Submit a Manuscript

International Electronic Journal of Mathematics Education

Increase Representation in Mathematics Classes: Effects of Computer Assisted Instruction Development with Hippo Animator
Uba Umbara 1 2 * , Munir 3 * , R. Susilana 4 * , Evan Farhan Wahyu Puadi 1 *
1 Department of Mathematics Education, STKIP Muhammadiyah Kuningan, Kuningan, Indonesia2 School of Postgraduate Studies, Universitas Pendidikan Indonesia, Bandung, Indonesia3 Department of Computer Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia4 Department of Curriculum & Educational Technology, Universitas Pendidikan Indonesia, Bandung, Indonesia
* Corresponding Author
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Umbara U, Munir, Susilana R, Puadi EFW. Increase Representation in Mathematics Classes: Effects of Computer Assisted Instruction Development with Hippo Animator. INT ELECT J MATH ED. 2020;15(2), em0567. https://doi.org/10.29333/iejme/6262
APA 6th edition
In-text citation: (Umbara et al., 2020)
Reference: Umbara, U., Munir, Susilana, R., & Puadi, E. F. W. (2020). Increase Representation in Mathematics Classes: Effects of Computer Assisted Instruction Development with Hippo Animator. International Electronic Journal of Mathematics Education, 15(2), em0567. https://doi.org/10.29333/iejme/6262
Chicago
In-text citation: (Umbara et al., 2020)
Reference: Umbara, Uba, Munir, R. Susilana, and Evan Farhan Wahyu Puadi. "Increase Representation in Mathematics Classes: Effects of Computer Assisted Instruction Development with Hippo Animator". International Electronic Journal of Mathematics Education 2020 15 no. 2 (2020): em0567. https://doi.org/10.29333/iejme/6262
Harvard
In-text citation: (Umbara et al., 2020)
Reference: Umbara, U., Munir, Susilana, R., and Puadi, E. F. W. (2020). Increase Representation in Mathematics Classes: Effects of Computer Assisted Instruction Development with Hippo Animator. International Electronic Journal of Mathematics Education, 15(2), em0567. https://doi.org/10.29333/iejme/6262
MLA
In-text citation: (Umbara et al., 2020)
Reference: Umbara, Uba et al. "Increase Representation in Mathematics Classes: Effects of Computer Assisted Instruction Development with Hippo Animator". International Electronic Journal of Mathematics Education, vol. 15, no. 2, 2020, em0567. https://doi.org/10.29333/iejme/6262
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Umbara U, Munir, Susilana R, Puadi EFW. Increase Representation in Mathematics Classes: Effects of Computer Assisted Instruction Development with Hippo Animator. INT ELECT J MATH ED. 2020;15(2):em0567. https://doi.org/10.29333/iejme/6262

Abstract

This study is about developing and testing the effectiveness of Computer Assisted Instruction (CAI) through multimedia in mathematical representations. Features that are developed using the hippo animator provides learning instructions with the tutorial model. The principle of self-regulated learning and mastery learning are both the strategy in learning to ensure students to be able to learn to be self-sufficient and complete. The principles of reliability, usability, maintainability, compatibility, reliability, reliability, interactive and communicative are carried out to assess multimedia well designed in terms of interfaces to maximize mathematical representation. For these purposes, R & D was selected in this study with structured and systematic stages through strict quality control. The results of the study show that multimedia developed is feasible to be produced and used in mathematics learning. Finally, the use of CAI through multimedia plays an important role in facilitating mathematical learning, especially in improving mathematical representation. However, the significance of differences in improving students’ mathematical skill in representation ability is not found.

Keywords

mathematical representation computer assisted instruction multimedia hippo animator

References

  • Abdullah, N., Zakaria, E., & Halim, L. (2012). The effect of a thinking strategy approach through visual representation on achievement and conceptual understanding in solving mathematical word problems. Asian Social Science, 8(16), 30. https://doi.org/10.5539/ass.v8n16p30
  • Abrahamson, D. (2006). Mathematical representations as conceptual composites: Implications for design. In Proceedings of the Twenty Eighth Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (Vol. 2, pp. 464-466).
  • Adu-Gyamfi, K., Stiff, L. V, & Bossé, M. J. (2012). Lost in translation: Examining translation errors associated with mathematical representations. School Science and Mathematics, 112(3), 159-170. https://doi.org/10.1111/j.1949-8594.2011.00129.x
  • Afriyani, D., Sa’dijah, C., Subanji, S., & Muksar, M. (2018). Characteristics of Students’ Mathematical Understanding in Solving Multiple Representation Task based on Solo Taxonomy. International Electronic Journal of Mathematics Education, 13(3), 281-287. https://doi.org/10.12973/iejme/3920
  • Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183-198. https://doi.org/10.1016/j.learninstruc.2006.03.001
  • Andrà, C., Arzarello, F., Ferrara, F., Holmqvist, K., Lindström, P., Robutti, O., & Sabena, C. (2009). How students read mathematical representations: An eye tracking study. In Proceedings of the 33rd Conference of the International Group for the Psychology of Mathematics Education (Vol. 2, pp. 49-56).
  • Anohina, A. (2005). Analysis of the terminology used in the field of virtual learning. Journal of Educational Technology & Society, 8(3), 91-102.
  • Bal, A. P. (2015). Skills of using and transform multiple representations of the prospective teachers. Procedia-Social and Behavioral Sciences, 197, 582-588. https://doi.org/10.1016/j.sbspro.2015.07.197
  • Bernard-Opitz, V., Sriram, N., & Nakhoda-Sapuan, S. (2001). Enhancing social problem solving in children with autism and normal children through computer-assisted instruction. Journal of Autism and Developmental Disorders, 31(4), 377-384. https://doi.org/10.1023/A:1010660502130
  • Björklund, C. (2010). Broadening the horizon: Toddlers’ strategies for learning mathematics. International Journal of Early Years Education, 18(1), 71-84. https://doi.org/10.1080/09669761003661246
  • Blok, H., Oostdam, R., Otter, M. E., & Overmaat, M. (2002). Computer-assisted instruction in support of beginning reading instruction: A review. Review of Educational Research, 72(1), 101-130. https://doi.org/10.3102/00346543072001101
  • Brown, F. (2000). Computer Assisted Instruction in Mathematics Can Improve Students’ Test Scores: A Study.
  • Bruner, J. S. (1966). Toward a theory of instruction (Vol. 59). Harvard University Press.
  • Cai, J., & Lester Jr, F. K. (2005). Solution representations and pedagogical representations in Chinese and US classrooms. The Journal of Mathematical Behavior, 24(3-4), 221-237. https://doi.org/10.1016/j.jmathb.2005.09.003
  • Carbonell, J. R. (1970). AI in CAI: An artificial-intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems, 11(4), 190-202. https://doi.org/10.1109/TMMS.1970.299942
  • Carreira, S. (2015). Mathematical problem solving beyond school: Digital tools and students’ mathematical representations. In Selected regular lectures from the 12th international congress on mathematical education (pp. 93-113). https://doi.org/10.1007/978-3-319-17187-6_6
  • Cathcart, W. G., Pothier, Y. M., Vance, J. H., & Bezuk, N. S. (2000). Learning mathematics in elementary and middle schools. Prentice Hall Allyn and Bacon.
  • 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. https://doi.org/10.1016/j.compedu.2008.01.007
  • Chen, M.-J., Lee, C.-Y., & Hsu, W.-C. (2015). Influence of mathematical representation and mathematics self-efficacy on the learning effectiveness of fifth graders in pattern reasoning. International Journal of Learning, Teaching and Educational Research, 13(1).
  • Cuban, L. (2009). Oversold and underused. Harvard university press. https://doi.org/10.2307/j.ctvk12qnw
  • De Witte, K., Haelermans, C., & Rogge, N. (2015). The effectiveness of a computer-assisted math learning program. Journal of Computer Assisted Learning, 31(4), 314-329. https://doi.org/10.1111/jcal.12090
  • Dick, W., Carey, L., & Carey, J. O. (2015). The systematic design of instruction. 8th. United States Of.
  • Gagatsis, A., & Elia, I. (2004). The Effects of Different Modes of Representation on Mathematical Problem Solving. International Group for the Psychology of Mathematics Education.
  • Gagatsis, A., & Shiakalli, M. (2004). Ability to translate from one representation of the concept of function to another and mathematical problem solving. Educational Psychology, 24(5), 645-657. https://doi.org/10.1080/0144341042000262953
  • Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: an introduction (8. utg.). Boston: Pearson/Allyn and Bacon.
  • Goldin, G. A. (1998). Representational systems, learning, and problem solving in mathematics. The Journal of Mathematical Behavior, 17(2), 137-165. https://doi.org/10.1016/S0364-0213(99)80056-1
  • Goldin, G. A., & Kaput, J. J. (1996). A joint perspective on the idea of representation in learning and doing mathematics. Theories of Mathematical Learning, 397.
  • Haidar, N., Obeed, S., & Jawad, M. (2011). Mathematical representation of bolted-joint stiffness: A new suggested model. Journal of Mechanical Science and Technology, 25(11), 2827-2834. https://doi.org/10.1007/s12206-011-0725-0
  • Hake, R. R., & others. (1999). Analyzing change/gain scores (Unpublished) [Online] Retrieved from http://www.Physics.Indiana.Edu/~Sdi/AnalyzingChange-Gain.Pdf
  • Janvier, C. E. (1987). Problems of representation in the teaching and learning of mathematics. In This book stems from a symposium organized by CIRADE (Centre Interdisciplinaire de Recherche sur l” Apprentissage et le Développement en Education) of Université du Que1bec abec abec abec abec abec abec abec abec abec abec abec abec abec abec abec à Montr.
  • Kaput, J. J. (1987). Representation systems and mathematics. Problems of Representation in the Teaching and Learning of Mathematics, 19, 26.
  • Killpatrick, J., Swafford, J., & Findell, B. (2001). Adding it up: Helping children learn mathematics. Washington, DC: National Academy Press.
  • Lesh, R., Landau, M., & Hamilton, E. (1983). Conceptual models and applied mathematical problem-solving research. Acquisition of Mathematics Concepts and Processes, 263-343.
  • Lesh, R., Post, T. R., & Behr, M. (1987). Representations and translations among representations in mathematics learning and problem solving. In Problems of representations in the teaching and learning of mathematics. Lawrence Erlbaum.
  • Macaruso, P., & Rodman, A. (2011). Efficacy of computer-assisted instruction for the development of early literacy skills in young children. Reading Psychology, 32(2), 172-196. https://doi.org/10.1080/02702711003608071
  • Macaruso, P., & Walker, A. (2008). The efficacy of computer-assisted instruction for advancing literacy skills in kindergarten children. Reading Psychology, 29(3), 266-287. https://doi.org/10.1080/02702710801982019
  • McCoy, L. P., Baker, T. H., & Little, L. S. (1996). Using multiple representations to communicate: An algebra challenge. Communication in Mathematics, K-12 and beyond (1996 Yearbook) Reston, Va: NCTM.
  • Minarni, A., & Napitupulu, E. E. (2017). Developing Instruction Materials Based on Joyful PBL to Improve Students Mathematical Representation Ability. International Education Studies, 10(09), 23-38. https://doi.org/10.5539/ies.v10n9p23
  • Minarni, A., Napitupulu, E., & Husein, R. (2016). Mathematical understanding and representation ability of public junior high school in north sumatra. Journal on Mathematics Education, 7(1), 43-56. https://doi.org/10.22342/jme.7.1.2816.43-56
  • Mišútová, M., & Mišút, M. (2012). Impact of ICT on the Quality of Mathematical Education. In Proceedings of the 6th International Multi-Conference on Society, Cybernetics and Informatics (pp. 76-80).
  • Munir, Kusnendar, J., & Rahmadhani. (2016). Developing an effective multimedia in education for special education (MESE): An introduction to arithmetic. In AIP Conference Proceedings (Vol. 1708, p. 50001). https://doi.org/10.1063/1.4941159
  • Munir, M. (2012). Multimedia konsep & aplikasi dalam pendidikan. Bandung: Alfabeta.
  • NCTM. (2000). Principles and standards for school mathematics (Vol. 1). National Council of Teachers of.
  • Newby, T. J., Stepich, D. A., Russell, J. D., & Lehman, J. D. (2006). Educational technology for teaching and learning. Prentice Hall.
  • Nguyen, T. T., Hui, S. C., & Chang, K. (2012). A lattice-based approach for mathematical search using formal concept analysis. Expert Systems with Applications, 39(5), 5820-5828. https://doi.org/10.1016/j.eswa.2011.11.085
  • Noblesse, F., Delhommeau, G., Huang, F., & Yang, C. (2011). Practical mathematical representation of the flow due to a distribution of sources on a steadily advancing ship hull. Journal of Engineering Mathematics, 71(4), 367-392. https://doi.org/10.1007/s10665-011-9453-9
  • O’Hara, M. (2008). Young children, learning and ICT: A case study in the UK maintained sector. Technology, Pedagogy and Education, 17(1), 29-40. https://doi.org/10.1080/14759390701847443
  • Oktavianingtyas, E., Salama, F. S., Fatahillah, A., Monalisa, L. A., & Setiawan, T. B. (2018). Development 3D Animated Story as Interactive Learning Media with Lectora Inspire and Plotagon on Direct and Inverse Proportion Subject. In Journal of Physics: Conference Series (Vol. 1108, p. 12111). https://doi.org/10.1088/1742-6596/1108/1/012111
  • Pape, S. J. (2004). Middle school children’s problem-solving behavior: A cognitive analysis from a reading comprehension perspective. Journal for Research in Mathematics Education, 187-219. https://doi.org/10.2307/30034912
  • Pape, S. J., & Tchoshanov, M. A. (2001). The role of representation (s) in developing mathematical understanding. Theory into Practice, 40(2), 118-127. https://doi.org/10.1207/s15430421tip4002_6
  • Pennington, R. C. (2010). Computer-assisted instruction for teaching academic skills to students with autism spectrum disorders: A review of literature. Focus on Autism and Other Developmental Disabilities, 25(4), 239-248. https://doi.org/10.1177/1088357610378291
  • Plowman, L., & Stephen, C. (2003). A ‘benign addition’? Research on ICT and pre-school children. Journal of Computer Assisted Learning, 19(2), 149-164. https://doi.org/10.1046/j.0266-4909.2003.00016.x
  • Psycharis, S., & Kallia, M. (2017). The effects of computer programming on high school students’ reasoning skills and mathematical self-efficacy and problem solving. Instructional Science, 45(5), 583-602. https://doi.org/10.1007/s11251-017-9421-5
  • Rahmawati, D., Hidayanto, E., Anwar, R. B., & others. (2017). Process of Mathematical Representation Translation from Verbal into Graphic. International Electronic Journal of Mathematics Education, 12(3), 367-381.
  • Renshaw, C. E., Taylor, H. A., & Reynolds, C. H. (1998). Impact of computer-assisted instruction in hydrogeology on critical-thinking skills. Journal of Geoscience Education, 46(3), 274-279. https://doi.org/10.5408/1089-9995-46.3.274
  • Rice, L. M., Wall, C. A., Fogel, A., & Shic, F. (2015). Computer-assisted face processing instruction improves emotion recognition, mentalizing, and social skills in students with ASD. Journal of Autism and Developmental Disorders, 45(7), 2176-2186. https://doi.org/10.1007/s10803-015-2380-2
  • Richards, C. (2005). The design of effective ICT-supported learning activities: Exemplary models, changing requirements, and new possibilities. Language Learning & Technology, 9(1), 60-79.
  • Rittle-Johnson, B., & Star, J. R. (2007). Does comparing solution methods facilitate conceptual and procedural knowledge? An experimental study on learning to solve equations. Journal of Educational Psychology, 99(3), 561. https://doi.org/10.1037/0022-0663.99.3.561
  • Salkind, G. M., & Hjalmarson, M. (2007). Mathematical representations. George Mason University.
  • Schoenfeld, A. H. (2013). Cognitive science and mathematics education. Routledge.
  • Seo, Y.-J., & Woo, H. (2010). The identification, implementation, and evaluation of critical user interface design features of computer-assisted instruction programs in mathematics for students with learning disabilities. Computers & Education, 55(1), 363-377. https://doi.org/10.1016/j.compedu.2010.02.002
  • Shute, R., & Miksad, J. (1997). Computer assisted instruction and cognitive development in preschoolers. Child Study Journal, 27(3), 237-253.
  • Supandi, S., Waluya, S. B., Rochmad, R., Suyitno, H., & Dewi, K. (2018). Think-Talk-Write Model for Improving Students’ Abilities in Mathematical Representation. International Journal of Instruction, 11(3), 77-90. https://doi.org/10.12973/iji.2018.1136a
  • Surya, E., Sabandar, J., Kusumah, Y. S., & Darhim, D. (2013). Improving of junior high school visual thinking representation ability in mathematical problem solving by CTL. Journal on Mathematics Education, 4(1), 113-126. https://doi.org/10.22342/jme.4.1.568.113-126
  • Tandiling, E. (2015). Effectivity of Problem Based Learning (PBL) in improving students’ mathematical representation. In Proceeding of International Conference on Research, Implementation, and Education of Mathematics and Sciences (p. 151).
  • Thomas, L., & Knezek, D. (2002). Standards for technology-supported learning environments. The State Education Standard, 3(3), 14-20.
  • Tienken, C. H., & Wilson, M. J. (2007). The Impact of Computer Assisted Instruction on Seventh-Grade Students’ Mathematics Achievement. Planning and Changing, 38, 181-190.
  • Van Scoyoc, A. M. (2003). Reducing library anxiety in first-year students: The impact of computer-assisted instruction and bibliographic instruction. Reference & User Services Quarterly, 329-341.
  • Warsito, Darhim, & Herman, T. (2018). Improving students’ mathematical representational ability through RME-based progressive mathematization. In Journal of Physics: Conference Series (Vol. 948, p. 12038). https://doi.org/10.1088/1742-6596/948/1/012038
  • Widakdo, W. A. (2017). Mathematical Representation Ability by Using Project Based Learning on the Topic of Statistics. In Journal of Physics: Conference Series (Vol. 895, p. 12055). https://doi.org/10.1088/1742-6596/895/1/012055
  • Wieczorek, M., & Lewandowski, M. (2017). A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm. Applied Energy, 192, 222-233. https://doi.org/10.1016/j.apenergy.2017.02.022
  • Willis, G. B., & Fuson, K. C. (1988). Teaching children to use schematic drawings to solve addition and subtraction word problems. Journal of Educational Psychology, 80(2), 192. https://doi.org/10.1037/0022-0663.80.2.192
  • Yerushalmy, M. (1997). Designing representations: Reasoning about functions of two variables. Journal for Research in Mathematics Education, 28(4), 431. https://doi.org/10.2307/749682
  • Zaitseva, E., & Levashenko, V. (2013). Multiple-valued logic mathematical approaches for multi-state system reliability analysis. Journal of Applied Logic, 11(3), 350-362. https://doi.org/10.1016/j.jal.2013.05.005
  • Zhang, L., Watson, E. M., & Banfield, L. (2007). The efficacy of computer-assisted instruction versus face-to-face instruction in academic libraries: a systematic review. The Journal of Academic Librarianship, 33(4), 478-484. https://doi.org/10.1016/j.acalib.2007.03.006

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
All Rights Reserved © 2006-2020