The need analysis for computational chemistry based learning media atomic structure and chemical bonding basic chemistry courses

Authors

DOI:

https://doi.org/10.24114/jpkim.v16i1.56258

Keywords:

Atomic structure, Chemical bonds, Learning media, Needs analysis

Abstract

The aim of this research is to analyze students' needs for computational chemistry-based learning media on atomic structure and chemical bonding in the Basic Chemistry course. The population of this study were the first grade students of the Chemistry Department, FMIPA Unimed. The number of samples in this study was 93 students from three classes. The instruments used are multiple choice questions and questionnaires to determine mastery of atomic structure and chemical bonding. The research results show that the average score is 34.430 (poor category). The average score achieved in atomic structure material was 33.16 (very poor). The lowest score achieved in the atomic properties sub-material was 9.3. The average score achieved in chemical bonding material was 36.1 (very poor). The lowest score achieved in the properties of ionic compound sub-material was 17.5. The results of the questionnaire showed that the atomic structure material that students considered the most difficult was the wave mechanics atomic model at 72.233 (quite difficult), while for chemical bonding material it was the octet and duplet rule at 71.055 (quite difficult).

Author Biographies

Asep Wahyu Nugraha, Universitas Negeri Medan, Medan 20221, Indonesia

Department of Chemistry 

Marudut Sinaga, Universitas Negeri Medan, Medan 20221, Indonesia

Department of Chemistry

Ayi Darmana, Universitas Negeri Medan, Medan 20221, Indonesia

Department of Chemistry

Ani Sutiani, Universitas Negeri Medan, Medan 20221, Indonesia

Department of Chemistry

Nisa Qurrata Aini, Universitas Pendidikan Indonesia, Bandung 40154, Indonesia

Department of Chemistry

References

Abdinejad, M., Talaie, B., Qorbani, H. S., & Dalili, S. (2020). Student Perceptions Using Augmented Reality and 3D visualization technologies in chemistry education. Journal of Science Education and Technology, 30(1), 87“96. https://doi.org/10.1007/s10956-020-09880-2

Benzer, A. I., & Yildiz, B. (2019). The effect of computer-AIDED 3D modeling activities on pre-service teachers™ spatial abilities and attitudes towards 3d modeling. Journal of Baltic Science Education, 18(3), 335“348. https://doi.org/10.33225/jbse/19.18.335

Bongers, A., Beauvoir, B., Streja, N., Northoff, G., & Flynn, A. B. (2020). Building mental models of a reaction mechanism: the influence of static and animated representations, prior knowledge, and spatial ability. Chemistry Education Research and Practice, 21(2), 496“512. https://doi.org/10.1039/c9rp00198k

Boone, H., & Boone, D. (2012). Analyzing likert data. Journal of Extension, 50(2). https://doi.org/10.34068/joe.50.02.48

Dickmann, T., Opfermann, M., Dammann, E., Lang, M., & Rumann, S. (2019). What you see is what you learn? The role of visual model comprehension for academic success in chemistry. Chemistry Education Research and Practice, 20(4), 804“820. https://doi.org/10.1039/c9rp00016j

Eriksen, K., Nielsen, B. E., & Pittelkow, M. (2020). Visualizing 3D molecular structures using an augmented reality app. Journal of Chemical Education, 97(5), 1487“1490. https://doi.org/10.1021/acs.jchemed.9b01033

Aw, J. K., Boellaard, K. C., Tan, T. K., Yap, J., Loh, Y. P., Colasson, B., Blanc, É., Lam, Y., & Fung, F. M. (2020). Interacting with three-dimensional molecular structures using an augmented reality mobile app. Journal of Chemical Education, 97(10), 3877“3881. https://doi.org/10.1021/acs.jchemed.0c00387

Kolomuç, A., & Tekin, S. (2011). Chemistry teachers™ misconceptions concerning concept of chemical reaction rate. International Journal of Physics & Chemistry Education, 3(2), 84“101. https://doi.org/10.51724/ijpce.v3i2.194

Lindgren, R., & Schwartz, D. L. (2009). Spatial learning and computer simulations in science. International Journal of Science Education, 31(3), 419“438. https://doi.org/10.1080/09500690802595813

Lowrie, T., Logan, T., & Hegarty, M. (2019). The influence of spatial visualization training on students™ spatial reasoning and mathematics performance. Journal of Cognition and Development, 20(5), 729“751. https://doi.org/10.1080/15248372.2019.1653298

Lubinski, D. (2010). Spatial ability and STEM: A sleeping giant for talent identification and development. Personality and Individual Differences, 49(4), 344“351. https://doi.org/10.1016/j.paid.2010.03.022

Marfali, D. (2019). Pengembangan lembar kerja berbasis Predict-Observe-Explain untuk pemodelan reaksi SN2 pada Alkil Halida menggunakan NWChem. Thesis. Program Studi Ilmu Kimia, UIN Sunan Gunung Djati. Bandung.

Nugraha, A. W., Onggo, D., & Martoprawiro, M. A. (2019). Theoretical study on structure prediction and molecular formula determination of polymeric complexes comprising Fe(II) and 1,2,4-H-Triazole Ligand. Russian Journal of Inorganic Chemistry, 64(6), 755“761. https://doi.org/10.1134/s0036023619060123

Nugraha, A. W., Jahro, I. S., Onggo, D., & Martoprawiro, M. A. (2022). Predictability for polymeric structure deviations, transition temperature, and transition patterns in 1,2,4 H-Triazole Iron(II) complexes using density functional theory method. Russian Journal of Inorganic Chemistry, 67(S2), S150“S157. https://doi.org/10.1134/s0036023622602653

Nugraha, A. W., Muchtar, Z., Jahro, I. S., Sutiani, A., Nasution, H. A., & Ivansyah, A. L. (2021). The study of stability and structure of the interaction between β-Carotene compounds with methanol, ethanol, acetone, chloroform, carbon tetrachloride, cyclohexane, and N-hexane using the hartree-fock and the density functional theory method. Journal of Physics: Conference Series, 1819(1), 012055. https://doi.org/10.1088/1742-6596/1819/1/012055

Stieff, M., & Uttal, D. (2015). How Much Can Spatial Training Improve STEM Achievement?. Educational Psychology Review, 27(4), 607“615. https://doi.org/10.1007/s10648-015-9304-8

Treagust, D., Chittleborough, G., & Mamiala, T. (2003). The role of submicroscopic and symbolic representations in chemical explanations. International Journal of Science Education, 25(11), 1353“1368. https://doi.org/10.1080/0950069032000070306

Willits, F. K., Theodori, G. L., & Luloff, A. E. (2016). Another look at Likert scales. Journal of Rural Social Sciences, 31(3), 6. https://egrove.olemiss.edu/jrss/vol31/iss3/6

Downloads

Published

2024-04-20

Issue

Vol. 16 No. 1 (2024): April

Section

Articles

License

Copyright (c) 2024 Asep Wahyu Nugraha, Marudut Sinaga, Ayi Darmana, Ani Sutiani, Nisa Qurrata Aini

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors published in this journal agree to the following terms:

  1. The copyright of each article is retained by the author (s).
  2. The author grants the journal the first publication rights with the work simultaneously licensed under the Creative Commons Attribution License, allowing others to share the work with an acknowledgment of authorship and the initial publication in this journal.
  3. Authors may enter into separate additional contractual agreements for the non-exclusive distribution of published journal versions of the work (for example, posting them to institutional repositories or publishing them in a book), with acknowledgment of their initial publication in this journal.
  4. Authors are permitted and encouraged to post their work online (For example in the Institutional Repository or on their website) before and during the submission process, as this can lead to productive exchanges, as well as earlier and larger citations of published work.

Creative Commons License

Jurnal Pendidikan Kimia is licensed under a Creative Commons Attribution 4.0 International License