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Comparison of Students’ Performance on Algorithmic, Conceptual and Graphical Chemistry Gas Problems

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Abstract

The purpose of this study was to determine whether there were significant differences in students’ performances amongst conceptual, algorithmic and graphical questions tests. Seventy-one eleventh-grade students were involved in this study. In order to assess students’ performance, conceptual, graphical and algorithmic questions tests were utilized. Students’ performances in each test were analyzed statistically. Statistical analysis using one-way ANOVA of student tests scores pointed to statistically significant differences amongst each of three test scores (P < 0.05) in favor of the conceptual test. Further analyses were conducted to compare one type of questions with others. From these comparisons, positive relationships were found between conceptual understanding and algorithmic understanding and between conceptual understanding and graphical understanding. Also, results obtained indicated that most of the students lack of graphical understanding. The results suggest that students need more training about graphical understanding.

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References

  • Ashmore AD, Frazer MJ, Casey RJ (1979) Problem solving and problem solving networks in chemistry. J Chem Edu 56:377–379

    Google Scholar 

  • Ates S, Stevens JT (2003) Teaching line graphs to tenth grade students having different cognitive developmental levels by using two different instructional modules. Res Sci Technol Edu 21(1):55–66

    Article  Google Scholar 

  • Beichner RJ (1994) Testing student interpretation of kinematics graphs. Am J Phys 62: 750–762

    Article  Google Scholar 

  • Berg CA, Smith P (1994) Assessing students’ abilities to construct and interpret line graphs: disparities between multiple-choice and free-response instruments. Sci Edu 78:527–554

    Article  Google Scholar 

  • Bilgin İ (2006) The effects of pair problem solving technique incorporating Polya’s problem solving strategy on undergraduate students’ performance in chemistry. J Sci Edu 7(2):101–106

    Google Scholar 

  • Bodner GM, Herron JD (2002) Problem-solving in chemistry. In: Gilbert JK, De John O, Justi R, Treagust DF, Van Driel JH (eds) Chemical education: towards research-based practice. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 235–266

    Google Scholar 

  • Bowen GM, Roth W-M, McGinn MK (1999) Interpretations of graphs by university biology students and practicing scientists: toward a social practice view of scientific representation practices. J Res Sci Teaching 36(9):1020–1043

    Article  Google Scholar 

  • Bunce DM (1993) Introduction: symposium: lecture and learning: are they compatible? J Chem Edu 70(3):179–180

    Google Scholar 

  • Chiu MH (2001) Algorithmic problem solving and conceptual understanding of chemistry by students at a local high school in Taiwan. Proc Nat Sci Council, ROC (D) 11(1):20–38

    Google Scholar 

  • Clement J. (1985) Misconceptions in graphing. Proceedings of the 9th conference of the international group for the psychology of mathematics education 1:369–375

    Google Scholar 

  • Forster PA (2004) Graphing in physics: Processes and sources of error in tertiary entrance examinations in Western Australia. Res Sci Edu 34:239–265

    Article  Google Scholar 

  • Frazer MJ, Sleet RJ (1984) A study of students’ attempts to solve chemical problems. Eur J Sci Edu 6:141–152

    Google Scholar 

  • Gabel DL, Bunce DM (1994) Research on problem solving: chemistry. In: Gabel DL (eds) Handbook of research on science teaching and learning. Macmillan Publishing Company, New York, pp 301–326

    Google Scholar 

  • Gabel D, Sherwood R, Enochs L (1984) Problem-solving skills of high school chemistry students. J Res Sci Teaching 21(2):221–233

    Article  Google Scholar 

  • Hadjidemetriou C, Williams JS (2001) Children’s graphical conceptions: Assessment of learning for teaching. Proceedings of the 25th conference of the international group for the psychology of mathematics education 3:89–96

    Google Scholar 

  • Leinhardt G, Zaslavsky O, Stein MK (1990) Function, graphs, and graphing: tasks, learning and teaching. Rev Edu Res 60:1–64

    Article  Google Scholar 

  • Lenton G, Stevens B, Illes R. (2000). Numeracy in science: pupils’ understanding of graphs. School Sci Rev 82(299):15–23

    Google Scholar 

  • Lin Q, Kirsch P, Turner R (1996) Numeric and conceptual understanding of general chemistry at a minority institution. J Chem Edu 73(10):1003–1005

    Google Scholar 

  • Lythcott J (1990). Problem solving and requisite knowledge of chemistry. J Chem Edu 67(3):248–252

    Google Scholar 

  • Mason DS, Shell DF, Crawley FE (1997) Differences in problem solving nonscience majors in introductory chemistry on paired algorithmic-conceptual problems. J Res Sci Teaching 34(9):905–923

    Article  Google Scholar 

  • McKennzie DL, Padilla MJ (1986) The construction and validation of the test of graphing in science (TOGS). J Res Sci Teaching 23(7): 571–579

    Article  Google Scholar 

  • Nakhleh MB (1993). Are our students conceptual thinkers or algorithmic problem solvers? J Chem Edu 70(1):52–55

    Google Scholar 

  • Nakhleh MB, Mitchell RC (1993) Concept learning versus problem solving: there is a difference. J Chem Edu 70(3):190–192

    Google Scholar 

  • Niaz M (1987). Relation between M-space of students and M-Demand of different items of general chemistry and its interpretation based upon the neo-Piagetian theory of Pascual-Leone. J Chem Edu 64:502–505

    Google Scholar 

  • Niaz M (1988). Manipulation of M-demand of chemistry problems and its effect on student performance: a neo-Piagetian study. J Res Sci Teaching 25:643–657

    Article  Google Scholar 

  • Niaz M (1989). The relation between M-demand, algorithms, and problem solving: a neo-Piagetian analysis. J Chem Edu 66:422–424

    Google Scholar 

  • Niaz M (1995). Progressive transitions from algorithmic to conceptual understanding in student ability to solve chemistry problems: a Lakatosian interpretation. Sci Edu 79:19–36

    Article  Google Scholar 

  • Niaz M, Robinson WR (1992). From ‘algorithmic mode’ to ‘conceptual gestalt’ in understanding the behavior of gases: an epistemological perspective. Res Sci Technol Edu 10:53–64

    Article  Google Scholar 

  • Niaz M, Robinson WR (1993). Teaching algorithmic problem solving or conceptual understanding: role of development level, mental capacity, and cognitive style. J Sci Edu Technol 2(2):407–416

    Article  Google Scholar 

  • Nurrenbern S, Pickering M (1987) Concept learning versus problem solving. Is there a difference? J Chem Edu 64(6):508–510

    Google Scholar 

  • Pascual-Leone J (1987) Organismic process for neo-Piagetian theories: a dialectic causal account of cognitive development. Int J Psychol 22:531–570

    Article  Google Scholar 

  • Piaget J (1985). The equilibration of cognitive structures: the central problem of intellectual development. University of Chicago Press, Chicago

    Google Scholar 

  • Pickering M (1990) Further studies on concept learning versus problem solving. J Chem Edu 67(3):254–255

    Google Scholar 

  • QCA (1998) Standards at key stage 3: Science. Report on the 1997 National Curriculum Assessments for 14-year-olds. Qualifications and Curriculum Authority, London

    Google Scholar 

  • Roth WM, McGinn MK (1998) Inscriptions: a social practice approach to “representations”. Rev Edu Res 68:35–59

    Article  Google Scholar 

  • Sawrey BA (1990). Concept learning versus problem solving: Revisited. J Chem Edu 67(3):253–254

    Article  Google Scholar 

  • Tsaparlis G, Kausathana M, Niaz M (1998). Molecular-equilibrium problems: manipulation of logical structure and M-Demand, and their effect on students performance. Sci Edu 82:437–454

    Article  Google Scholar 

  • Widjaja YB, Heck A (2003). How a realistic mathematics education approach and microcomputer-based laboratory worked in lessons on graphing at an Indonesian junior high school. J Sci Mathematics Edu Southeast Asia 26(2):1–51

    Google Scholar 

Download references

Acknowledgement

The author thanks to Dr. Mansoor Niaz from Universidad de Oriente for his proofreading and comments.

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  1. Fatih Faculty of Education, Department of Secondary Science and Mathematics Education, Karadeniz Technical University, 61335, Sogutlu-Trabzon, Turkey

    Bayram Coştu

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  1. Bayram Coştu
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Correspondence to Bayram Coştu.

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Coştu, B. Comparison of Students’ Performance on Algorithmic, Conceptual and Graphical Chemistry Gas Problems. J Sci Educ Technol 16, 379–386 (2007). https://doi.org/10.1007/s10956-007-9069-z

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  • DOI: https://doi.org/10.1007/s10956-007-9069-z

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