Electronic Journal of Science Education V4 N4- June 2000 - Kumar/Helgeson

Effect of Gender on Computer-Based Chemistry Problem Solving:
Early Findings


David D. Kumar
Florida Atlantic University


Stanley L. Helgeson
Professor Emeritus
The Ohio State University


Science education reform has emphasized the need for integrating computer technology into learning, teaching, and assessment. One possibility for providing better education in science would be to modify science teaching and learning with special emphasis in computer technology such as computer-based laboratories, interactive videos, simulations, intelligent tutors, the Internet, and the World Wide Web. While the combination of science and technology seems to be a promising approach to reforming science education, there are some inherent problems. Kahle and Meece (1994), in a synthesis of research, found gender differences in science achievement. Rowe (1993) pointed out systematic gender differences in the use of computers in the classroom, where "... girls are often not given appropriate support and contexts for learning about and with computers" (p. 238). The gender differences in computer use are more evident at the secondary level than in the elementary years (Hattie & Fitzgerald, 1988).

According to Rowe (1993), "computers are tools which can be used for a variety of purposes. However, in the absence of a broader perspective, many schools subsume them under mathematics/science curricula, and thus they take on an existing stigma of sex stereotypes" (p. 257). As Cheek and Agruso (1995) stated, "available research suggests that widespread use of these [computer] technological tools raises significant issues for females and underrepresented populations in mathematics, science and technology fields" (p. 75). Nevertheless, as reported in Rowe (1993), when asked "which subjects do you not like as much as you used to, now that you are using a computer?" 30% of the girls and 12% of the boys said mathematics, while only 11% of the girls said science compared to 30% of the boys (p. 255). According to Rockman (cited in Branscum), "we should start exploring ways to achieve equity using technology" (p. 88). In this context, how the gender differences in science education affect computer based science learning is a question that needs to be explored.


This study tested the effect of gender on high school students solving stoichiometric problems using a software called Hyperequation (Kumar, 1993) on the Macintosh computer platform. Hyperequation is a Hypercard (TM) based software for balancing chemical equations. Hyperequation is capable of registering student responses and providing immediate feedback, as well as allowing the teacher to access a record of student performance in the program. (See Kumar (1993) for a prototype of the Hyperequation software.) The original version of Hyperequation contains five previously tested and validated chemical equations listed below. The reliability coefficient of these five equations on the Hypercard is 0.73. See Kumar, White and Helgeson (1994), and Niaz and Lawson (1985) for more details.

Equation 1: H2SO4 + 2NaOH = Na2SO4 + 2H2O

Equation 2: 2HCl + Na2CO3 = 2NaCl + H2O + CO2

Equation 3: 2Fe(OH)3 + 3H2SO4 = Fe2(SO4)3 + 6H2O

Equation 4: 2H3PO4 + 3CaSO3 = Ca3(PO4)2 + 3SO2 + 3H2O

Equation 5: 2H2XO3 + 5H3ZO3 = 2HX + 5H3ZO4 + H2O


The sample consisted of sixty high school chemistry students (30 males and 30 females) who participated in this study on a voluntary basis. Students received instruction in balancing chemical equations as a part of their curriculum one month prior to the administration of the study. Also, they had completed a course on computer literacy the previous year.

The study was administered using a Macintosh computer and the Hyperequation software. The computer recorded the responses, time on task and number of attempts made by each student. Scoring was based on correctness of responses by assigning a one (1) for a right answer and a zero (0) for a wrong answer; and on rate of attempt (number of attempts per minute). The alpha level was 0.05.


T-test results revealed no significant differences between males and females in terms of correctness (T = 1.49, DF = 58, p = 0.14) and rate of attempt (T = 0.85, DF= 58, p = 0.40). Also, only a weak correlation was noted between correctness and rate of attempt (r = 0.06).

The overall means for correctness (maximum correct being 5) and rate of attempt is presented in Table 1.

Table 1

Overall Means for Correctness and Rate of Attempt

Gender Correctness Rate of Attempts

N = 30

2.07 1.8
  [1.08] [0.88]

N = 30

1.67 1.58
  [0.99] [1.10]

Note: Standard Deviations shown in brackets

Overall, correctness for male students (Mean = 2.07, SD = 1.08) was higher than that for females (Mean = 1.67, SD = 0.99) by an average of 30% and this difference is evident in Equations 2 and 4. See Table 2. Also, the overall rate of attempt for male students (Mean = 1.80; SD = 0.88) was higher than that for females (Mean = 1.28; SD = 1.10).

Table 2

Item Means for Correctness

Gender Eqn1 Eqn2 Eqn3 Eqn4 Eqn5 Overall

N = 30

0.9 0.83 0.13 0.17 0.03 2.07
  [.31] [.38] [.34] [.38] [.18] [1.08]

N = 30

0.9 0.6 0.68 0.13 0 2.07
  [.31] [.49] [.25] [.35]   [.99]

Note: Standard Deviations shown in brackets

However, in Equation 1 the mean score (0.90, SD = 0.31) is the same for both groups, and in Equation 3 female students (Mean = 0.68, SD = 0.25) outscored males (Mean = 0.13, SD = 0.34). The mean score for males in Equation 5, the most difficult of the five equations, is 0.03 (SD = 0.18) while that for females is zero. The performance of female students, particularly in Equation 5, demands further research.

Discussion and Summary

Considering the limited scope of this study, the results must be interpreted with caution. The study did not find any significant differences between male and female high school students solving stoichiometric chemistry problems using the Hyperequation software on Macintosh computers. Collins (1984) and Jackson (1988) in separate studies attributed increase in student achievement in computer-based science tasks to the availability of immediate feedback. As pointed out earlier, the Hyperequation software is programmed to provide immediate feedback, which might be contributing to narrowing any gender gaps. According to research, the quality of feedback has an effect on the "self-confidence" of females (Lenney, 1977) and consequently their performance in science learning tasks (Rowe, 1994). However, to what extent feedback influenced particularly the performance of female students in this study is an important question for further research. Also, the possibility of immediate feedback causing students simply to react rather than to think about the equations needs to be examined.


Branscum, D. (1992). Educators need support to make computing meaningful. It is time to wield technology wisely. Macworld, September 1992, pp. 83-88.

Cheek, D. W., & Agruso, S. (1995). Gender and equity issues in computer-based science assessment. Journal of Science Education and Technology, 4(1), 75-79.

Collins, M. A. J. (1984). Improved learning with computerized test. The American Biology Teacher, 46(3), 188-190.

Hattie, J. & Fitzgerald, D. (1988). Sex differences in attitudes, achievement and use of computers. Australian Journal of Education, 31, 3-26.

Jackson, B. (1988). A comparison between computer-based and traditional assessment tests, and their effects on pupil learning and scoring. School Science Review, 69(249), 809-815.

Kahle, J. B., & Meece, J. (1994). Research on gender issues in the classroom. In Gabel, D. L. (Ed.), Handbook of research on science teaching and learning. New York: Macmillan Publishing Company.

Kumar, D. D., White, A.L. & Helgeson, S.L. (1994). A study of the effect of HyperCard and pen-paper performance assessment methods on expert-novice chemistry problem solving. Journal of Science Education and Technology, 3(3), 187-200.

Kumar, D. D. (1993). Hyperequation. The Agora, 3, 8-9.

Lenney, E. (1977). Women's self-confidence in achievement setting. Psychological Bulletin, 84, 1-13.

Niaz, M. & Lawson, A.E. (1985). Balancing chemical equations: The role of developmental level and mental capacity. Journal of Research and Science Teaching, 22(1), 41-51.

Rowe, H. A. H. (1993). Learning with personal computers. Victoria, Australia: The Australian Council for Educational Research

About the authors...

Dr. David Kumar is Professor of Science Education at Florida Atlantic University. His research interests include computer applications in chemistry problem solving, and policy studies in science and technology education. He is co-editor of the book Science, Technology, and Society: A Sourcebook on Research and Practice published by Kluwer Academic/Plenum Publishing, New York (2000).

Dr. Stan Helgeson is Professor Emeritus of Science Education at The Ohio State University. He was President of the National Association for Research in Science Teaching for two terms, and Associate Director of the ERIC Clearinghouse for Science, Mathematics and Environmental Education.

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