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The effects of 3D-representation instruction on composite-solid surface-area learning for elementary school students

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Abstract

Providing instruction on spatial geometry, specifically how to calculate the surface areas of composite solids, challenges many elementary school teachers. Determining the surface areas of composite solids involves complex calculations and advanced spatial concepts. The goals of this study were to build on students’ learning processes for basic and composite solids and employ Google SketchUp, an Internet resource tool, to develop and implement surface-area instructional and learning strategies (SAILS) for composite solids, and then measure its effect on learning achievement and attitudes. The fifth-grade students (N = 111) who were enrolled in this study were divided into an experimental and a control group. The experimental group (N = 56) received SAILS instruction, whereas the control group (N = 55) received traditional instruction. The results indicated that students who received SAILS instruction exhibited better performance on both immediate and maintained surface-area learning achievement tests compared to those who received traditional instruction; furthermore, this effect was more prominent among boys than girls. Low- and moderate-ability students who received SAILS instruction exhibited significantly greater improvement of attitudes toward learning mathematics compared to those receiving traditional instruction with physical teaching aids.

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References

  • Ainsworth, S., Bibby, P., & Wood, D. (2002). Examining the effects of different multiple representational systems in learning primary mathematics. Journal of the Learning Sciences, 11(1), 25–61.

    Article  Google Scholar 

  • Aliasgari, M., Riahinia, N., & Mojdehavar, F. (2010). Computer-assisted instruction and student attitudes towards learning mathematics. Education, Business and Society: Contemporary Middle Eastern Issues, 3(1), 6–14.

    Article  Google Scholar 

  • Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70(2), 181–214.

    Article  Google Scholar 

  • Barbieri, M. J., & Light, P. (1992). Interaction, gender, and performance on a computer-based problem solving task. Learning and Instruction, 2(3), 199–213.

    Article  Google Scholar 

  • Battista, M. T. (1990). Spatial visualization and gender differences in high school geometry. Journal for Research in Mathematics Education, 21(1), 47–60.

    Article  Google Scholar 

  • Battista, M. T. (1999). Fifth graders’ enumeration of cubes in 3D arrays: Conceptual progress in an inquiry-based classroom. Journal of Research in Mathematics Education, 30(4), 417–448.

    Article  Google Scholar 

  • Battista, M. T. (2003). Understanding students’ thinking about area and volume measurement. In D. H. Clements & G. Bright (Eds.), Learning and teaching measurement (pp. 122–142). Reston, VA: National Council of Teachers of Mathematics.

    Google Scholar 

  • Battista, M. T., & Clements, D. H. (1996). Students’ understanding of three-dimensional rectangular arrays of cubes. Journal of Research in Mathematics Education, 27(3), 258–292.

    Article  Google Scholar 

  • Ben-Chaim, D., Lappan, G., & Houang, R. T. (1985). Visualizing rectangular solids made of small cubes: Analyzing and effecting students’ performance. Educational Studies in Mathematics, 16(4), 389–409.

    Article  Google Scholar 

  • Black, T. R. (1999). Doing quantitative research in the social sciences. Thousand Oaks, CA: SAGE Publications Inc.

    Google Scholar 

  • Blaye, A., Light, P., Joiner, R., & Sheldon, S. (1991). Collaboration as a facilitator of planning and problem solving on a computer-based task. British Journal of Developmental Psychology, 9(4), 471–483.

    Article  Google Scholar 

  • Braddock, D., Rizzolo, M. C., Thompson, M., & Bell, R. (2004). Emerging technologies and cognitive disability. Journal of Special Education Technology, 19(4), 49–56.

    Google Scholar 

  • Brenner, M. E., Mayer, R. E., Moseley, B., Brar, T., Duran, R., Reed, B. S., et al. (1997). Learning by understanding: The role of multiple representations in learning algebra. American Educational Research Journal, 34(4), 663–689.

    Article  Google Scholar 

  • Chang, K. E., Chen, Y. L., Lin, H. Y., & Sung, Y. T. (2008). Effects of learning support in simulation-based physics learning. Computers & Education, 51(4), 1486–1498.

    Article  Google Scholar 

  • Chang, K. E., Sung, Y. T., & Chen, I. D. (2002). The effect of concept mapping to enhance text comprehension and summarization. Journal of Experimental Education, 71(1), 5–23.

    Article  Google Scholar 

  • Chang, K. E., Sung, Y. T., & Lin, S. Y. (2007). Developing geometry thinking through multimedia learning activities. Computers in Human Behavior, 23(5), 2212–2229.

    Article  Google Scholar 

  • Chen, Y. L., Hong, Y. R., Sung, Y. T., & Chang, K. E. (2011). Efficacy of simulation-based learning of electronics using visualization and manipulation. Educational Technology & Society, 14(2), 269–277.

    Google Scholar 

  • Chen, Y. L., Pan, P. R., Sung, Y. T., & Chang, K. E. (2013). Correcting misconceptions on electronics: Effects of a simulation-based learning environment backed by a conceptual change model. Educational Technology & Society, 16(2), 212–227.

    Google Scholar 

  • Christou, C., Jones, K., Pitta-Pantazi, D., Pittalis, M., Mousoulides, N., Matos, J. F., Sendova, E., Zachariades, T., & Boytchev, P. (2007, February). Developing student spatial ability with 3D software applications. Paper presented at the 5th congress of the European Society for Research in Mathematics Education (CERME), Larnaca.

  • Clements, D. H. (1999). ‘Concrete’ manipulatives, concrete ideas. Contemporary Issues in Early Childhood, 1(1), 45–60.

    Article  Google Scholar 

  • Clements, D. H., & Battista, M. T. (1992). Geometry and spatial reasoning. In D. A. Grouws (Ed.), Handbook of research on mathematics teaching and learning: A project of the National Council of Teachers of Mathematics (pp. 420–464). New York: Macmillan Publishing Co Inc.

    Google Scholar 

  • Clements, D. H., & Stephan, M. (2003). Measurement in pre-k to grade 2 mathematics. Mathematics: Standards for pre-school and kindergarten mathematics education (pp. 299–317). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Cromby, J. J., Standen, P. J., & Brown, D. J. (1996). The potentials of virtual environments in the education and training of people with learning disabilities. Journal of Intellectual Disability Research, 40(6), 489–501.

    Article  Google Scholar 

  • Department of Education, Keelung City. (2008). The construction of the Elementary School Mathematics Competency Test (ESMCT). Technical Report. Keelung City: Department of Education.

  • Drabowicz, T. (2014). Gender and digital usage inequality among adolescents: A comparative study of 39 countries. Computers & Education, 74(1), 98–111.

    Article  Google Scholar 

  • Eiriksdottir, E., & Catrambone, R. (2009). Procedural instructions, principles, and examples: How to structure instructions for procedural tasks to enhance performance, learning, and transfer. Human Factors, 53(6), 749–776.

    Article  Google Scholar 

  • Fennema, E., & Sherman, J. (1976). Fennema–Sherman mathematics attitudes scales: Instruments designed to measure attitudes toward the learning of mathematics by females and males. Journal for Research in Mathematics Education, 7(5), 324–326.

    Article  Google Scholar 

  • French, D. (2004). Teaching and learning geometry: Issues and methods in mathematical education. New York: Continuum.

    Google Scholar 

  • Friedman, L. (1989). Mathematics and the gender gap: A meta-analysis of recent studies on sex differences in mathematical tasks. Review of Educational Research, 59(2), 185–213.

    Google Scholar 

  • Garg, A. X., Geoffery, R. N., Eva, K. W., Spero, L., & Sharan, S. (2002). Is there any real virtue of virtual reality? The minor role of multiple orientations in learning anatomy from computers. Academic Medicine, 77(10), 97–99.

    Article  Google Scholar 

  • Geary, D. C., & DeSoto, M. C. (2001). Sex differences in spatial abilities among adults from the United States and China: Implications for evolutionary theory. Evolution and Cognition, 7(2), 172–177.

    Google Scholar 

  • Huk, T. (2006). Who benefits from learning with 3D models? The case of spatial ability. Journal of Computer Assisted Learning, 22(6), 392–404.

    Article  Google Scholar 

  • Huk, T., Steinke, M., & Floto, C. (2003). The influence of visual spatial ability on the attitude of users towards high-quality 3D-animations in hypermedia learning environments. In A. Rossett (Ed.), World conference on e-learning in corporate, government, healthcare, and higher education (pp. 1038–1041). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE).

    Google Scholar 

  • Korakakis, G., Pavlatou, J. A., Palyvos, N., & Spyrellis, N. (2009). 3D visualization types in multimedia applications for science learning: A case study for 8th grade students in Greece. Computers & Education, 52(2), 390–401.

    Article  Google Scholar 

  • Lannen, T., Brown, D., & Powell, H. (2002). Control of virtual environments for young people with learning difficulties. Disability and Rehabilitation, 24(11–12), 578–586.

    Article  Google Scholar 

  • Li, Q., & Ma, X. (2010). A meta-analysis of the effects of computer technology on school students’ mathematics learning. Educational Psychology Review, 22(3), 215–243.

    Article  Google Scholar 

  • Lindberg, S. M., Hyde, J. S., Petersen, J. L., & Linn, M. C. (2010). New trends in gender and mathematics performance: A meta-analysis. Psychological Bulletin, 136(6), 1123–1135.

    Article  Google Scholar 

  • Ma, X. (1997). Reciprocal relationships between attitude toward mathematics and achievement in mathematics. Journal of Educational Research, 90(4), 221–229.

    Article  Google Scholar 

  • Ma, X., & Xu, J. (2004). Determining the causal ordering between attitude toward mathematics and achievement in mathematics. American Journal of Education, 110(3), 256–280.

    Article  Google Scholar 

  • Maria, D., & Garcia, M. (2009). The influence of new technologies on learning and attitudes in mathematics in secondary students. Electronic Journal of Research in Educational Psychology, 7(17), 369–396.

    Google Scholar 

  • Mayer, R. E. (1987). Educational psychology: A cognitive approach. Boston: Little, Brown and Company.

    Google Scholar 

  • Mayer-Smith, J., Pedretti, E., & Woodrow, J. (2000). Closing of the gender gap in technology enriched science education: A case study. Computers & Education, 35(1), 51–63.

    Article  Google Scholar 

  • Moyer, P. S., Bolyard, J. J., & Spikell, M. A. (2002). What are virtual manipulatives? Teaching Children Mathematics, 8(6), 372–377.

    Google Scholar 

  • Moyer, P. S., Salkind, G., & Bolyard, J. J. (2008). Virtual manipulatives used by K-8 teachers for mathematics instruction: Considering mathematical, cognitive, and pedagogical fidelity. Contemporary Issues in Technology and Teacher Education, 8(3), 202–218.

    Google Scholar 

  • Olkun, A., Altun, S., & Smith, G. (2005). Computers and 2D geometric learning of Turkish fourth and fifth graders. British Journal of Educational Technology, 36(2), 317–326.

    Article  Google Scholar 

  • Pantelidis, V. (1995). Reasons to use virtual reality in education. VR in the Schools, 1(1), 9.

    Google Scholar 

  • Pape, S. J., & Tchoshanov, M. A. (2001). The role of multiple representation(s) in developing mathematical understanding. Theory into Practice, 40(2), 118–127.

    Article  Google Scholar 

  • Piaget, J., & Inhelder, B. (1956). The child’s conception of space. London: Routledge & Kegan Paul.

    Google Scholar 

  • Piaget, J., Inhelder, B., & Szeminska, A. (1960). The child’s conception of geometry. New York: Basic Books.

    Google Scholar 

  • Plumm, K. M. (2008). Technology in the classroom: Burning the bridges to the gaps in gender-biased education? Computers & Education, 50(3), 1052–1068.

    Article  Google Scholar 

  • Rafi, A., Samsudin, K. A., & Said, C. S. (2008). Training in spatial visualization: The effects of training method and gender. Educational Technology & Society, 11(3), 127–140.

    Google Scholar 

  • Reimer, K., & Moyer, P. (2005). Third-graders learn about fractions using virtual manipulatives: A classroom study. The Journal of Computers in Mathematics and Science Teaching, 24(1), 5–25.

    Google Scholar 

  • Reinen, I. J., & Plomp, T. (1997). Information technology and gender equality: A contradiction in terminis? Computers & Education, 28(2), 65–78.

    Article  Google Scholar 

  • Reyes, L. H. (1984). Affective variables and mathematics education. The Elementary School Journal, 84(5), 558–581.

    Article  Google Scholar 

  • Rutherford, A. (2011). ANOVA and ANCOVA: AGLM approach. Hoboken, NJ: Wiley.

    Book  Google Scholar 

  • Steen, K., Brooks, D., & Lyon, T. (2006). The impact of virtual manipulatives on first grade geometry instruction and learning. Journal of Computers in Mathematics and Science Teaching, 25(4), 373–391.

    Google Scholar 

  • Van Hiele, P. M. (1986). Structure and insight: A theory and mathematics education. Orlando, FL: Academic Press.

    Google Scholar 

  • Vekiri, I., & Chronaki, A. (2008). Gender issues in technology use: Perceived social support, computer self-efficacy and value beliefs, and computer use beyond school. Computers & Education, 51(3), 1392–1404.

    Article  Google Scholar 

  • Volman, M., & van Eck, E. (2001). Gender equity and information technology in education: The second decade. Review of Educational Research, 71(4), 613–634.

    Article  Google Scholar 

  • Yerushalmy, M. (1991). Student perceptions of algebraic function using multiple representation software. Journal of Computer Assisted Learning, 7(1), 42–57.

    Article  Google Scholar 

Download references

Acknowledgments

The author appreciate the funding supports from the Ministry of Science and Technology, Taiwan (102-2911-I-003-301; 102-2511-S-003-001-MY3; 101-2511-S-003-058-MY3; 101-2511-S-003-047-MY3; 99-2631-S-003-003).

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Correspondence to Yao-Ting Sung.

Appendices

Appendix A: The procedures, strategies, and SketchUp functions used in the SAILS program

Procedure 1

Establishing a link between 2D and 3D spatial structure representations. Basic solids (e.g., a cuboid) can be illustrated in a 2D plane. However, clues regarding the top, side, and perspective views are lacking, and may thus prevent children from understanding the conversion between plane figures in 2D space and solid objects in 3D space (Battista 1999; Piaget and Inhelder 1956; Piaget et al. 1960). This also increases the difficulty of transforming 3D images into actual 3D mental models. Therefore, the first essential task is to help students convert 2D graphical representations into 3D mental representations.

There are two instructional strategies in the first procedure. The first, Strategy 1A, involves the visualization of 3D models. Here learners are guided to build a 3D object (Fig. A1) based on 2D or physical object images by using the modeling function of Google SketchUp. They could also employ three colors to represent the three-axis planes in order to identify the sides/faces of the cubes. Strategy 1B includes the coordination and structuring of 2D and 3D features. Using functions such as dragging and rotating, hiding planes (Fig. A2), and X-ray (Fig. A3), learners can manipulate the 3D model and perform tasks such as rotation, displaying side and top views, and hiding axis planes. In this way learners establish the correspondence between points, line segments, and side relationships for both 3D and 2D physical objects.

Procedure 2

Decomposing, calculating, and recomposing the area of each side of the basic solid. Determining the surface area of a basic solid involves the calculation of multiple areas. Therefore, children must first decompose the sides/faces of the basic solids and grasp how many different sides/faces there are as well as their differences and similarities. They should then calculate the area of each side/face using Battista’s enumeration method or by applying a general formula, and then sum (re-compose) the areas of multiple sides to arrive at a surface-area value.

There are four instructional strategies in the second procedure. Strategy 2A is unfolding objects and counting the number of sides. Using the frame function options, 3D images can be converted to show top, bottom, front, back, left, right, and isometric views, enabling learners to understand the views of each side of the cube. By unfolding the basic solids, learners can determine the number of sides and side shapes for these solids, as well as the symmetric or corresponding sides (Fig. A4). Strategy 2B is enumeration and calculation. Learners first identify the corresponding or symmetric sides and conduct enumeration or use formulae to calculate the area (Fig. A4). Strategy 2C is recomposing and summing the areas of all sides/faces. Students sum the total surface areas of the six sides of the cube. Finally, strategy 2D is the verification of the calculation results. Learners compared the results they obtained with those provided by Google SketchUp through the calculation functions.

Procedure 3

Restructuring the basic solid into a composite solid to establish a link between basic- and composite-solid representations. Composite solids can be considered as composites of basic solids (Figs. A5, A6, A7), which means that composite solids are deformations resulting from the addition or subtraction of basic solids. However, the similarities and differences between mental models for basic and composite solids can only be understood through unit filling, shifting and conservation.

There are two instructional strategies in the third procedure. The first, strategy 3A, is unitization by cubes. Using unit cubes (in which each side equaled 1 in length, see Fig. A5), basic cuboids are decomposed as composites of unit cubes (Fig. A6). Using the coloring function, learners color the corresponding three pairs of sides red, yellow, and blue. Using the virtual building blocks function, learners form a basic cuboid by copying and pasting unit cubes. Strategy 3B is re-organizing and re-locating. Cube units are added to or subtracted from the basic solid to produce composite solid (Fig. A7). The relationship between the location of the basic and composite solids is also identified.

Procedure 4

Decomposing, calculating, and recomposing the surface area of each side of the composite solid. Similar to Procedure 2, in which the surface of a basic solid should be decomposed into several sides, and then recomposed into the surface area of each side, composite solids must first be decomposed and then converted into a composite of one or more basic-solid to facilitate the calculation of each basic solid surface areas. After the surface area of each piece is calculated, the values are summed to obtain the surface area of the composite solid. In addition to possessing the ability to complete this step, children must also know not to repeat the calculation of shifted or filled-in surface areas.

There are four instructional strategies in the fourth procedure. Strategy 4A is identifying the location and number of the sides/faces. The number of sides of the composite solid is verified by observing the rotational, top, side, and perspective views. The similarities and differences between the shapes of the sides, as well as their relationships, are also verified. Strategy 4B is calculating the surface area of basic-cuboid sides by filling, shifting and integrating. The filling function of Google SketchUp is employed to fill up the holes in the concave side of a concave object and deform the concave into a conglomerate of basic solids (like Fig. A6); the shifting function is employed to remove the protrusion in the convex side of a convex object and dismantle the convex into basic solids (like Figs. A6, A7). The processes and end-results of filling and shifting will form dynamic images which are helpful for students’ understanding of the differences and similarities of the original and deformed solids and their corresponding surfaces. The total surface area of the basic solid is subsequently calculated. For example, after the unit cube in Fig. A7 was shifted, the surface area of the yellow side in Fig. A8 comprised 3 × 3 = 9 units of area.

Strategy 4C is calculating the surface area of non-basic-cuboid sides. In addition to the basic-cuboid sides, the surface areas of the concave or convex sides are calculated. For example, in addition to the top-view of Fig. A6, both the red (Fig. A9) and green (Fig. A10) sides comprised (2 × 3) + 1 = 7 units of area. Strategy 4D is recomposing and summing the surface areas of all sides: The total surface area of the composite solid can be obtained by recomposing the surface area of the basic solid and that of the concave/convex composite. For example, the areas of the three colors in Fig. A7 can be summed as (9 + 7 + 7) + (9 + 7 + 7) = 46. After the calculation, learners verify the results they obtained with those provided by Google SketchUP through the calculation function.

See Table 6.

Table 6 Figures of Google SketchUP functions

Appendix B

See Table 7.

Table 7 Exemplar items of the composite-solid surface-area achievement test

Appendix C

See Table 8.

Table 8 Summary table of the four-factor mixed design ANCOVA for the CSSAAT

Appendix D

See Table 9.

Table 9 Summary table of the four-factor mixed design ANOVA for the MaLAS

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Sung, YT., Shih, PC. & Chang, KE. The effects of 3D-representation instruction on composite-solid surface-area learning for elementary school students. Instr Sci 43, 115–145 (2015). https://doi.org/10.1007/s11251-014-9331-8

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