Chinese Journal of Science Education 2002,, 135-156 2002, 10(2), 135-156 1 2 1 2 90 7 9 91 1 31 91 3 19 1 260 r.814p<.001 r.898p<.001.813.965 KR 20.60.63 r.482p<.01 r.435p<.01 effect size R 2.343 R 2.172.332
136 Project 2061 Benchmarks for Science LiteracyAmerican Association for the Advancement of Science, [AAAS], 1994 2001 p. 5, 2001 problem solving Gagne1970 Gage1986 Fisher1990 1 1, 1993
137 2000 2000 Niedelman1990 Smith 1991 1. (problem context) problem structure social factors 2. (affect) (experience) (domain-specific knowledge) CPS general problemsolving knowledge) Creative Problem Solving CPS CPS Wallace Osborn (the problem solver s knowledge Parnes TreffingerIsaksen Dorval mustbe adequate, organized, accessible, inte grated, and accurate) (other Parnes personalcharacteristics) Osborn-Parnes CPSOsborn-Parnes Jonassen2000 traditions of CPS CPS nature of problem representation individual differences Parnes, 1987 context 1999
138 1 9 () 90 2 90 5 260 136 124 SPSS 8.0 (Statistical Package for the Social Sciences) Borg & Gall, 1989 semi- coding structural interview () 89 1 89 11 () pilot studies Parnes CPS () 89 12 90 1
139 1 89/06 89/08 89/10 6 /300 6 /276 4 /175 1 r.911 p<.001 r.944p<.001 50 5 44 100.905.965 (2000).806.931.917.960 p<.200 50 r.834p<.001 100 r.832p<.001 5 47.813.901 89 9 11.721.923.751.886 r.814 89 6 10 p<.001 r.898 pilot tests p<.001
140 ().55 20 () 1 Likert-type 1993, 2000, 1999, 1989 Foundations of Astronomy 40 Seeds, 1990.60 (n = 204)KR 20 =.76 (n = A B428)KR 21 =.70 (n = 428) C D 1993, 1972 C A B C D 89 9 11 A B () KR 20 =.60 (n = 133) KR 20 =.63 (n = 127) Hatcher Stepanski (1994)
141 Size, ES 5 Cohen s effect size index r (Cohen, 1988, p.77)glass, McGaw, Smith (1981)Glass Hopkins (1996, p. 449) effect size () (the difference between the mean of experimental and control groups) Pearson product-moment Cohen s effect size index d (1988, p.20) correlation stepwise Cohen (1988) multiple regression r 2 (proportion of variance) 1. effect size index r (Cohen, 1988, p.77) Nix Barnette 1998 Effect Size r 2 or R 2 Cohen1988 2. noise 0.6 0.1 0.3 0.5 p. 78-83 3. () 1995 4 1995 Cohen1994 significance tests 2 M81.02SD18.34 Effect M64.87SD18.38
142 2 / 50 50 100 133 M SD M 127 SD 260 M SD 37.65 11.04 36.28 08.70 36.98 9.97 43.37 11.04 28.59 11.04 36.15 13.27 81.02 18.34 64.87 18.38 73.13 20.03 3 133 127 260 100 100 100 72.59 55.20 64.10 12.27 15.68 16.50 4 260 40 21.92 5.07 4 21.92SD5.07 pilot test 3 M72.59 SD12.27 M55.20SD 15.68
143 5 n260.293**.507**.482**.331**.408**.435** ** p<.01 6 n260 1.000**.482**.435**.231** 1.000**.288**.125** 1.000**.197** 1.000** ** p<.01 r.331 () r.507 r.408 5 () (r.482) (r.43 5) () r.293
144 7 Model R R square F p 1.482.232 78.062**.000 2.573.328 62.740**.000 3.589.343 44.546**.000 Model B SE Beta t p 1 Constant 35.638 4.381 8.134**.000.585.066.482 8.835**.000 2 Constant 14.885 5.350 2.782*.006.472.065.389 7.284**.000 1.277.211.323 6.053**.000 3 Constant 9.006 5.835 1.543.124.461.064.380 7.158**.000 1.191.212.301 5.613**.000 2.832 1.175.125 2.410*.017 * p<.05** p<.001 r.573r 2.328p<.001 3 6 r.482p<.01 r.435 p<.01 r.231 r.589r 2.343p<.001 7 p<.01 r.12 1 5.288 2 3 2 3 1 2 r.482 R 2.232p<.001 2
145 8 ** p<.01 n260 1.000**.293**.331**.217** 1.000**.288**.125** 1.000**.197** 1.000** 9 Model R square F p 1.331.110 31.751**.000 2.390.152 23.102**.000 3.415.172 17.736**.000 Model B SE Beta 1 Constant 22.703 2.600 8.730**.000.651.116.331 5.635**.000 2 Constant 17.017 2.992 5.687**.000.529.118.269 4.483**.000.131.036.216 3.603**.000 3 Constant 13.653 3.262 4.186**.000 * p<.05** p<.001.479.119.244 4.044**.000.124.036.205 3.450**.001 1.620.657.114 2.468*.014 t p 14.89+0.47* +1.28* 3 9.01+0.46* +1.19* +2.83*
146 10 ** p<.01 n260 1.000**.507**.408**.186** 1.000**.288**.125** 1.000**.197** 1.000** 5 () 10 r.507p<.01 r.408p <.01 () 8 r.186p<.01 r.293p<.01 r.331 p<.01 r.217 r p<.01.288 1 r.125.288 r.507r 2.257p<.001 2 1 r.576 R 2.332p<.001 11 r.331r 2.110p<.001 2 r.390r 2.152 p<.001 3 1 2 r.415r 2.172p<.001 ( 9) () 1 2 3
147 11 Model R R square F p 1.507.257 89.150**.000 2.576.332 63.764**.000 Model B SE Beta t p 1 Constant 10.025 2.857 3.509**.001.408.043.507 9.442**.000 2 Constant -2.132 3.536 -.603.547.341.043.424 7.972**.000.748.139.286 5.365**.000 ** p<.001 12 9 6 18 6 S2 1. 3.. S2 S3 S5 S4 2. 4. S1 S1
148 13 7 6 11 S4 12 () 1. S1 S3 2. S1 S2 3. S3 S4 13 Thorsland & Novak, 1971; Wesney, 1977; Rowell, Gustafson Guilbert, 1997 2000 Barba1990
149 Coleman Shore1991 Ohanian, 1997; Wagner, 2001; O'Connell, 2000 1997 Barba1990 Coleman Shore 1991 9.01 100 2000
150 2 2 () () NSC 89-2511-S- 003-144 1. 1972 2. 2000
151 16. Cohen, J. (1988). Statistical power analysis for 3. 1997 the behavioral sciences (2nd ed.). Hillsdale, New Jersey: Lawrence Erlbaum Associate, Inc., 5, 80-92 17. Cohen, J. (1994). The earth is round (p<.05). 4. 1995 American Psychologist, 49, 997-1003. 18. Coleman, E. B., & Shore, B. (1991). Problemsolving processes of high and average 5. 1989 performers in physics. Journal for the Education of the Gifted, 14, 366-379. 6. 1993 7. 2000 20. Gage, B. A. (1986). An analysis of problem, 8, 35-56 8. 2001 9. 1999 10. 2000 22. Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology 11., 216, 3-16 1999 12. CA: Sage. 1993, 40, 1-14 13. American Association for the Advancement of Science (1994). Benchmarks for science literacy. New York: Oxford University Press. 14. Barba, R. H. (1990). A comparison of expert and novice earth and space science teachers' problem solving abilities. Unpublished doctoral dissertation, The Pennsylvania State University. 15. Borg, W. R., & Gall, M. D. (1989). Educational research (5th ed.). London: Longman Group Ltd. 19. Fisher, R. (1990). Teaching children think. Oxford: Basil Blackwell. solving processes used in college chemistry quantitative equilibrium problem. Unpublished doctoral dissertation, The University of Maryland. 21. Gagne, R. M. (1970). The conditions of learning. London: Holt-Saunders. (3rd ed.). Needham Heights, MA: Allyn & Bacon. 23. Glass, G. V., McGaw, B., & Smith, W. (1981). Meta-analysis in social research. Beverly Hills, 24. Hatcher, L., & Stepanski, E. J. (1994). A step-bystep approach to using the SAS system for univariate and multivariate statistics. Cary, NC: SAS Institute. 25. Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48, 63-85. 26. Niedelman, M. S. (1990). An investigation of transfer to mathematics of a problem-solving strategy learned in earth science. Dissertation Abstracts International, 51(11), 3622. 27. Nix, T. W., & Barnette, J. J. (1998). The data
152 analysis dilemma: Ban or abandon. A review of null hypothesis significance testing. Research in the Schools, 5, 3-14. 28. O'Connell, S. (2000). Introduction to problem solving: Strategies for the elementary math classroom. Westport, CT: Heinemann. 29. Ohanian, S. (1997). Math that measures up. American School Board Journal, 184, 25-27. 30. Parnes, S. J. (1987). Visioneering-state of the art. Journal of Creative Behavior, 21, 283-299. 31..Rowell, P. M., Gustafson, B. J., & Guilbert, S. M. (1997). Problem-solving through technology: An interpretive dilemma. Alberta Journal of Educational Research, 43, 86-98. 32. Seeds, M. A. (1990). Foundations of astronomy. Belmont: Wadsworth Publishing Company. 33. Smith, M. U. (Eds.) (1991). Toward a unified theory of problem solving. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. 34. Thorsland, M. N., & Novak, J. D. (1971). The identification and significance of intuitive and analytic problem solving approaches among college physics students. Science Education, 58, 245-265. 35. Wagner, E. P. (2001). A study comparing the efficacy of a mole ratio flow chart to dimensional analysis for teaching reaction stoichiometry. School Science and Mathematics, 101, 10-22. 36. Wesney, J. (1977). An analysis of influence of prior cognitive development in physics and in mathematical reasoning on concept attainment in the study of mechanics in introductory college physics. Dissertation Abstracts International, 38, 5379.
153 1. 2. 3. 25 4. Apollo 3 Apollo 24 Apollo Apollo
154 24 25 25 24 46 20 30
155 Exploring the Interrelationship Between Tenth-Graders Problem-Solving Abilities and Their Prior Knowledge and Reasoning Skills in Earth Science Chia-Ling Wu 1 and Chun-Yen Chang 2 1 Yang-Ming Junior High School, Chang-Hua, Taiwan 2 Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan Abstract The purpose of this study was to develop the Problem Solving Ability Test (PSAT) and a matching Domain-Specific Knowledge Test (DSKT) that covers the basic knowledge central to the PSAT, with the aims to investigate the interrelationship between students problem solving ability (PSA) and their domain-specific knowledge (DSK) as well as reasoning skills (RS) in the area of earth science. The PSAT was constructed based on the Creative Problem Solving (CPS) model, which emphasizes students divergent-thinking ability (DTA) and convergent-thinking ability (CTA) subscales. The sample consisted of 260 tenth-grade students enrolled at a national senior high school in the eastern region of Taiwan. Quantitative analyses employed Pearson-product-moment correlation and stepwise multiple regression method. Qualitative data were acquired through semi-structured interviewing with coding and triangulation procedures to explore students perceptions toward the PSAT and DSKT in greater depth. Results are as follows: (a) The overall scores of the PSAT are highly correlated with both the subscales of DTA (.814p<.001) and CTA (r.898p <.001) with an inter-rater reliability ranged from.813 to.965. The reliability of the DSKT (KR 20 ) ranged from.60 to.63; (b) A significantly positive correlation existed between students PSA and their DSK (r.482p<.01) and RS (r.435p<.01) with medium to large effect sizes. In addition, students DSK, RS and attitudes toward problem solving (ATPS) significantly predict their performance on the PSAT (R 2.343). Students DSK and RS also predict their performance on the DTA and CTA subscales of the PSAT (R 2.172.332), approaching large effect sizes; (c) Students RS are more significantly correlated with their DTA (large effect size) and students DSK are more significantly correlated with their CTA (toward large effect size); (d) Semistructured interviews revealed that students perceived knowledge, attitudes and experiences are essential in scoring high on the DSKT; while students thought that knowledge, attitude, thinking, and experiences were fundamental to better performance on the PSAT. The results of qualitative analyses are generally in line with the findings of quantitative analyses. It is, therefore, suggested that teachers should be able to improve students problem solving performance through the enhancement of students domain-specific
156 knowledge and reasoning skills in earth science classrooms. Moreover, we should emphasize students reasoning skills in developing divergent-thinking abilities, while stressing domain-specific knowledge in increasing students convergent-thinking ability. Key words: Secondary Education, Prior Knowledge, Earth Science, Reasoning Skills, Problem Solving.