PL EN


Preferences help
enabled [disable] Abstract
Number of results
2018 | 101 | 65-76
Article title

Application of accelerated learning method to improve the ability of students' mathematical representation

Content
Title variants
Languages of publication
EN
Abstracts
EN
One of the mathematical skills that must be possessed by students is the ability of mathematical representation, therefore the applied learning method should be able to facilitate the students to learn to represent the mathematical ability they have. The purpose of this study to determine the improvement of students' mathematical representation in mathematics learning using accelerated learning method. The research method used is Quasi Experiments with Nonequivalent (Pretest and Posttest) design of Control Group Design. The population in this study is the students of grade X in Pasundan Majalaya Senior High School by using Purposive Sampling technique. The samples involved in the study were 48 students of Pasundan Majalaya Senior High School, 24 students came from class X MIPA 1 (experimental class) and 24 students came from class X MIPA 3 (control class). The learning method is applied to the students of class X MIPA 1 is the method of accelerated learning whereas in the students of class X MIPA 3 is conventional methods. The result of mathematical representation of students 'mathematical representation shows that there is an improvement of ability for students' mathematical representation with gain value of 0.56 with middle criterion for class X MIPA 1 and 0.17 with lower criterion for class X MIPA 3.
Year
Volume
101
Pages
65-76
Physical description
References
  • [1] Aflich Y. F., Sofie D., Mayasari, Astri Y. N., Mathematical Representation Ability of Senior High School Students: An Evaluation from Students’ Mathematical Disposition. Journal of Research and Advances in Mathematics Education, 3 (1) (2018), p. 46-56.
  • [2] Ani M., Elvis N. & Rahmad H., Mathematical Understanding and Representation Ability of Public Junior High School In North Sumatra. Journal of Mathematics Education, 7 (1) (2016), p. 43-56.
  • [3] Butsic V., Lewis D. J., Radeloff V. C., Baumann M. & Kuemmerle T., Quasi-Experimental Methods Enable Stronger Inferences from Observational Data in Ecology. Basic and Applied Ecology, 19 (2017), p. 1-10.
  • [4] Colin R., Accelerated Learning for The 21st Century. Bandung: Nuansa (2012).
  • [5] David E. M., The Relationship between Mathematics Preparation and Conceptual Learning Gains in Physics: A Possible ‘‘Hidden Variable’’ in Diagnostic Pretest Scores. Am. J. Phys, 70 (12) (2002), p.1259-1268.
  • [6] David R. & Dellwo, Course Assessment Using Multi-Stage Pre/Post Testing and The Components of Normalized Change. Journal of the scholarship of teaching and learning, 10 (1) (2010), p. 55-67.
  • [7] Dwi R., Purwanto, Subanji, Erry H., Rahmad B. A., Process of Mathematical Representation Translation from Verbal into Graphic. IEJME—Mathematics Education, 12 (4) (2017), p. 367-381
  • [8] Eames M., Luttman S. & Parker S., Accelerated Vs. Traditional Accounting Education and CPA Exam Performance. Journal of Accounting Education, Available online 30 April 2018.
  • [9] Edgar T. W. & Manz D. O., Quasi-Experimental Research. Research Methods for Cyber Security, (2017), p. 251–268.
  • [10] Edith D., Visual Representations in Mathematics Teaching: An Experiment With Students. Acta Didactica Napocensia, 8(1) (2015), p. 21-26
  • [11] Grady M., Students’ Conceptions of Mathematics as Sensible: Towards The SCOMAS Framework. The Journal of Mathematical Behavior, 50 (2018), p. 126-141.
  • [12] Hafiziani E. P., The Influence of Concrete Pictorial Abstract (CPA) Approach to The Mathematical Representation Ability Achievement of The Preservice Teachers at Elementary School. International Journal of Education and Research, 3 (6) (2015), p.113-125.
  • [13] Huang J., The Evolutionary Perspective of Knowledge Creation – A Mathematical Representation. Knowledge-Based Systems, 22 (6) (2009), p. 430-438.
  • [14] Ilker e., Sulaiman A. M., Rukayya S. A., Comparison of Convenience Sampling and Purposive Sampling. American Journal of Theoretical and Applied Statistics, 5 (1) (2016), p. 1-4.
  • [15] Jhon S. & Gay S., Correcting the Normalized Gain for Guessing. The Physics Teacher, 48 (2010), p. 194-196.
  • [16] Juariah, Enhancing Student's Ability and Student's Mathematical Communication Skills through Mathematical Process Skills. (Thesis). UPI Bandung Graduate School: Not published (2008).
  • [17] Karman L. N., Yaya S. K., The Effectiveness Ofict-Assisted Project-Based Learning In Enhancing Students’ Statistical Communication Ability. International Journal of Education and Research, 3 (2) (2015), p. 187-196
  • [18] Lei B., Theoretical Comparisons of Average Normalized Gain Calculations. Am. J. Phys. 74 (10) (2006).
  • [19] Liu B., Xiao Y. & Hao Z., A Selective Multiple Instance Transfer Learning Method for Text Categorization Problems. Knowledge-Based Systems, 141 (2018), p. 178-187.
  • [20] Mohamad N. & Widodo W., the Relationship between Student Perceptions towards Mathematics Teacher with Mathematics Academic Performance. Journal of Psychology, 15 (2) (2016), p. 139-146.
  • [21] Muchamad S. N., Wahyu H., Mohammad D. S., Analysis of Students Mathematical Representation and Connection on Analytical Geometry Subject. Journal of Mathematics Education, 5 (2) (2016), p. 99-108
  • [22] NCTM, Principles and standards for school mathematics. USA: NCTM (2000).
  • [23] Sarah I., Improved Problem Solving Ability and Multiple Mathematical Representation And Self-Esteem of Junior High School Students Using Quantum Learning Models. (Thesis). UPI Bandung Graduate School: Unpublished (2013).
  • [24] Suprapto, Co-operative Learning Think Pair Square to Improve Student Motivation and Learning Outcomes of Mathematics Grade VI-1 SMP Negeri 5 Penajam. Postgraduate of State University of Malang: Unpublished (2013). [November 15, 2015]
  • [25] Syarifah F., Increasing the Ability of Mathematical Representation, Mathematical Problem Solving, and Self Esteem of Junior High School Students through Learning with Open Ended Approach. (Dissertation). UPI Bandung Graduate School: Unpublished (2010).
  • [26] Vincent P. C., Jeffrey A. P. & Jeffrey J. S., Interpreting FCI scores: normalized gain, pre instruction scores, and scientific reasoning ability. Am. J. Phys., 73 (12) (2005).
  • [27] William E. A. & Melissa Y. H., The objective minimization function for the mathematical representation of solubility data for solutes dissolved in binary solvent mixtures. The Journal of Chemical Thermodynamics, 104 (2017), p. 61-66.
  • [28] Yennello S.J., Accelerated Learning: Undergraduate Research Experiences at the Texas A&M Cyclotron Institute. Physics Procedia, 90 (2017), p. 354-363.
  • [29] Yumiati, Mery N., Analysis of Mathematic Representation Ability of Junior High School Students in The Implementation of Guided Inquiry Learning. Journal of Mathematics Education, 6 (2) (2017), p. 137-148.
  • [30] Tomas U., Ganiron J., Application of Accelerated Learning in Teaching Environmental Control System in Qassim University. International Journal of Education and Learning, 2 (2) (2013), p. 27-38.
Document Type
article
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.psjd-4dfd163f-90d9-4e10-afb0-5748e9410c5e
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.