?id=2416

Patrick B. Kohl is a Teaching Professor at the Colorado School of Mines with a background in physics education research (PER). His primary expertise lies in the adaptation of the canonical Studio Physics model to the specific conditions associ- ated with any local implementation. More recently, Dr. Kohl has begun work on revitalizing upper-division courses, including the adaptation of the flipped classroom to upper division Electricity and Magnetism and the creation of an elective course on the fostering of creativity during physics problem solving. [email protected]

Yen-Ruey Kuo is a postdoctoral researcher at the Graduate Institute of Science Education, National Changhua University of Education in Taiwan. He obtained his PhD degree in science education at Curtin University in Australia. His research interests include multiple representations, inquiry-based instruction, and students’ affective learning outcome. [email protected]

Sangchil Lee is a doctoral student in Teacher Education and Curriculum Studies at the University of Massachusetts, Amherst. His research interests include investigation of the meaning potentials of multiple representations and how teacher talk mediates them. [email protected]

Jing-Wen Lin is Professor in the Graduate Institute of Science Education and Director of the Center of Science Education at National Dong Hwa University, Taiwan. She taught elementary school science and other subjects for 5 years before working in universities. Her research interests include students’ conceptual change and evolution, and how models and modelling can contribute to science teaching and learning. [email protected] http://134.208.10.216:8080/PDF_FILES/5362.pdf About the Authors 315

Cedric Linder is Chair and Professor in the Division of Physics Education Research at the Department of Physics and Astronomy at Uppsala University, Sweden. Cedric has extensive experience teaching university and high school physics, and much of his research has been situated in the complex relations between educational theory, teacher knowledge, teacher action and student learning. In 2014, Cedric was recipi- ent of The International Commission on Physics Education’s Medal in recognition of outstanding work in the field of physics education research. [email protected] http://www.physics.uu.se/forskning/Fysikens+didaktik/?languageId=1

Andreas Müller is professor of science education at the University of Geneva, Switzerland. After a PhD in physics (Heidelberg, Germany) and a habilitation in physics education (Giessen, Germany), he worked in several research and teacher education institutions in Germany. His R&D interests include context-based science education and the role of tasks for effective learning, in particular regarding the use of multiple representations as cognitive tools. In science teacher education, his main goal is to provide a synthesis of research-based knowledge and good practice. [email protected].

Pasi Nieminen (PhD) is a postdoctoral researcher at the University of Jyväskylä, Finland. He taught lower secondary mathematics, physics and chemistry for 2 years before his PhD studies. His dissertation (2013) focused on multiple representations in physics learning. At the moment, he works as a researcher in two research projects. EU-funded ASSIST-ME (2013–2016) investigates formative and summative assessment in inquiry-based STEM education. DARLING (2015–2019) focuses on teachers’ orchestration of student argumentation in physics and mathematics and it is funded by the Finnish Academy. [email protected]

Maria Opfermann works as a professor at the Ruhr Universität Bochum in Germany. She obtained her PhD at the Knowledge Media Research Center in Tuebingen, Germany, before working as a postdoc and assistant professor at the University of Duisburg-Essen, the Saarland University and the Utrecht University. Her research and teaching interests include multimedia learning (in particular the kind of visualizations that can foster learning), cognitive load and its assessment, and the support of self-regulated learning and metacognitive abilities. [email protected] http://ife.rub.de/sdf/team

Antti Savinainen started his PhD thesis about students’ conceptual coherence of the force concept as a Marie Curie Fellow at the University of Leeds in 2001. He finished his thesis at the University of Joensuu, Finland in 2004. Antti teaches physics to the Finnish curriculum and for the International Baccalaureate in Kuopio Lyseo Upper Secondary School. In addition, he works as an Adjunct Professor (Physics Education Research) at the University of Jyväskylä, Finland. [email protected] 316 About the Authors

Jochen Scheid is currently a postdoctoral researcher at University Koblenz-Landau, Germany, and supervisor at the graduate school “Teaching and Learning Processes” of the German Research Foundation (DFG) in Landau, Germany. He held a position at University of Education in Lucerne, Switzerland. Before his doctorate in physics education at the DFG graduate school in Landau, he taught physics and biology in secondary schools for 7 years. His research is focused on teaching-learning sequences, cognitive activation, experiments, multiple representations, and representational competence. [email protected]

Annett Schmeck works as a project manager in the field of integration and education for the Mercator Foundation in Essen in Germany. She obtained her PhD at the department of Instructional Psychology at the University of Duisburg-Essen before working as a postdoctoral researcher in the department. Her research interests include multimedia learning and in particular learning with self-generated visualizations (that is, paper-based and computer-based drawings), cognitive load and self-regulated learning in general. [email protected]

Wolfgang Schnotz is Professor Emeritus of General and Educational Psychology at University of Koblenz-Landau. He received his PhD from the Technical University Berlin and held positions at University of Tübingen, University of Bielefeld, University of Vienna, and University of Jena. He was Chief Editor of the journal Learning and Instruction, and he is editorial board member of numerous journals. He has published in the field of reading and listening comprehension, comprehension of graphics, learning from text, hypermedia and animation. [email protected]

Salim Siddiqui is a retired Senior Lecturer within the Department of Physics and Astronomy at Curtin University in Perth Western Australia. He taught physics at Degree College in Pakistan for 10 years before completing his PhD in Physics from The University of Western Australia. His research interests include medical physics and physics education research. He was awarded Curtin Excellence and Innovation in Teaching Award in 2006. [email protected]

Florence R. Sullivan is an Associate Professor of learning technology at the University of Massachusetts, Amherst. Her research focuses on student collaborative learning processes in computational and technology-enabled learning environments. [email protected] http://people.umass.edu/florence

David F. Treagust is John Curtin Distinguished Professor at Curtin University in Perth, Australia. He taught secondary science for 10 years in schools in England and Australia before working in universities in the USA and Australia. His research About the Authors 317 interests include students’ ideas about science concepts and their contribution to conceptual change and how multiple representations can contribute to teaching, learning and diagnostic assessment. [email protected] http://oasisapps.curtin.edu.au/staff/profile/view/D.Treagust

Kofi Charu Nat Turner is a published author and Associate Professor at the University of Massachusetts, Amherst. He has taught in schools in Japan, Ghana and the U.S. for 20 years. [email protected] http://www.umass.edu/education/faculty-staff-listings/KCNatTurner

Russell Tytler is Alfred Deakin Professor, Science Education, at Deakin University, Melbourne. His research focuses on student learning and reasoning in science, pedagogy and teacher learning, school-community links and wider STEM education policy. He leads an active STEM Education research group that is intensely classroom focused. [email protected] http://www.deakin.edu.au/about-deakin/people/russell-tytler

Jouni Viiri is professor of mathematics and science education at the University of Jyväskylä, Finland. He taught physics, chemistry and mathematics for 5 years in secondary schools and then 10 years physics for engineering students. His research interests include the relation of classroom interactions to students’ learning, use of technology in education and educational research such as eye tracking in multiple representation studies. [email protected] https://www.jyu.fi/edu/laitokset/okl/en/staff/viiri-jouni

Bradford Wheeler is a doctoral candidate in the College of Education at the University of Massachusetts, Amherst. He conducts research on faculty development and education technology. [email protected] www.bradford-d-wheeler.com

Michelle H. Wilkerson is an Assistant Professor at the University of California, Berkeley. She received her PhD in Learning Sciences from Northwestern University, and also served as an Assistant Professor at Tufts University from 2011 to 2015. Michelle’s research focuses on secondary students’ learning with and about simula- tions, data visualizations, and other scientific computing tools. [email protected] http://www.ocf.berkeley.edu/~mwilkers 318 About the Authors

Mihye Won is Senior Lecturer at Curtin University in Perth, Australia. She studied chemistry and science education at Ewha University, Seoul, Korea, and completed her PhD study at the University of Illinois at Urbana-Champaign, USA. Her research interests include Deweyan inquiry-based teaching and learning, and the use of multiple representations in science education. [email protected] http://oasisapps.curtin.edu.au/staff/profile/view/Mihye.Won

Chee Leong Wong is an educational consultant in Singapore. He taught physics for more than 10 years in Catholic Junior College and National University of Singapore High School of Mathematics and Science. He has recently completed his PhD thesis titled “A framework for defining physical concepts”. His research interests include multiple representations in science education, inquiry approaches in learning science, definitions of scientific concepts, history and philosophy of science, and ana- lysing The Feynman Lectures on Physics. [email protected]

Jeff Xavier is an evaluator of educational programs at SageFox Consulting Group in Amherst, Massachusetts. He specializes in the evaluation of STEM-related initia- tives in higher education.

Jennifer Yeo is Assistant Professor at National Institute of Education, Nanyang Technological University, Singapore. She taught physics at the secondary level for 8 years in Singapore schools. Her research interests include understanding how people learn science in computer-supported learning environments and constructiv- ists’ learning environments. She has particular interest in how students produce explanation in science and the role of representations in mediating thinking and reasoning. [email protected] http://www.nie.edu.sg/profile/yeo-jennifer

Marjan Zadnik is Adjunct A/Professor and was the Inaugural Dean of Teaching and Learning in Engineering, Science and Computing, at Curtin University, over- seeing the teaching quality of hundreds of academics, and the learning experience of their students. He has won many awards for excellence in teaching, as well as competitive research grants. Current educational research projects include measur- ing the effectiveness of teaching Einsteinian physics in schools, as well as investi- gating how students learn in science and engineering laboratory environments. [email protected] Index

A C Activity theory, 234 Case-study, 47, 52, 55, 61, 64, 126, 178, Adrion, W.R., 289–308 233, 259 Ainsworth, S., 74, 76, 79, 183, 184, 291, 292, Cha, Y., 289–309 294, 305, 307 Chiu, M.-H., 71–89 Ainsworth, S.E., 3, 8, 10, 11 Choreographies of teaching, 2, 16, 17 Airey, J., 95–119 Chu, H.-E., 183–204 Alternative conception, 71, 72, 78, 84, 85, Coe, R., 33 88, 125, 142, 143, 147, 163, 184, 186, Cognitive activation, 212, 213, 215, 217, 193, 201 224, 225 Analogical limitations, 72, 83–85, 87 Cognitive load, 14, 16, 27, 72, 74, 78, 82–88, Analogical representations, 71–89 97, 116, 164, 179, 213, 224, 225 Analogies, 14, 58, 71–89, 123, 184, 300 Cole, M., 234 Anderson, G., 126 Coletta, V.P., 222 Angell, C., 25–44 Collaborative learning, scaffolding, 290 Animation, 12, 13, 16, 31, 43, 53, 54, Computational modeling, 51–53 63, 183, 189, 194, 196, 289, Conceptual change, 71, 84, 125, 157, 184, 290, 294, 298 212, 214, 217, 222–224 Arons, A.B., 187 Conceptual understanding, 11, 125, 126, 136, Assessment, 126, 127, 129, 131, 132, 135, 169, 177, 211–213, 215–217, 220, 136, 142, 151, 158, 237, 306 223–226, 286, 291, 297, 300, 301, 305 Astronomy, 139–159 Crombie, A.C., 28 Ausubel, D., 222 Cueing, 241, 247–250

B D Baeriswyl, F.J., 2, 5, 16, 17 Daniels, H., 234 Berland, L.K., 50 De Jong, T., 76 Black, D.E., 77 De Posada, J.M., 184, 190 Bodemer, D., 125 Definition, 99, 127, 156, 170, 185, 187–192, Bruner, J.S., 103 200–202, 222, 234

© Springer International Publishing AG 2017 319 D.F. Treagust et al. (eds.), Multiple Representations in Physics Education, Models and Modeling in Science Education 10, DOI 10.1007/978-3-319-58914-5 320 Index

DeFT framework, 9, 11 Geometrical optics, 210, 211, 215, Design research, 3, 31 217–219, 222 Diagrammatic, 48, 153, 158, 187, 189, 201, Gilbert, J.K., 2, 3, 255–286, 291, 305, 307 238, 294, 300 Glynn, S.M., 79 Disciplinary shorthand, 101, 102, 107, 109 Graphical representations, 177, 201, 202, 240 DiSessa, A., 306 Gravel, B.E., 47–68 Duit, R., 23–24 Guided inquiry, 139, 143, 144, 156, 157, 159 Dupin, J.J., 77, 193 Guttersrud, Ø., 25–44

E H Effect size, 13, 33, 35, 131, 220–224, 226 Hake, R., 224 Electric current, 11, 13, 77, 78, 82, 87, Harrison, A.G., 73, 74, 77 184–202 Hart, D., 289–308 Engelhardt, P.V., 222 Härtel, H., 77 Examination-Analogical Mapping- Hartshorne, C., 280 Transformation (EAT), 72, 76, 78, Hettmannsperger, R., 209–226 87–89 Hill, C.N., 289–308 External representation, 2, 3, 8–13, 15–18, 96, Hmelo-Silver, C.E., 50 98, 164, 179, 183, 292 Höffler, T.N., 13, 16 Hubber, P., 139–159

F Finkelstein, N., 231–252 I Fischer, H.E., 1–18, 93, 94 Image formation, 210, 214, 216, 218, Force, 1, 10, 11, 26–29, 37, 132, 158, 219, 223 163–166, 169–171, 173–179, 191, 194, Instructional environment, 73, 233–235, 198, 200, 212, 268, 269, 275, 279, 282, 250, 252 284, 285, 306 Instrument, 33, 83, 126, 127, 129, 131, Force Concept Inventory (FCI), 164–166, 134–136, 144, 155, 218, 219, 223, 237, 168–170, 177, 178, 222 255, 283 Ford, M., 28, 29, 41 Interaction diagram (ID), 171, 173–176, Form, 2, 3, 5, 12, 14, 15, 18, 25, 28, 35, 38, 178, 179 43, 47–49, 51, 58, 62, 65, 66, 68, 72, Interaction diagrams, 164, 173, 175 79, 81, 82, 86, 101, 104, 105, 118, 134, Interactive-textbooks, 289 141–143, 145, 149, 151, 152, 154, 164, Introductory courses, 125, 127, 233, 235 183–185, 188, 191, 193, 194, 197, 201, Isomorphic problems, 241, 251 210, 216, 219, 223, 225, 232, 234, 252, 256–259, 261, 268, 274, 279–286, 295 Fredlund, T., 105, 107, 108, 110, 119 J Free-body diagram, 30, 176 Johnson, M., 268 Freyberg, P., 77 Johsua, S., 77, 193 Function, 3, 8–11, 18, 27, 60, 64, 72, 74, 76, 99, 102, 105–107, 118, 164, 183, 184, 233, 286 K Kalkan, H., 156 Kalyuga, S., 15 G Kind, P.M., 25–44 Galilei, G., 5 Kiroglu, K., 156 Galili, I., 125 Kohl, P., 231–252 Garnett, P.J., 186 Kozma, R., 298 Gauld, C.F., 77 Kress, G., 105, 106, 261 Gentner, D., 77, 79 Kuhn, T.S., 98, 106 Gentner, D.R., 77, 79 Kuo, Y.-R., 135 Index 321

L 123–136, 139, 154, 158, 163–179, Lakoff, G., 268 183–202, 209, 211, 212, 214, 218, 223, Langer, I., 12 225, 247–250, 256, 281, 283, 285, 286, Learner characteristics, 11, 15, 16, 18 289, 290, 306, 307 Learning, 2–18, 26–28, 31–34, 36, 37, 39–43, Multiple visualzations, 289–308 47, 49, 50, 52, 64, 66–68, 72–75, 78, 79, 81–89, 95–99, 102, 103, 105–107, 110–113, 115–119, 124–126, 129, 134, N 136, 139–146, 153–155, 157–159, 163, Nersessian, N.J., 51, 58 164, 169–171, 174, 175, 177–179, Netz, R., 27 183–185, 190, 192, 201, 209–225, 231, Newton’s third law, 171–179 234, 250, 255, 258, 264, 284, 286, Niegemann, H.M., 14 289–292, 296, 297, 301, 305–307 Nieminen, P., 163–179 Learning technologies, 74 Non-physics majors, 136 Lee, S., 289–308 Lemke, J.L., 105 Leutner, D., 13 O Level of abstractness, 258, 259, 281 Opfermann, M., 1–18 Level of complexity, 75 Optics, 28, 111, 124–127, 129, 131, 132, Level of precision, 257, 258 134–136, 212, 213, 223, 237, 239 Li, Q., 306 Osbeck, L.M., 51, 58 Lin, J.-W., 71–89 Osborne, R., 77 Linder, C., 95–119 Oser, F.K., 2, 5, 16, 17 Linguistic mode, 139 Littleton, K., 40 Lowe, R., 210 P Paatz, R., 14 Passmore, C., 50 M Pedagogical affordances, 99, 105–110, 119 Mathematical modelling, 2, 17, 25, 26, 28–33, Penuel, W.R., 306 36, 39–43 Personal preferences, 29, 72, 82–84, 87, 88 Mayer, R.E., 7, 15, 27, 97 Physics teaching, 2, 26, 29, 32–37, 39, 41, 42 McDermott, L., 96, 105 Ploetzner, R., 210 Meaning-making, 49, 95, 97–99, 103, 106, Plummer, J.D., 143 107, 158, 159, 291, 298, 303 Mechanics, 3, 28, 77, 99, 113, 168, 174, 212, 225, 236 R Mercer, N., 40 Ray diagram, 105, 112, 113, 124, 131, Meta-representational competence, 151, 235, 133–135, 210, 213, 214, 216, 217 240, 251 Reiser, B.J., 50 Modeling, 3, 48, 49, 51, 53, 54, 60, 61, 63, Representation construction approach, 66, 252 139–159 Moreno, R., 15 Representational activity tasks (RATs), 213, Mortimer, E.F., 35, 36 214, 217, 218, 223–225 Müller, A., 209–226 Representational coherence, 168, 210, 211, Multimedia learning, 3–7, 11 213, 214, 217, 218, 220, 223, 225 Multi-modal representations, 139 Representational competence, 102, 235, 252, Multiple analogies, 71–78, 81, 85–88 290, 298, 307 Multiple external representations (MERs), Representational consistency, 164–166, 8–11, 16, 18 168–171, 177 Multiple representations, 1–18, 29, 31, 34, 39, Representational ladder, 210, 211 43, 49, 72–76, 79, 89, 95, 96, 117, Representational tools, 49, 55, 64, 65, 67 322 Index

Representational Variant of the Force Concept Technology learning technologies, 74 Inventory (R-FCI), 164–166, 168, 169, Text and picture comprehension, 3, 8, 11 171, 177, 178 Thornton, R.K., 170, 177 Representation-related conceptual change Treagust, D.F., 2, 3, 73, 74, (RCC), 213–216, 218, 219, 221–226 123–136, 186 Representations, 1–18, 25–44, 47–51, 55, 58, Turner, K.C.N., 289–308 60–62, 64–68, 72–79, 81–83, 86, 87, Tytler, R., 139–159 89, 96–102, 105, 116, 117, 119, 123–136, 139–154, 156–159,163–168, 171–173, 175, 177–179, 183–202, U 209–218, 222, 223, 225, 231–252, Upper secondary, 25–42, 163, 165, 166, 255–286, 290–294,297–308 177–179 Reyer, T., 17 Ronen, M., 125 Russell, J., 298 V van Heuvelen, A., 96 Veel, R., 257 S Viiri, J., 163–179 Savinainen, A., 163–179 Visualizations, 3, 6, 12–14, 16, 47, 143, Scaffolding activities, 28, 29, 39–42 210, 289–292, 294, 295, 300, Scalise, K., 290 304–308 Scheid, J., 209–226 Vygotsky, L.S., 27, 234 Schmeck, A., 1–18 Schnotz, W., 13, 17, 209–226 Schwedes, H., 77 W Science education, 1–18, 48–50, 72, 88, 96, Waldrip, B., 124, 215 98, 99, 210, 212, 224, 226, 255, 284 Wargo, B.M., 28, 29, 41 Scientific explanations, 255–259, 261, Wheeler, B.D., 289–309 281–286 Wilczek, F., 197 Scientific reasoning, 26, 27, 30, 41, 307 Wilkerson, M.H., 47–68 Scott, P., 35, 36 Won, M., 123–136 Shear, L., 306 Wong, C.L., 183–202 Siddiqui, S., 123–136 Simulation, 25, 31, 47–51, 53, 54, 61–65, 67, 96, 123, 145, 183, 289, 290, 294, 298 X Social semiotics, 95–119 Xavier, J., 289–308 Solomon, J., 77 Xiang, L., 50 Spiro, R.J., 71 Student learning, 72, 140, 143, 144 Students’ perception, 33, 73, 78, 82, Y 84–87 Yap, K.C., 202 Students’ representations, 124, 150 Yeo, J., 255–286 Students’ perceptions, 33, 72, 73 Sullivan, F.R., 289–308 Z Zadnik, M., 123–136 T Zeilik, M., 155 Teaching sequence, 112, 114, 142, 144–147, 152, 216