Research Article
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Year 2023, Volume: 10 Issue: 6, 84 - 103, 01.11.2023
https://doi.org/10.17275/per.23.90.10.6

Abstract

References

  • Abramovich, S., Grinshpan, A. Z., & Milligan, D. L. (2019). Teaching mathematics through concept motivation and action learning. Education Research International, 2019, 1–13. https://doi.org/10.1155/2019/3745406
  • Angraini, L. M. (2021). Didactical design of mathematical reasoning in mathematical basic concepts of courses. JNPM (Jurnal Nasional Pendidikan Matematika), 5(1), 1. https://doi.org/10.33603/jnpm.v5i1.3943
  • Anwar, L. (2023). Learning trajectory of geometry proof construction: Studying the emerging understanding of the structure of Euclidean proof. Eurasia Journal of Mathematics, Science and Technology Education, 19(5). https://doi.org/10.29333/ejmste/13160
  • Ausubel, D. P. (1962). A subsumption theory of meaningful verbal learning and retention. The Journal of General Psychology, 66(2), 213–224. https://doi.org/10.1080/00221309.1962.9711837
  • Bahamonde, A. D. C., Fortuny Aymemí, J. M., & Gómez I Urgellés, J. V. (2017). Mathematical modelling and the learning trajectory: Tools to support the teaching of linear algebra. International Journal of Mathematical Education in Science and Technology, 48(3), 338–352. https://doi.org/10.1080/0020739X.2016.1241436
  • Bakker, A., & Van Eerde, D. (2015). An introduction to design-based research with an example from statistics education. In A. Bikner-Ahsbahs, C. Knipping, & N. Presmeg (Eds.), Approaches to Qualitative Research in Mathematics Education (pp. 429–466). Springer Netherlands. https://doi.org/10.1007/978-94-017-9181-6_16
  • Baroody, A. J. (2022). Lessons learned from 10 experiments that tested the efficacy and assumptions of hypothetical learning trajectories. Education Sciences, 12(3). https://doi.org/10.3390/educsci12030195
  • Brinberg, D., & McGrath, J. E. (1985). Validity and the research process. In Validity and the Research Process. Sage Publications.
  • Broietti, F. C. D. (2022). Hypothetical learning trajectory and understanding the content of solutions in the teaching of chemistry. Curriculo Sem Fronteiras, 22. https://doi.org/10.35786/1645-1384.v22.1810
  • Busch, E. L. (2023). Multi-view manifold learning of human brain-state trajectories. Nature Computational Science, 3(3), 240–253. https://doi.org/10.1038/s43588-023-00419-0
  • Cazares, S. I. (2019). Design and evaluation of a hypothetical learning trajectory to confidence intervals based on simulation and real data. Bolema - Mathematics Education Bulletin, 33(63), 1–26. https://doi.org/10.1590/1980-4415v33n63a01
  • Chen, Y. H. (2023). Manipulator trajectory optimization using reinforcement learning on a reduced-order dynamic model with deep neural network compensation. Machines, 11(3). https://doi.org/10.3390/machines11030350
  • Cuevas-Vallejo, A. (2023). A learning trajectory for university students regarding the concept of vector. Journal of Mathematical Behavior, 70. https://doi.org/10.1016/j.jmathb.2023.101044
  • Demetriou, A. (2023). A deep learning framework for generation and analysis of driving scenario trajectories. SN Computer Science, 4(3). https://doi.org/10.1007/s42979-023-01714-3
  • Dhuheir, M. A. (2023). Deep reinforcement learning for trajectory path planning and distributed inference in resource-constrained UAV swarms. IEEE Internet of Things Journal, 10(9), 8185–8201. https://doi.org/10.1109/JIOT.2022.3231341
  • Feishi, G., Rongjian, H., & Lingyuan, G. (2017). Theory and development of teaching through variation in mathematics in China. In Theory and Development of Teaching through Variation in Mathematics in China (pp. 13–41). BRILL.
  • Ferreira, P. E. A., & Silva, K. A. P. D. (2019). Modelagem matemática e uma proposta de trajetória hipotética de aprendizagem. Bolema: Boletim de Educação Matemática, 33(65), 1233–1254. https://doi.org/10.1590/1980-4415v33n65a13
  • George, M., & Apter, A. J. (2004). Gaining insight into patients’ beliefs using qualitative research methodologies. Curr Opin Allergy Clin Immunol, 4(3), 185–189.
  • Gravemeijer, K. (1994). Developing realistic mathematics education. CD Beta Press.
  • Guarte, J. M., & Barrios, E. B. (2006). Estimation under purposive sampling. Communications in Statistics - Simulation and Computation, 35(2), 277–284. https://doi.org/10.1080/03610910600591610
  • Haggarty, L. (Ed.). (2002). Aspects of teaching secondary mathematics: Perspectives on practice. RoutledgeFalmer : Open University Press.
  • Huh, J. (2023). Deep learning-based autonomous excavation: a bucket-trajectory planning algorithm. IEEE Access, 11, 38047–38060. https://doi.org/10.1109/ACCESS.2023.3267120
  • Ivars, P. (2018). Enhancing noticing: Using a hypothetical learning trajectory to improve pre-service primary teachers’ professional discourse. Eurasia Journal of Mathematics, Science and Technology Education, 14(11). https://doi.org/10.29333/ejmste/93421
  • Kaitera, S., & Harmoinen, S. (2022). Developing mathematical problem-solving skills in primary school by using visual representations on heuristics. LUMAT: International Journal on Math, Science and Technology Education, 10(2). https://doi.org/10.31129/LUMAT.10.2.1696
  • Kirk, J., & Miller, M. L. (1988). Reliability and validity in qualitative research. International Journal of Qualitative Studies in Education, 1(1).
  • Kolaghassi, R. (2023). Deep learning models for stable gait prediction applied to exoskeleton reference trajectories for children with cerebral palsy. IEEE Access, 11, 31962–31976. https://doi.org/10.1109/ACCESS.2023.3252916
  • Kuncoro, K. S., Zakkia, A., Sulistyowati, F., & Kusumaningrum, B. (2021). Students’ mathematical critical thinking based on self-esteem through problem based learning in geometry. Southeast Asian Mathematics Education Journal, 11(1), 41–52. https://doi.org/10.46517/seamej.v11i1.122
  • Lestari, T. V. D. (2020). Hypothetical learning trajectory and students’ understanding of the concepts of the arithmetic sequence. Journal of Physics: Conference Series, 1581(1). https://doi.org/10.1088/1742-6596/1581/1/012038
  • Lidinillah, D. A. M. (2011). Educational design research: A theoretical framework for action. Universitas Pendidikan Indonesia.
  • Luo, Q. (2023). Deep reinforcement learning based computation offloading and trajectory planning for multi-UAV cooperative target search. IEEE Journal on Selected Areas in Communications, 41(2), 504–520. https://doi.org/10.1109/JSAC.2022.3228558
  • Mansouri, N. (2023). Machine learning of multi-modal tumor imaging reveals trajectories of response to precision treatment. Cancers, 15(6). https://doi.org/10.3390/cancers15061751
  • Mattison, R. E. (2023). Longitudinal trajectories of reading and mathematics achievement for students with learning disabilities. Journal of Learning Disabilities, 56(2), 132–144. https://doi.org/10.1177/00222194221085668
  • Merriam, S. B. (1998). Qualitative research and case study applications in education. Jossey-Bass Publishers.
  • Mutaqin, E. J., Herman, T., Wahyudin, W., & Muslihah, N. N. (2023). Hypothetical learning trajectory in place value concepts in elementary school. Mosharafa: Jurnal Pendidikan Matematika, 12(1), 125–134. https://doi.org/10.31980/mosharafa.v12i1.1313
  • Mаhammadovna, S. I. (2023). Features of cluster design in modern paradigms of education. TELEMATIQUE, 22(1), 348–355.
  • National Council of Teachers of Mathematics (NCTM)). (1989). Curriculum and evaluation standards for school mathematics: A vision of mathematical power and appreciation for all.
  • Nishimura, M. (2023). Viewbirdiformer: Learning to recover ground-plane crowd trajectories and ego-motion from a single ego-centric view. IEEE Robotics and Automation Letters, 8(1), 368–375. https://doi.org/10.1109/LRA.2022.3221335
  • Placido, D. (2023). A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nature Medicine, 29(5), 1113–1122. https://doi.org/10.1038/s41591-023-02332-5
  • Qian, Z. (2023). Reinforcement learning based dual-UAV trajectory optimization for secure communication. Electronics (Switzerland), 12(9). https://doi.org/10.3390/electronics12092008
  • Risdiyanti, I., & Prahmana, R. C. I. (2021). Designing learning trajectory of set through the indonesian shadow puppets and mahabharata stories. Infinity Journal, 10(2), 331. https://doi.org/10.22460/infinity.v10i2.p331-348
  • Rokhmawati, L. N., Ratnaningsih, N., & Ni’mah, K. (2023). Aturan penjumlahan dan perkalian dalam kaidah pencacahan: bagaimanakah desain hypothetical learning trajectory berbasis RME? Jurnal Pembelajaran Matematika Inovatif, 6(3), 937–950. https://doi.org/10.22460/jpmi.v6i3.17321
  • Siemon, D., Barkatsas, T., & Seah, R. (2019). Researching and Using Progressions (Trajectories) in Mathematics Education. BRILL. https://doi.org/10.1163/9789004396449
  • Silverman, D. (2009). Doing qualitative research (3rd ed.). SAGE Publications Ltd.
  • Simon, M. A. (2018). Empirically-based hypothetical learning trajectories for fraction concepts: Products of the Learning Through Activity research program. Journal of Mathematical Behavior, 52, 188–200. https://doi.org/10.1016/j.jmathb.2018.03.003
  • Sukestiyarno, Y. L. (2023). Learning trajectory of non-Euclidean geometry through ethnomathematics learning approaches to improve spatial ability. Eurasia Journal of Mathematics, Science and Technology Education, 19(6). https://doi.org/10.29333/ejmste/13269
  • Supply, A.-S., Vanluydt, E., Van Dooren, W., & Onghena, P. (2023). Out of proportion or out of context? Comparing 8- to 9-year-olds’ proportional reasoning abilities across fair-sharing, mixtures, and probability contexts. Educational Studies in Mathematics, 113(3), 371–388. https://doi.org/10.1007/s10649-023-10212-5
  • Tordesillas, J. (2023). Deep-PANTHER: Learning-based perception-aware trajectory planner in dynamic environments. IEEE Robotics and Automation Letters, 8(3), 1399–1406. https://doi.org/10.1109/LRA.2023.3235678
  • Tykhonov, A. (2023). A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission. Astroparticle Physics, 146. https://doi.org/10.1016/j.astropartphys.2022.102795
  • Ulfa, C., & Wijaya, A. (2019). Expanding hypothetical learning trajectory in mathematics instructional. Journal of Physics: Conference Series, 1320(1), 012091. https://doi.org/10.1088/1742-6596/1320/1/012091
  • Wang, X. (2023). A deep learning model for ship trajectory prediction using automatic identification system (AIS) data. Information (Switzerland), 14(4). https://doi.org/10.3390/info14040212
  • Wijaya, A., Elmaini, E., & Doorman, M. (2021). A learning trajectory for probability: a case of game-based learning. Journal on Mathematics Education, 12(1), 1–16. https://doi.org/10.22342/jme.12.1.12836.1-16
  • Yuan, Z. (2023). Hierarchical trajectory planning for narrow-space automated parking with deep reinforcement learning: A federated learning scheme. Sensors, 23(8). https://doi.org/10.3390/s23084087
  • Yuliardi, R., & Rosjanuardi, R. (2021). Hypothetical learning trajectory in student’s spatial abilities to learn geometric transformation. JRAMathEdu (Journal of Research and Advances in Mathematics Education), 6(3), 174–190. https://doi.org/10.23917/jramathedu.v6i3.13338
  • Zhan, T. (2023). VRR-Net: Learning vehicle–road relationships for vehicle trajectory prediction on highways. Mathematics, 11(6). https://doi.org/10.3390/math11061293
  • Ziyi, Z. (2023). Multi-agent deep-learning based comparative analysis of team sport trajectories. IEEE Access, 11, 43305–43315. https://doi.org/10.1109/ACCESS.2023.3269287
  • Zou, Y. (2023). A learning trajectory planning for vibration suppression of industrial robot. Industrial Robot. https://doi.org/10.1108/IR-02-2023-0013

The Learning Trajectory Based on STEM of Elementary School Pupils’ in Solving Proportion Material: Didactical Design-Research

Year 2023, Volume: 10 Issue: 6, 84 - 103, 01.11.2023
https://doi.org/10.17275/per.23.90.10.6

Abstract

This study aims to determine the trajectory of students' thinking when solving proportion problems using STEM-based learning media. The participants were 27 fifth-grade students from SD Negeri 2 Pilangsari in Cirebon Regency. The students are divided into four groups using purposive sampling and receive the same treatment. The treatment involved a proportion study that utilized STEM media, and the student’s learning trajectory was monitored based on their problem-solving patterns. Hypothetical Learning Trajectory (HLT) was used to develop the hypotheses. The HLT was used as a guide for the researchers' assumptions. The data were collected through observation by researchers, student work, and documentation. The results of the HLT were used to test the assumptions related to the student's thinking processes and their learning in completing proportion operations using STEM. Based on the results obtained during the practice, some findings exceeded the researcher's expectations and hypotheses, but some did not. These differences become a new finding expected to become a subject for further research, where several groups have different ways of thinking based on mathematical disposition. Through STEM media, the electrical engineering students' high enthusiasm and creativity can be known through the electric graph. In conclusion, proportional relationships are an important mathematical concept with practical applications in various fields. The use of STEM media for teaching materials can help students acquire a better understanding of mathematical concepts and skills.

References

  • Abramovich, S., Grinshpan, A. Z., & Milligan, D. L. (2019). Teaching mathematics through concept motivation and action learning. Education Research International, 2019, 1–13. https://doi.org/10.1155/2019/3745406
  • Angraini, L. M. (2021). Didactical design of mathematical reasoning in mathematical basic concepts of courses. JNPM (Jurnal Nasional Pendidikan Matematika), 5(1), 1. https://doi.org/10.33603/jnpm.v5i1.3943
  • Anwar, L. (2023). Learning trajectory of geometry proof construction: Studying the emerging understanding of the structure of Euclidean proof. Eurasia Journal of Mathematics, Science and Technology Education, 19(5). https://doi.org/10.29333/ejmste/13160
  • Ausubel, D. P. (1962). A subsumption theory of meaningful verbal learning and retention. The Journal of General Psychology, 66(2), 213–224. https://doi.org/10.1080/00221309.1962.9711837
  • Bahamonde, A. D. C., Fortuny Aymemí, J. M., & Gómez I Urgellés, J. V. (2017). Mathematical modelling and the learning trajectory: Tools to support the teaching of linear algebra. International Journal of Mathematical Education in Science and Technology, 48(3), 338–352. https://doi.org/10.1080/0020739X.2016.1241436
  • Bakker, A., & Van Eerde, D. (2015). An introduction to design-based research with an example from statistics education. In A. Bikner-Ahsbahs, C. Knipping, & N. Presmeg (Eds.), Approaches to Qualitative Research in Mathematics Education (pp. 429–466). Springer Netherlands. https://doi.org/10.1007/978-94-017-9181-6_16
  • Baroody, A. J. (2022). Lessons learned from 10 experiments that tested the efficacy and assumptions of hypothetical learning trajectories. Education Sciences, 12(3). https://doi.org/10.3390/educsci12030195
  • Brinberg, D., & McGrath, J. E. (1985). Validity and the research process. In Validity and the Research Process. Sage Publications.
  • Broietti, F. C. D. (2022). Hypothetical learning trajectory and understanding the content of solutions in the teaching of chemistry. Curriculo Sem Fronteiras, 22. https://doi.org/10.35786/1645-1384.v22.1810
  • Busch, E. L. (2023). Multi-view manifold learning of human brain-state trajectories. Nature Computational Science, 3(3), 240–253. https://doi.org/10.1038/s43588-023-00419-0
  • Cazares, S. I. (2019). Design and evaluation of a hypothetical learning trajectory to confidence intervals based on simulation and real data. Bolema - Mathematics Education Bulletin, 33(63), 1–26. https://doi.org/10.1590/1980-4415v33n63a01
  • Chen, Y. H. (2023). Manipulator trajectory optimization using reinforcement learning on a reduced-order dynamic model with deep neural network compensation. Machines, 11(3). https://doi.org/10.3390/machines11030350
  • Cuevas-Vallejo, A. (2023). A learning trajectory for university students regarding the concept of vector. Journal of Mathematical Behavior, 70. https://doi.org/10.1016/j.jmathb.2023.101044
  • Demetriou, A. (2023). A deep learning framework for generation and analysis of driving scenario trajectories. SN Computer Science, 4(3). https://doi.org/10.1007/s42979-023-01714-3
  • Dhuheir, M. A. (2023). Deep reinforcement learning for trajectory path planning and distributed inference in resource-constrained UAV swarms. IEEE Internet of Things Journal, 10(9), 8185–8201. https://doi.org/10.1109/JIOT.2022.3231341
  • Feishi, G., Rongjian, H., & Lingyuan, G. (2017). Theory and development of teaching through variation in mathematics in China. In Theory and Development of Teaching through Variation in Mathematics in China (pp. 13–41). BRILL.
  • Ferreira, P. E. A., & Silva, K. A. P. D. (2019). Modelagem matemática e uma proposta de trajetória hipotética de aprendizagem. Bolema: Boletim de Educação Matemática, 33(65), 1233–1254. https://doi.org/10.1590/1980-4415v33n65a13
  • George, M., & Apter, A. J. (2004). Gaining insight into patients’ beliefs using qualitative research methodologies. Curr Opin Allergy Clin Immunol, 4(3), 185–189.
  • Gravemeijer, K. (1994). Developing realistic mathematics education. CD Beta Press.
  • Guarte, J. M., & Barrios, E. B. (2006). Estimation under purposive sampling. Communications in Statistics - Simulation and Computation, 35(2), 277–284. https://doi.org/10.1080/03610910600591610
  • Haggarty, L. (Ed.). (2002). Aspects of teaching secondary mathematics: Perspectives on practice. RoutledgeFalmer : Open University Press.
  • Huh, J. (2023). Deep learning-based autonomous excavation: a bucket-trajectory planning algorithm. IEEE Access, 11, 38047–38060. https://doi.org/10.1109/ACCESS.2023.3267120
  • Ivars, P. (2018). Enhancing noticing: Using a hypothetical learning trajectory to improve pre-service primary teachers’ professional discourse. Eurasia Journal of Mathematics, Science and Technology Education, 14(11). https://doi.org/10.29333/ejmste/93421
  • Kaitera, S., & Harmoinen, S. (2022). Developing mathematical problem-solving skills in primary school by using visual representations on heuristics. LUMAT: International Journal on Math, Science and Technology Education, 10(2). https://doi.org/10.31129/LUMAT.10.2.1696
  • Kirk, J., & Miller, M. L. (1988). Reliability and validity in qualitative research. International Journal of Qualitative Studies in Education, 1(1).
  • Kolaghassi, R. (2023). Deep learning models for stable gait prediction applied to exoskeleton reference trajectories for children with cerebral palsy. IEEE Access, 11, 31962–31976. https://doi.org/10.1109/ACCESS.2023.3252916
  • Kuncoro, K. S., Zakkia, A., Sulistyowati, F., & Kusumaningrum, B. (2021). Students’ mathematical critical thinking based on self-esteem through problem based learning in geometry. Southeast Asian Mathematics Education Journal, 11(1), 41–52. https://doi.org/10.46517/seamej.v11i1.122
  • Lestari, T. V. D. (2020). Hypothetical learning trajectory and students’ understanding of the concepts of the arithmetic sequence. Journal of Physics: Conference Series, 1581(1). https://doi.org/10.1088/1742-6596/1581/1/012038
  • Lidinillah, D. A. M. (2011). Educational design research: A theoretical framework for action. Universitas Pendidikan Indonesia.
  • Luo, Q. (2023). Deep reinforcement learning based computation offloading and trajectory planning for multi-UAV cooperative target search. IEEE Journal on Selected Areas in Communications, 41(2), 504–520. https://doi.org/10.1109/JSAC.2022.3228558
  • Mansouri, N. (2023). Machine learning of multi-modal tumor imaging reveals trajectories of response to precision treatment. Cancers, 15(6). https://doi.org/10.3390/cancers15061751
  • Mattison, R. E. (2023). Longitudinal trajectories of reading and mathematics achievement for students with learning disabilities. Journal of Learning Disabilities, 56(2), 132–144. https://doi.org/10.1177/00222194221085668
  • Merriam, S. B. (1998). Qualitative research and case study applications in education. Jossey-Bass Publishers.
  • Mutaqin, E. J., Herman, T., Wahyudin, W., & Muslihah, N. N. (2023). Hypothetical learning trajectory in place value concepts in elementary school. Mosharafa: Jurnal Pendidikan Matematika, 12(1), 125–134. https://doi.org/10.31980/mosharafa.v12i1.1313
  • Mаhammadovna, S. I. (2023). Features of cluster design in modern paradigms of education. TELEMATIQUE, 22(1), 348–355.
  • National Council of Teachers of Mathematics (NCTM)). (1989). Curriculum and evaluation standards for school mathematics: A vision of mathematical power and appreciation for all.
  • Nishimura, M. (2023). Viewbirdiformer: Learning to recover ground-plane crowd trajectories and ego-motion from a single ego-centric view. IEEE Robotics and Automation Letters, 8(1), 368–375. https://doi.org/10.1109/LRA.2022.3221335
  • Placido, D. (2023). A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nature Medicine, 29(5), 1113–1122. https://doi.org/10.1038/s41591-023-02332-5
  • Qian, Z. (2023). Reinforcement learning based dual-UAV trajectory optimization for secure communication. Electronics (Switzerland), 12(9). https://doi.org/10.3390/electronics12092008
  • Risdiyanti, I., & Prahmana, R. C. I. (2021). Designing learning trajectory of set through the indonesian shadow puppets and mahabharata stories. Infinity Journal, 10(2), 331. https://doi.org/10.22460/infinity.v10i2.p331-348
  • Rokhmawati, L. N., Ratnaningsih, N., & Ni’mah, K. (2023). Aturan penjumlahan dan perkalian dalam kaidah pencacahan: bagaimanakah desain hypothetical learning trajectory berbasis RME? Jurnal Pembelajaran Matematika Inovatif, 6(3), 937–950. https://doi.org/10.22460/jpmi.v6i3.17321
  • Siemon, D., Barkatsas, T., & Seah, R. (2019). Researching and Using Progressions (Trajectories) in Mathematics Education. BRILL. https://doi.org/10.1163/9789004396449
  • Silverman, D. (2009). Doing qualitative research (3rd ed.). SAGE Publications Ltd.
  • Simon, M. A. (2018). Empirically-based hypothetical learning trajectories for fraction concepts: Products of the Learning Through Activity research program. Journal of Mathematical Behavior, 52, 188–200. https://doi.org/10.1016/j.jmathb.2018.03.003
  • Sukestiyarno, Y. L. (2023). Learning trajectory of non-Euclidean geometry through ethnomathematics learning approaches to improve spatial ability. Eurasia Journal of Mathematics, Science and Technology Education, 19(6). https://doi.org/10.29333/ejmste/13269
  • Supply, A.-S., Vanluydt, E., Van Dooren, W., & Onghena, P. (2023). Out of proportion or out of context? Comparing 8- to 9-year-olds’ proportional reasoning abilities across fair-sharing, mixtures, and probability contexts. Educational Studies in Mathematics, 113(3), 371–388. https://doi.org/10.1007/s10649-023-10212-5
  • Tordesillas, J. (2023). Deep-PANTHER: Learning-based perception-aware trajectory planner in dynamic environments. IEEE Robotics and Automation Letters, 8(3), 1399–1406. https://doi.org/10.1109/LRA.2023.3235678
  • Tykhonov, A. (2023). A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission. Astroparticle Physics, 146. https://doi.org/10.1016/j.astropartphys.2022.102795
  • Ulfa, C., & Wijaya, A. (2019). Expanding hypothetical learning trajectory in mathematics instructional. Journal of Physics: Conference Series, 1320(1), 012091. https://doi.org/10.1088/1742-6596/1320/1/012091
  • Wang, X. (2023). A deep learning model for ship trajectory prediction using automatic identification system (AIS) data. Information (Switzerland), 14(4). https://doi.org/10.3390/info14040212
  • Wijaya, A., Elmaini, E., & Doorman, M. (2021). A learning trajectory for probability: a case of game-based learning. Journal on Mathematics Education, 12(1), 1–16. https://doi.org/10.22342/jme.12.1.12836.1-16
  • Yuan, Z. (2023). Hierarchical trajectory planning for narrow-space automated parking with deep reinforcement learning: A federated learning scheme. Sensors, 23(8). https://doi.org/10.3390/s23084087
  • Yuliardi, R., & Rosjanuardi, R. (2021). Hypothetical learning trajectory in student’s spatial abilities to learn geometric transformation. JRAMathEdu (Journal of Research and Advances in Mathematics Education), 6(3), 174–190. https://doi.org/10.23917/jramathedu.v6i3.13338
  • Zhan, T. (2023). VRR-Net: Learning vehicle–road relationships for vehicle trajectory prediction on highways. Mathematics, 11(6). https://doi.org/10.3390/math11061293
  • Ziyi, Z. (2023). Multi-agent deep-learning based comparative analysis of team sport trajectories. IEEE Access, 11, 43305–43315. https://doi.org/10.1109/ACCESS.2023.3269287
  • Zou, Y. (2023). A learning trajectory planning for vibration suppression of industrial robot. Industrial Robot. https://doi.org/10.1108/IR-02-2023-0013
There are 56 citations in total.

Details

Primary Language English
Subjects Educational Psychology
Journal Section Research Articles
Authors

Mochamad Guntur 0000-0001-6226-7999

Siti Sahronih 0000-0003-2512-0626

Nur Indah Septia Ningsih 0000-0002-8271-6728

Puja Windari 0009-0003-4957-9957

Early Pub Date November 2, 2023
Publication Date November 1, 2023
Acceptance Date September 11, 2023
Published in Issue Year 2023 Volume: 10 Issue: 6

Cite

APA Guntur, M., Sahronih, S., Ningsih, N. I. S., Windari, P. (2023). The Learning Trajectory Based on STEM of Elementary School Pupils’ in Solving Proportion Material: Didactical Design-Research. Participatory Educational Research, 10(6), 84-103. https://doi.org/10.17275/per.23.90.10.6