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Journal of Science Education and Technology

, Volume 26, Issue 1, pp 70–81 | Cite as

The Effectiveness of Interacting with Scientific Animations in Chemistry Using Mobile Devices on Grade 12 Students’ Spatial Ability and Scientific Reasoning Skills

  • Sulaiman M. Al-BalushiEmail author
  • Ali S. Al-Musawi
  • Abdullah K. Ambusaidi
  • Fatemah H. Al-Hajri
Article

Abstract

The purpose of the current study was to investigate the effectiveness of interacting with animations using mobile devices on grade 12 students’ spatial and reasoning abilities. The study took place in a grade 12 context in Oman. A quasi-experimental design was used with an experimental group of 32 students and a control group of 28 students. The experimental group studied chemistry using mobile tablets that had a digital instructional package with different animation and simulations. There was one tablet per student. A spatial ability test and a scientific reasoning test were administered to both groups prior and after the study, which lasted for 9 weeks. The findings showed that there were significant statistical differences between the two groups in terms of spatial ability in favour of the experimental group. However, there were no differences between the two groups in terms of reasoning ability. The authors reasoned that the types of animations and simulations used in the current study featured a wide range of three-dimensional animated illustrations at the particulate level of matter. Most probably, this decreased the level of abstractness that usually accompanies chemical entities and phenomena and helped the students to visualize the interactions between submicroscopic entities spatially. Further research is needed to decide on types of scientific animations that could help students improve their scientific reasoning.

Keywords

Chemistry education Mobile learning Scientific animations Scientific reasoning Spatial ability Three-dimensional representations 

Notes

Acknowledgments

The authors would like to express their sincere appreciation to The Research Council (TRC) in Oman for their generous funding of this project (TRC No. ORG/EHR/12/005) and to various research assistants and teachers who made the implementation of study possible.

References

  1. Akpınar E (2014) The use of interactive computer animations based on POE as a presentation tool in primary science teaching. J Sci Educ Technol 23:527–537. doi: 10.1007/s10956-013-9482-4 CrossRefGoogle Scholar
  2. Al-Abed A (1994) Spatial ability and math achievement for grade ten students. Arab J Educ 14(1):205–225 (in Arabic) Google Scholar
  3. Al-Balushi SM (2013a) The nature of anthropomorphic mental images created by low and high spatial ability students for different astronomical and microscopic scientific topics. Inter J Sci Soc 4(4):51–63Google Scholar
  4. Al-Balushi SM (2013b) The relationship between learners’ distrust of scientific models, their spatial ability, and the vividness of their mental images. Int J Sci Mat Educ 11(3):707–732CrossRefGoogle Scholar
  5. Al-Balushi SM, Al-Battashi IA (2013) Ninth graders’ spatial ability and working memory capacity (WMC) in relation to their science and mathematics achievement and their gender. J Turk Sci Educ 10(1):12–27Google Scholar
  6. Al-Balushi SM, Al-Hajri S (2014) Associating animations with concrete models to enhance students’ comprehension of different visual representations in organic chemistry. Chem Educ Res Pract 15:47–58CrossRefGoogle Scholar
  7. Al-Balushi SM, Coll R (2013) Exploring verbal, visual and schematic learners’ static and dynamic mental images of scientific species and processes in relation to their spatial ability. Int J Sci Educ 35(3):460–489CrossRefGoogle Scholar
  8. Al-Hadhrami A, Ambusaidi A (2012) The relationship between logical thinking and understanding of genetic concepts by grade twelve students in Oman. Al-Najah Univ J 26(4):959–996 (in Arabic) Google Scholar
  9. Al-Khawaldah S (2008) The effectiveness of teaching by using conceptual change texts on grade ten students’ conceptual understanding of photosynthesis. J Damascus Univ 2(1):285–310 (in Arabic) Google Scholar
  10. Barak M, Dori YJ (2011) Science education in primary schools: is an animation worth a thousand pictures? J Sci Educ Technol 20:608–620. doi: 10.1007/s10956-011-9315-2 CrossRefGoogle Scholar
  11. Barnea N, Dori YJ (1999) High-school chemistry students’ performance and gender differences in a computerized molecular modeling learning environment. J Sci Educ Technol 8(4):257–271CrossRefGoogle Scholar
  12. Black AA (2005) Spatial ability and earth science conceptual understanding. J Geosci Educ 53(4):402–414CrossRefGoogle Scholar
  13. Bogacz S, Trafton J (2005) Understanding dynamic and static displays: using images to reason dynamically. Cogn Syst Res 6:312–319CrossRefGoogle Scholar
  14. BouJaoude S, Salloum S, Abd-El-Khalick F (2005) Relationships between selective cognitive variables and students’ ability to solve chemistry problems. Int J Sci Educ 26(1):63–84CrossRefGoogle Scholar
  15. Boyce C, Mishra C, Halverson K, Thomas A (2014) Getting students outside: using technology as a way to stimulate engagement. J Sci Educ Technol 23:815–826. doi: 10.1007/s10956-014-9514-8 CrossRefGoogle Scholar
  16. Brownlow S, McPheron T, Acks C (2003) Science background and spatial abilities in men and women. J Sci Educ Technol 12(4):371–380CrossRefGoogle Scholar
  17. Chiu M-H, Wu H (2009) The roles of multimedia in the teaching and learning of the triplet relationship in chemistry. In: Gilbert J, Treagust DF (eds) Multiple representations in chemical education, models and modeling in science education, vol 4. Springer, Dordrecht, pp 251–283CrossRefGoogle Scholar
  18. Clement J, Zietsman A, Monaghan J (2005) Imagery in science learning in students and experts. In: Gilbert J (ed) Visualization in science education. Springer, Dordrecht, pp 169–184CrossRefGoogle Scholar
  19. Cranford K, Tiettmeyer J, Chuprinko B, Jordan S, Grove N (2014) Measuring load on working memory: the use of heart rate as a means of measuring chemistry students’ cognitive load. J Chem Educ 91:641–647CrossRefGoogle Scholar
  20. Creswell J (2008) Research design: qualitative, quantitative, and mixed methods approaches, 3rd edn. Sage, Los AngelesGoogle Scholar
  21. Ekstrom R, French J, Harmon H (1976) Manual for kit of factor-referenced cognitive tests. Educational Testing Service, PrincetonGoogle Scholar
  22. Gilbert J (2005) Visualization: a metacognitive skill in science and science education. In: Gilbert J (ed) Visualization in science education. Springer, Dordrecht, pp 9–27CrossRefGoogle Scholar
  23. Halpern DF, Benbow CP, Geary DC, Gur RC, Hyde JS, Gernsbacher MA (2007) The science of sex differences in science and mathematics. Psychol Sci Public Interest 8(1):1–51CrossRefGoogle Scholar
  24. Kali Y, Linn MC (2008) Designing effective visualization for elementary school science. Elem Sch J 109(2):181–198CrossRefGoogle Scholar
  25. Kaplan D, Black J (2003) Mental models and computer-based scientific inquiry learning: effects of mechanistic cues on adolescent representation and reasoning about causal systems. J Sci Educ Technol 12(4):483–493CrossRefGoogle Scholar
  26. Karacop A, Doymus K (2013) Effects of jigsaw cooperative learning and animation techniques on students’ understanding of chemical bonding and their conceptions of the particulate nature of matter. J Sci Educ Technol 22:186–203. doi: 10.1007/s10956-012-9385-9 CrossRefGoogle Scholar
  27. Keehner M, Yvonne L, Montello D, Tendick F, Hegarty M (2006) Learning a spatial skill for surgery: how the contributions of abilities change with practice. Appl Cognit Psychol 20:487–503. doi: 10.1002/acp.1198 CrossRefGoogle Scholar
  28. Kelly R (2014) Using variation theory with metacognitive monitoring to develop insights into how students learn from molecular visualizations. J Chem Educ 91:1152–1161CrossRefGoogle Scholar
  29. Kelly R, Jones L (2007) Exploring how different features of animations of sodium chloride dissolution affect students’ explanations. J Sci Educ Technol 16:413–429. doi: 10.1007/s10956-007-9065-3 CrossRefGoogle Scholar
  30. Khan S (2011) New pedagogies on teaching science with computer simulations. J Sci Educ Technol 20:215–232. doi: 10.1007/s10956-010-9247-2 CrossRefGoogle Scholar
  31. Kozhevnikov M, Thornton R (2006) Real-time data display, spatial visualization ability, and learning force and motion concepts. J Sci Educ Technol 15(1):111–132. doi: 10.1007/s10956-006-0361-0 CrossRefGoogle Scholar
  32. Kozma R, Russell J (2005) Students becoming chemists: developing representationl competence. In: Gilbert JK (ed) Visualization in science education, vol 1. Springer, Dordrecht, pp 121–146CrossRefGoogle Scholar
  33. Kumar D, Sherwood R (2007) Effect of a problem based simulation on the conceptual understanding of undergraduate science education students. J Sci Educ Technol 16(3):239–246CrossRefGoogle Scholar
  34. Kumar D, Thomas P, Morris J, Tobias K, Baker M, Jermanovich T (2011) Effect of current electricity simulation supported learning on the conceptual understanding of elementary and secondary teachers. J Sci Educ Technol 20:111–115. doi: 10.1007/s10956-010-9229-4 CrossRefGoogle Scholar
  35. Lawson A (1995) Science teaching and the development of thinking. Wadsworth Publishing Company, BelmontGoogle Scholar
  36. Lawson A, Clark B, Cramer-Meldrum E, Falconer KA, Sequist JM, Kwon Y (2000) Development of scientific reasoning in college biology: do two levels of general hypothesis-testing skills exist? J Res Sci Teach 37(1):81–101CrossRefGoogle Scholar
  37. Levy D (2013) How dynamic visualization technology can support molecular reasoning. J Sci Educ Technol 22:702–717. doi: 10.1007/s10956-012-9424-6 CrossRefGoogle Scholar
  38. Mammino L (2008) Teaching chemistry with and without external representations in professional environments with limited resources. In: Gilbert J, Reiner M, Nakhleh M (eds) Visualization: theory and practice in science education, vol 3. Springer, Dordrecht, pp 155–185CrossRefGoogle Scholar
  39. Michalchik V, Rosenquist A, Kozma R, Kreikemeier P, Schank P (2008) Representational resources for constructing shared understandings in the high school chemistry classroom. In: Gilbert J, Reiner M, Nakhleh M (eds) Visualization: theory and practice in science education, vol 3. Springer, Dordrecht, pp 233–282CrossRefGoogle Scholar
  40. Miyake A, Friedman N, Rettinger D, Shah P, Hegarty M (2001) How are visuospatial working memory, executive functioning, and spatial abilities related? a latent-variable analysis. J Exp Psychol Gen 130(4):621–640CrossRefGoogle Scholar
  41. Monaghan J, Clement J (2000) Algorithms, visualization, and mental models: high school students’ interactions with a relative motion simulation. J Sci Educ Technol 9(4):311–325CrossRefGoogle Scholar
  42. Nakhleh M, Postek B (2008) Learning chemistry using multiple external representations. In: Gilbert J, Reiner M, Nakhleh M (eds) Visualization: theory and practice in science education, vol 3. Springer, Dordrecht, pp 209–231CrossRefGoogle Scholar
  43. Oliver-Hoyo M, Sloan C (2014) The development of a visual-perceptual chemistry specific (VPCS) assessment tool. J Res Sci Teach 51(8):963–981CrossRefGoogle Scholar
  44. Pallant A, Tinker R (2004) Reasoning with atomic-scale molecular dynamic models. J Sci Educ Technol 13(1):51–66CrossRefGoogle Scholar
  45. Powell C, Mason D (2013) Effectiveness of podcasts delivered on mobile devices as a support for student learning during general chemistry laboratories. J Sci Educ Technol 22:148–170. doi: 10.1007/s10956-012-9383-y CrossRefGoogle Scholar
  46. Rapp D, Kurby CA (2008) The ‘ins’ and ‘outs’ of learning: Internal representations and external visualizations. In: Gilbert J, Reiner M, Nakhleh M (eds) Visualization: theory and practice in science education. Springer, Dordrecht, pp 29–52CrossRefGoogle Scholar
  47. Reiner M (2008) The nature and development of visualization: a review of what is known. In: Gilbert J, Reiner M, Nakhleh M (eds) Visualization: theory and practice in science education, vol 3. Springer, Dordrecht, pp 25–27Google Scholar
  48. Ryu S, Han Y, Paik S-H (2015) Understanding co-development of conceptual and epistemic understanding through modeling practices with mobile internet. J Sci Educ Technol 24:330–355. doi: 10.1007/s10956-014-9545-1 CrossRefGoogle Scholar
  49. Silk E, Schunn C, Cary M (2009) The impact of an engineering design curriculum on science reasoning in an urban setting. J Sci Educ Technol 18:209–223. doi: 10.1007/s10956-009-9144-8 CrossRefGoogle Scholar
  50. Sorby SA, Baartmans B (2000) The development and assessment of a course for enhancing the 3-D spatial visualization skills of first year engineering students. J Eng Educ 89:301–307CrossRefGoogle Scholar
  51. Sorby SA, Drummer T, Hungwe K, Charlesworth P (2005) Developing 3-D spatial visualization skills for non-engineering students. In: Paper presented at the 2005 American society for engineering education annual conference & expositionGoogle Scholar
  52. Stieff M (2007) Mental rotation and diagrammatic reasoning in science. Learn Instr 17:219–234CrossRefGoogle Scholar
  53. Stieff M, Raje S (2010) Expert algorithmic and imagistic problem solving strategies in advanced chemistry. Spat Cognit Comput 10:53–81Google Scholar
  54. Stratford S, Krajcik J, Soloway E (1998) Secondary students’ dynamic modeling processes: analyzing, reasoning about, synthesizing, and testing models of stream ecosystems. J Sci Educ Technol 7(3):215–234CrossRefGoogle Scholar
  55. Trafton J, Trickett S, Mintz F (2005) Connecting internal and external representations: spatial transformations of scientific visualizations. Found Sci 10(1):89–106CrossRefGoogle Scholar
  56. Trickett S, Trafton J (2007) “What if…”: the use of conceptual simulations in scientific reasoning. Cognit Sci 31:843–875CrossRefGoogle Scholar
  57. van der Kolk K, Hartog R, Beldman G, Gruppen H (2013) Exploring the potential of smartphones and tablets for performance support in food chemistry laboratory classes. J Sci Educ Technol 22:984–992. doi: 10.1007/s10956-013-9444-x CrossRefGoogle Scholar
  58. van Someren M, Barnard Y, Sandberg J (1999) The think aloud method: a practical guide to modelling cognitive processes. Academic, LondonGoogle Scholar
  59. Velazquez-Marcano A, Williamson V, Ashkenazi G, Tasker R, Williamson K (2004) The use of video demonstrations and particulate animation in general chemistry. J Sci Educ Technol 13(3):315–323CrossRefGoogle Scholar
  60. Wang C-Y, Barrow LH (2011) Characteristics and levels of sophistication: an analysis of chemistry students’ ability to think with mental models. Res Sci Educ 41(4):561–586. doi: 10.1007/s11165-11010-19180-11167 CrossRefGoogle Scholar
  61. Wang H-Y, Duh H, Li N, Lin T-J, Tsai C-C (2014) An investigation of university students’ collaborative inquiry learning behaviors in an augmented reality simulation and a traditional simulation. J Sci Educ Technol 23:682–691. doi: 10.1007/s10956-014-9494-8 CrossRefGoogle Scholar
  62. Wilkerson-Jerde M, Gravel B, Macrander C (2015) Exploring shifts in middle school learners’ modeling activity while generating drawings, animations, and computational simulations of molecular diffusion. J Sci Educ Technol 24:396–415. doi: 10.1007/s10956-014-9497-5 CrossRefGoogle Scholar
  63. Wu H, Shah P (2004) Exploring visuospatial thinking in chemistry learning. Sci Educ 88:465–492CrossRefGoogle Scholar
  64. Wu H, Krajcik JS, Soloway E (2001) Promoting conceptual understanding of chemical representations: students’ use of a visualization tool in the classroom. J Res Sci Teach 38(7):821–842. doi: 10.1002/sce.10126 CrossRefGoogle Scholar
  65. Yarden H, Yarden A (2010) Learning using dynamic and static visualizations: students’ comprehension, prior knowledge and conceptual status of a biotechnological method. Res Sci Educ 40:375–402. doi: 10.1007/s11165-11009-19126-11160 CrossRefGoogle Scholar
  66. Yarden H, Yarden A (2011) Studying biotechnological methods using animations: the teacher’s role. J Sci Educ Technol 20:689–702. doi: 10.1007/s10956-010-9262-3 CrossRefGoogle Scholar
  67. Young K (2005) Direct from the source: the value of ‘think-aloud’ data in understanding learning. J Educ Enq 6(1):19–33Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Sulaiman M. Al-Balushi
    • 1
    Email author
  • Ali S. Al-Musawi
    • 2
  • Abdullah K. Ambusaidi
    • 1
  • Fatemah H. Al-Hajri
    • 1
  1. 1.Curriculum and Instruction Department, College of EducationSultan Qaboos UniversityMuscatSultanate of Oman
  2. 2.Instructional and Learning Technologies Department, College of EducationSultan Qaboos UniversityMuscatSultanate of Oman

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