Integration of algebra and chemistry concepts with molecular descriptors: A problem-based-learning exercise

A problem-based learning experience integrating mathematical concepts of linear and abstract algebra for undergraduate chemistry students is presented. The pedagogical framework was focused on the conceptual understanding of the vector space, graph theory and matrix algebra as a tool to obtain chemi...

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Autores:
Cubillán, Néstor
Marrero-Ponce, Yovani
Inciarte González, Alicia
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5286
Acceso en línea:
https://hdl.handle.net/11323/5286
https://repositorio.cuc.edu.co/
Palabra clave:
Problem-based-learning
Abstract and linear algebra
Chemoinformatics
Multidisciplinary
Aprendizaje basado en problemas
Álgebra lineal y abstracta
Quimioinformática
Multidisciplinariedad
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_c37f2f2b1df934522e079e09077251df
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5286
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Integration of algebra and chemistry concepts with molecular descriptors: A problem-based-learning exercise
title Integration of algebra and chemistry concepts with molecular descriptors: A problem-based-learning exercise
spellingShingle Integration of algebra and chemistry concepts with molecular descriptors: A problem-based-learning exercise
Problem-based-learning
Abstract and linear algebra
Chemoinformatics
Multidisciplinary
Aprendizaje basado en problemas
Álgebra lineal y abstracta
Quimioinformática
Multidisciplinariedad
title_short Integration of algebra and chemistry concepts with molecular descriptors: A problem-based-learning exercise
title_full Integration of algebra and chemistry concepts with molecular descriptors: A problem-based-learning exercise
title_fullStr Integration of algebra and chemistry concepts with molecular descriptors: A problem-based-learning exercise
title_full_unstemmed Integration of algebra and chemistry concepts with molecular descriptors: A problem-based-learning exercise
title_sort Integration of algebra and chemistry concepts with molecular descriptors: A problem-based-learning exercise
dc.creator.fl_str_mv Cubillán, Néstor
Marrero-Ponce, Yovani
Inciarte González, Alicia
dc.contributor.author.spa.fl_str_mv Cubillán, Néstor
Marrero-Ponce, Yovani
Inciarte González, Alicia
dc.subject.spa.fl_str_mv Problem-based-learning
Abstract and linear algebra
Chemoinformatics
Multidisciplinary
Aprendizaje basado en problemas
Álgebra lineal y abstracta
Quimioinformática
Multidisciplinariedad
topic Problem-based-learning
Abstract and linear algebra
Chemoinformatics
Multidisciplinary
Aprendizaje basado en problemas
Álgebra lineal y abstracta
Quimioinformática
Multidisciplinariedad
description A problem-based learning experience integrating mathematical concepts of linear and abstract algebra for undergraduate chemistry students is presented. The pedagogical framework was focused on the conceptual understanding of the vector space, graph theory and matrix algebra as a tool to obtain chemical information. The students were capable to solve a problem of physicochemical properties prediction through the calculation of molecular descriptors of the TOMOCOMD (acronym for TOpological MOlecular COMputational Design) approach. A “scientific congress” was organized by students to expose the results of the research. This evaluation strategy stimulated the self- and co-evaluation. The proposed experience demonstrated an enhanced learning compared to the traditional model.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-09-23T15:13:46Z
dc.date.available.none.fl_str_mv 2019-09-23T15:13:46Z
dc.date.issued.none.fl_str_mv 2019
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 1870-8404
0187-893X
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1870-8404
0187-893X
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/5286
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv http://dx.doi.org/10.22201/fq.18708404e.2019.2.65090
dc.relation.references.spa.fl_str_mv Ajai, J. T., Imoko, B. I., & Emmanuel, I. O. (2013). Comparison of the Learning Effectiveness of Problem-Based Learning ( PBL ) and Conventional Method of Teaching Algebra. Journal of Education and Practice, 4(1), 131–136. Retrieved from http://www. iiste.org/Journals/index.php/JEP/article/view/4053 Ali, R., Hukamdad, D., Akhter, A., & Khan, A. (2010). Effect of Using Problem Solving Method in Teaching Mathematics on the Achievement of Mathematics Students. Asian Social Science, 6(2), 67. https://doi.org/10.5539/ass.v6n2p67 Ashraf, S. S., Marzouk, S. A. M., Shehadi, I. A., & Murphy, B. M. (2011). An Integrated Professional and Transferable Skills Course for Undergraduate Chemistry Students. Journal of Chemical Education, 88(1), 44–48. https://doi.org/10.1021/ed100275y Bledsoe, K. E., & Flick, L. (2012). Concept Development and Meaningful Learning Among Electrical Engineering Students Engaged in a Problem-Based Laboratory Experience. Journal of Science Education and Technology, 21(2), 226–245. https:// doi.org/10.1007/s10956-011-9303-6 Chen, W. H. (2013). Teaching geometry through problem-based learning and creative design. Jurnal Teknologi (Social Sciences), 63, 123–127. Cowden, C. D., & Santiago, M. F. (2016). Interdisciplinary Explorations: Promoting Critical Thinking via Problem-Based Learning in an Advanced Biochemistry Class. Journal of Chemical Education, 93(3), 464–469. https://doi.org/10.1021/acs. jchemed.5b00378 Eaton, J. (2016). GNU Octave. Fakayode, S. O., King, A. G., Yakubu, M., Mohammed, A. K., & Pollard, D. A. (2012). Determination of Fe Content of Some Food Items by Flame Atomic Absorption Spectroscopy (FAAS): A Guided-Inquiry Learning Experience in Instrumental Analysis Laboratory. Journal of Chemical Education, 89(1), 109–113. https://doi. org/10.1021/ed1011585 Graovac, A., & Gutman, I. (1979). The Determinant of the Adjacency Matrix of a Molecular Graph. MATCH Communications in Mathematical and in COMputational Chemistry, 6, 49–73. Gron, L. U., Bradley, S. B., McKenzie, J. R., Shinn, S. E., & Teague, M. W. (2013). How To Recognize Success and Failure: Practical Assessment of an Evolving, First-Semester Laboratory Program Using Simple, Outcome-Based Tools. Journal of Chemical Education, 90(6), 694–699. https://doi.org/10.1021/ed200523w Gurses, A., Dogar, C., & Geyik, E. (2015). Teaching of the Concept of Enthalpy Using Problem Based Learning Approach. Procedia - Social and Behavioral Sciences, 197, 2390–2394. https://doi.org/10.1016/J.SBSPRO.2015.07.298 Gutman, I., & Vidovic, D. (2002). The Largest Eigenvalues of Adjacency and Laplacian Matrices, and Ionization Potentials of Alkanes. Indian Journal of Chemistry, 41A, 893–896. Hailikari, T. K., & Nevgi, A. (2010). How to Diagnose At‐risk Students in Chemistry: The case of prior knowledge assessment. International Journal of Science Education, 32(15), 2079–2095. https://doi.org/10.1080/09500690903369654 Hopkins, T. A., & Samide, M. (2013). Using a Thematic Laboratory-Centered Curriculum To Teach General Chemistry. Journal of Chemical Education, 90(9), 1162–1166. https://doi.org/10.1021/ed300438t Jansson, S., Söderström, H., Andersson, P. L., & Nording, M. L. (2015). Implementation of Problem-Based Learning in Environmental Chemistry. Journal of Chemical Education, 92(12), 2080–2086. https://doi.org/10.1021/ed500970y Jones, B. D., Epler, C. M., Tech, V., Bryant, L. H., Paretti, M. C., Jones, B. D., … Paretti, L. H. (2013). The Effects of a Collaborative Problem-based Learning Experience on Students’ Motivation in Engineering Capstone Courses. Interdisciplinary Journal of Problem-Based Learning, 7(2), 5–16. https://doi.org/10.7771/1541-5015.1344 Kerber, A., Laue, R., Meringer, M., Rücker, C., & Schymanski, E. (2014). Mathematical chemistry and chemoinformatics: Structure generation, elucidation and quantitative structure-property relationships. Mathematical Chemistry and Chemoinformatics: Structure Generation, Elucidation and Quantitative StructureProperty Relationships. https://doi.org/10.1515/9783110254075 Kolb, A. Y., & Kolb, D. A. (2012). Experiential Learning Theory. In Seel N.M. (Ed.), Encyclopedia of the Sciences of Learning (pp. 1215–1219). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4419-1428-6_227 Lin, Y. I., Son, J. Y., & Rudd, J. A. (2016). Asymmetric translation between multiple representations in chemistry. International Journal of Science Education, 38(4), 644–662. https://doi.org/10.1080/09500693.2016.1144945 Llorens-Molina, J.-A. (2010). El aprendizaje basado en problemas como estrategia para el cambio metodológico en los trabajos de laboratorio. Química Nova, 33(4), 994– 999. https://doi.org/10.1590/S0100-40422010000400043 Maplesoft. (2016). MAPLE. Marrero-Ponce, Y. (2003). Total and Local Quadratic Indices of the Molecular Pseudograph’s Atom Adjacency Matrix: Applications to the Prediction of Physical Properties of Organic Compounds. Molecules, 8(9), 687–726. https://doi. org/10.3390/80900687 Marrero-Ponce, Y., Garit, J., Torrens, F., Zaldivar, V., & Castro, E. (2004). Atom, AtomType, and Total Linear Indices of the “Molecular Pseudograph’s Atom Adjacency Matrix”: Application to QSPR/QSAR Studies of Organic Compounds. Molecules, 9(12), 1100–1123. https://doi.org/10.3390/91201100 Marrero-Ponce, Y., Khan, M. T. H., Casañola Martín, G. M., Ather, A., Sultankhodzhaev, M. N., Torrens, F., & Rotondo, R. (2007). Prediction of Tyrosinase Inhibition Activity Using Atom-Based Bilinear Indices. ChemMedChem, 2(4), 449–478. https://doi. org/10.1002/cmdc.200600186 Marrero Ponce, Y. (2004). Total and local (atom and atom type) molecular quadratic indices: significance interpretation, comparison to other molecular descriptors, and QSPR/QSAR applications. Bioorganic & Medicinal Chemistry, 12(24), 6351– 6369. https://doi.org/10.1016/J.BMC.2004.09.034 MATLAB. (2016). MATLAB. MATLAB. https://doi.org/10.1201/9781420034950 Mihalić, Z., & Trinajstić, N. (1992). A graph-theoretical approach to structure-property relationships. Journal of Chemical Education, 69(9), 701. https://doi.org/10.1021/ ed069p701 Murphy, P. M. (2007). Teaching Structure–Property Relationships: Investigating Molecular Structure and Boiling Point. Journal of Chemical Education, 84(1), 97. https://doi. org/10.1021/ed084p97 Pelligrino, J. W., & Hilton, M. L. (Eds.). (2012). Education for Life and Work. Washington: National Academies Press. https://doi.org/10.17226/13398 Ram, P. (1999). Problem-Based Learning in Undergraduate Instruction. A Sophomore Chemistry Laboratory. Journal of Chemical Education, 76(8), 1122. https://doi. org/10.1021/ed076p1122 Ross, A., & Willson, V. (2012). The Effects of Representations, Constructivist Approaches, and Engagement on Middle School Students’ Algebraic Procedure and Conceptual Understanding. School Science and Mathematics, 112(2), 117–128. https://doi. org/10.1111/j.1949-8594.2011.00125.x SageMath. (2016). SageMath Kernel. Sahin, M. (2010). Effects of Problem-Based Learning on University Students’ Epistemological Beliefs About Physics and Physics Learning and Conceptual Understanding of Newtonian Mechanics. Journal of Science Education and Technology, 19(3), 266–275. https://doi.org/10.1007/s10956-009-9198-7 Todeschini, R., & Consonni, V. (2009). Molecular Descriptors for Chemoinformatic. (R. Todeschini & V. Consonni, Eds.) (Vol. 41). Weinheim, Germany: Wiley-VCH.
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spelling Cubillán, NéstorMarrero-Ponce, YovaniInciarte González, Alicia2019-09-23T15:13:46Z2019-09-23T15:13:46Z20191870-84040187-893Xhttps://hdl.handle.net/11323/5286Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/A problem-based learning experience integrating mathematical concepts of linear and abstract algebra for undergraduate chemistry students is presented. The pedagogical framework was focused on the conceptual understanding of the vector space, graph theory and matrix algebra as a tool to obtain chemical information. The students were capable to solve a problem of physicochemical properties prediction through the calculation of molecular descriptors of the TOMOCOMD (acronym for TOpological MOlecular COMputational Design) approach. A “scientific congress” was organized by students to expose the results of the research. This evaluation strategy stimulated the self- and co-evaluation. The proposed experience demonstrated an enhanced learning compared to the traditional model.En este trabajo se presenta una experiencia de de aprendizaje basado en problemas que integra conceptos matemáticos de algebra lineal y abstracta para estudiante de pregrado de química. El marco pedagógico se enfocó en el entendimiento conceptual de espacios vectoriales, teoría de grafos y álgebra matricial como una herramienta para obtener información química. Los estudiantes fueron capaces de resolver un problema de predicción de propiedades fisicoquímicas a través del cálculo de descriptores del esquema TOMOCOMD (siglas en inglés de TOpological MOlecular COMputational Design). Los estudiantes organizaron un “congreso científico” para exponer los resultados de la investigación. Esta evaluación estimuló el auto- y co-evaluación. La experiencia propuesta demostró un aprendizaje mayor al ser comparado con el modelo tradicional.Cubillán, NéstorMarrero-Ponce, Yovani-will be generated-orcid-0000-0003-2721-1142-600Inciarte González, Alicia-will be generated-orcid-0000-0002-9972-0272-600engEducación Químicahttp://dx.doi.org/10.22201/fq.18708404e.2019.2.65090Ajai, J. T., Imoko, B. I., & Emmanuel, I. O. (2013). Comparison of the Learning Effectiveness of Problem-Based Learning ( PBL ) and Conventional Method of Teaching Algebra. Journal of Education and Practice, 4(1), 131–136. Retrieved from http://www. iiste.org/Journals/index.php/JEP/article/view/4053 Ali, R., Hukamdad, D., Akhter, A., & Khan, A. (2010). Effect of Using Problem Solving Method in Teaching Mathematics on the Achievement of Mathematics Students. Asian Social Science, 6(2), 67. https://doi.org/10.5539/ass.v6n2p67 Ashraf, S. S., Marzouk, S. A. M., Shehadi, I. A., & Murphy, B. M. (2011). An Integrated Professional and Transferable Skills Course for Undergraduate Chemistry Students. Journal of Chemical Education, 88(1), 44–48. https://doi.org/10.1021/ed100275y Bledsoe, K. E., & Flick, L. (2012). Concept Development and Meaningful Learning Among Electrical Engineering Students Engaged in a Problem-Based Laboratory Experience. Journal of Science Education and Technology, 21(2), 226–245. https:// doi.org/10.1007/s10956-011-9303-6 Chen, W. H. (2013). Teaching geometry through problem-based learning and creative design. Jurnal Teknologi (Social Sciences), 63, 123–127. Cowden, C. D., & Santiago, M. F. (2016). Interdisciplinary Explorations: Promoting Critical Thinking via Problem-Based Learning in an Advanced Biochemistry Class. Journal of Chemical Education, 93(3), 464–469. https://doi.org/10.1021/acs. jchemed.5b00378 Eaton, J. (2016). GNU Octave. Fakayode, S. O., King, A. G., Yakubu, M., Mohammed, A. K., & Pollard, D. A. (2012). Determination of Fe Content of Some Food Items by Flame Atomic Absorption Spectroscopy (FAAS): A Guided-Inquiry Learning Experience in Instrumental Analysis Laboratory. Journal of Chemical Education, 89(1), 109–113. https://doi. org/10.1021/ed1011585 Graovac, A., & Gutman, I. (1979). The Determinant of the Adjacency Matrix of a Molecular Graph. MATCH Communications in Mathematical and in COMputational Chemistry, 6, 49–73. Gron, L. U., Bradley, S. B., McKenzie, J. R., Shinn, S. E., & Teague, M. W. (2013). How To Recognize Success and Failure: Practical Assessment of an Evolving, First-Semester Laboratory Program Using Simple, Outcome-Based Tools. Journal of Chemical Education, 90(6), 694–699. https://doi.org/10.1021/ed200523w Gurses, A., Dogar, C., & Geyik, E. (2015). Teaching of the Concept of Enthalpy Using Problem Based Learning Approach. Procedia - Social and Behavioral Sciences, 197, 2390–2394. https://doi.org/10.1016/J.SBSPRO.2015.07.298 Gutman, I., & Vidovic, D. (2002). The Largest Eigenvalues of Adjacency and Laplacian Matrices, and Ionization Potentials of Alkanes. Indian Journal of Chemistry, 41A, 893–896. Hailikari, T. K., & Nevgi, A. (2010). How to Diagnose At‐risk Students in Chemistry: The case of prior knowledge assessment. International Journal of Science Education, 32(15), 2079–2095. https://doi.org/10.1080/09500690903369654 Hopkins, T. A., & Samide, M. (2013). Using a Thematic Laboratory-Centered Curriculum To Teach General Chemistry. Journal of Chemical Education, 90(9), 1162–1166. https://doi.org/10.1021/ed300438t Jansson, S., Söderström, H., Andersson, P. L., & Nording, M. L. (2015). Implementation of Problem-Based Learning in Environmental Chemistry. Journal of Chemical Education, 92(12), 2080–2086. https://doi.org/10.1021/ed500970y Jones, B. D., Epler, C. M., Tech, V., Bryant, L. H., Paretti, M. C., Jones, B. D., … Paretti, L. H. (2013). The Effects of a Collaborative Problem-based Learning Experience on Students’ Motivation in Engineering Capstone Courses. Interdisciplinary Journal of Problem-Based Learning, 7(2), 5–16. https://doi.org/10.7771/1541-5015.1344 Kerber, A., Laue, R., Meringer, M., Rücker, C., & Schymanski, E. (2014). Mathematical chemistry and chemoinformatics: Structure generation, elucidation and quantitative structure-property relationships. Mathematical Chemistry and Chemoinformatics: Structure Generation, Elucidation and Quantitative StructureProperty Relationships. https://doi.org/10.1515/9783110254075 Kolb, A. Y., & Kolb, D. A. (2012). Experiential Learning Theory. In Seel N.M. (Ed.), Encyclopedia of the Sciences of Learning (pp. 1215–1219). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4419-1428-6_227 Lin, Y. I., Son, J. Y., & Rudd, J. A. (2016). Asymmetric translation between multiple representations in chemistry. International Journal of Science Education, 38(4), 644–662. https://doi.org/10.1080/09500693.2016.1144945 Llorens-Molina, J.-A. (2010). El aprendizaje basado en problemas como estrategia para el cambio metodológico en los trabajos de laboratorio. Química Nova, 33(4), 994– 999. https://doi.org/10.1590/S0100-40422010000400043 Maplesoft. (2016). MAPLE. Marrero-Ponce, Y. (2003). Total and Local Quadratic Indices of the Molecular Pseudograph’s Atom Adjacency Matrix: Applications to the Prediction of Physical Properties of Organic Compounds. Molecules, 8(9), 687–726. https://doi. org/10.3390/80900687 Marrero-Ponce, Y., Garit, J., Torrens, F., Zaldivar, V., & Castro, E. (2004). Atom, AtomType, and Total Linear Indices of the “Molecular Pseudograph’s Atom Adjacency Matrix”: Application to QSPR/QSAR Studies of Organic Compounds. Molecules, 9(12), 1100–1123. https://doi.org/10.3390/91201100 Marrero-Ponce, Y., Khan, M. T. H., Casañola Martín, G. M., Ather, A., Sultankhodzhaev, M. N., Torrens, F., & Rotondo, R. (2007). Prediction of Tyrosinase Inhibition Activity Using Atom-Based Bilinear Indices. ChemMedChem, 2(4), 449–478. https://doi. org/10.1002/cmdc.200600186 Marrero Ponce, Y. (2004). Total and local (atom and atom type) molecular quadratic indices: significance interpretation, comparison to other molecular descriptors, and QSPR/QSAR applications. Bioorganic & Medicinal Chemistry, 12(24), 6351– 6369. https://doi.org/10.1016/J.BMC.2004.09.034 MATLAB. (2016). MATLAB. MATLAB. https://doi.org/10.1201/9781420034950 Mihalić, Z., & Trinajstić, N. (1992). A graph-theoretical approach to structure-property relationships. Journal of Chemical Education, 69(9), 701. https://doi.org/10.1021/ ed069p701 Murphy, P. M. (2007). Teaching Structure–Property Relationships: Investigating Molecular Structure and Boiling Point. Journal of Chemical Education, 84(1), 97. https://doi. org/10.1021/ed084p97 Pelligrino, J. W., & Hilton, M. L. (Eds.). (2012). Education for Life and Work. Washington: National Academies Press. https://doi.org/10.17226/13398 Ram, P. (1999). Problem-Based Learning in Undergraduate Instruction. A Sophomore Chemistry Laboratory. Journal of Chemical Education, 76(8), 1122. https://doi. org/10.1021/ed076p1122 Ross, A., & Willson, V. (2012). The Effects of Representations, Constructivist Approaches, and Engagement on Middle School Students’ Algebraic Procedure and Conceptual Understanding. School Science and Mathematics, 112(2), 117–128. https://doi. org/10.1111/j.1949-8594.2011.00125.x SageMath. (2016). SageMath Kernel. Sahin, M. (2010). Effects of Problem-Based Learning on University Students’ Epistemological Beliefs About Physics and Physics Learning and Conceptual Understanding of Newtonian Mechanics. Journal of Science Education and Technology, 19(3), 266–275. https://doi.org/10.1007/s10956-009-9198-7 Todeschini, R., & Consonni, V. (2009). Molecular Descriptors for Chemoinformatic. (R. Todeschini & V. Consonni, Eds.) (Vol. 41). 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