Limits...
A study assessing the potential of negative effects in interdisciplinary math-biology instruction.

Madlung A, Bremer M, Himelblau E, Tullis A - CBE Life Sci Educ (2011)

Bottom Line: We have developed and assessed an integrative learning module and found disciplinary learning gains to be equally strong in first-year students who actively engaged in embedded quantitative calculations as in those students who were merely presented with quantitative data in the context of interpreting biological and biostatistical results.When presented to advanced biology students, our quantitative learning tool increased test performance significantly.We conclude from our study that the addition of mathematical calculations to the first year and advanced biology curricula did not hinder overall student learning, and may increase disciplinary learning and data interpretation skills in advanced students.

View Article: PubMed Central - PubMed

Affiliation: Biological Science, California Polytechnic State University, San Luis Obispo, CA 93407, USA. amadlung@pugetsound.edu

ABSTRACT
There is increasing enthusiasm for teaching approaches that combine mathematics and biology. The call for integrating more quantitative work in biology education has led to new teaching tools that improve quantitative skills. Little is known, however, about whether increasing interdisciplinary work can lead to adverse effects, such as the development of broader but shallower skills or the possibility that math anxiety causes some students to disengage in the classroom, or, paradoxically, to focus so much on the mathematics that they lose sight of its application for the biological concepts in the center of the unit at hand. We have developed and assessed an integrative learning module and found disciplinary learning gains to be equally strong in first-year students who actively engaged in embedded quantitative calculations as in those students who were merely presented with quantitative data in the context of interpreting biological and biostatistical results. When presented to advanced biology students, our quantitative learning tool increased test performance significantly. We conclude from our study that the addition of mathematical calculations to the first year and advanced biology curricula did not hinder overall student learning, and may increase disciplinary learning and data interpretation skills in advanced students.

Show MeSH
Examples of exercise and assessment material from the introductory biology microarray learning module. (A) Exercise questions related to statistical decision making from the passive math version, and (B) the corresponding active math version of the module. Assessment questions related to this material are provided in (C). All students received the same set of assessment questions. Complete classroom material and the assessment tools can be found at www.polyploidy.org/index.php/Microarray_analysis.
© Copyright Policy - creative-commons
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3046887&req=5

Figure 1: Examples of exercise and assessment material from the introductory biology microarray learning module. (A) Exercise questions related to statistical decision making from the passive math version, and (B) the corresponding active math version of the module. Assessment questions related to this material are provided in (C). All students received the same set of assessment questions. Complete classroom material and the assessment tools can be found at www.polyploidy.org/index.php/Microarray_analysis.

Mentions: At the end of the second lecture, students were randomly divided into two groups. Students in both groups were given a packet containing a detailed handout of the lecture material and one of two versions of a take-home assignment that differed in whether or not computational tasks had to be performed. One version focused on the broader biological concepts and uses of microarrays and how statistics are used to analyze and interpret microarray data. Students receiving this version were asked a series of questions throughout the exercise (see Figure 1A for examples) but were not asked to perform any mathematical computations. Learning outcomes for this packet included the interpretation of statistical test results and knowledge of important terms for data analysis. Here, we term this version the “passive math” version because the students did not perform any of the mathematical calculations related to the statistical tests. The second version of the exercise, termed the “active math” version, was identical in content to the passive math version except that the accompanying questions included a series of hands-on computational tasks in addition to questions related to the interpretation of biostatistics in the context of microarray experimentation. All computations were related to analyzing microarray data, and included the calculation of log-ratios and standard deviations, normalization of data, and the application of a t test, which the students performed using a simple handheld calculator (see Figure 1B for examples). Although the assignments were similar in overall length and took the instructor approximately the same amount of time to complete, it is possible that the active math version took students longer and required more active engagement with the material than did the passive math version because of the need to perform statistical calculations. Take-home assignments were collected and graded for effort and completeness. We did not grade for mathematical accuracy because this could apply only to the assignment for the active math group. Based on the grades earned on the take-home assignments, students in both groups performed comparably on their respective homework (these data were not further evaluated for this study).


A study assessing the potential of negative effects in interdisciplinary math-biology instruction.

Madlung A, Bremer M, Himelblau E, Tullis A - CBE Life Sci Educ (2011)

Examples of exercise and assessment material from the introductory biology microarray learning module. (A) Exercise questions related to statistical decision making from the passive math version, and (B) the corresponding active math version of the module. Assessment questions related to this material are provided in (C). All students received the same set of assessment questions. Complete classroom material and the assessment tools can be found at www.polyploidy.org/index.php/Microarray_analysis.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3046887&req=5

Figure 1: Examples of exercise and assessment material from the introductory biology microarray learning module. (A) Exercise questions related to statistical decision making from the passive math version, and (B) the corresponding active math version of the module. Assessment questions related to this material are provided in (C). All students received the same set of assessment questions. Complete classroom material and the assessment tools can be found at www.polyploidy.org/index.php/Microarray_analysis.
Mentions: At the end of the second lecture, students were randomly divided into two groups. Students in both groups were given a packet containing a detailed handout of the lecture material and one of two versions of a take-home assignment that differed in whether or not computational tasks had to be performed. One version focused on the broader biological concepts and uses of microarrays and how statistics are used to analyze and interpret microarray data. Students receiving this version were asked a series of questions throughout the exercise (see Figure 1A for examples) but were not asked to perform any mathematical computations. Learning outcomes for this packet included the interpretation of statistical test results and knowledge of important terms for data analysis. Here, we term this version the “passive math” version because the students did not perform any of the mathematical calculations related to the statistical tests. The second version of the exercise, termed the “active math” version, was identical in content to the passive math version except that the accompanying questions included a series of hands-on computational tasks in addition to questions related to the interpretation of biostatistics in the context of microarray experimentation. All computations were related to analyzing microarray data, and included the calculation of log-ratios and standard deviations, normalization of data, and the application of a t test, which the students performed using a simple handheld calculator (see Figure 1B for examples). Although the assignments were similar in overall length and took the instructor approximately the same amount of time to complete, it is possible that the active math version took students longer and required more active engagement with the material than did the passive math version because of the need to perform statistical calculations. Take-home assignments were collected and graded for effort and completeness. We did not grade for mathematical accuracy because this could apply only to the assignment for the active math group. Based on the grades earned on the take-home assignments, students in both groups performed comparably on their respective homework (these data were not further evaluated for this study).

Bottom Line: We have developed and assessed an integrative learning module and found disciplinary learning gains to be equally strong in first-year students who actively engaged in embedded quantitative calculations as in those students who were merely presented with quantitative data in the context of interpreting biological and biostatistical results.When presented to advanced biology students, our quantitative learning tool increased test performance significantly.We conclude from our study that the addition of mathematical calculations to the first year and advanced biology curricula did not hinder overall student learning, and may increase disciplinary learning and data interpretation skills in advanced students.

View Article: PubMed Central - PubMed

Affiliation: Biological Science, California Polytechnic State University, San Luis Obispo, CA 93407, USA. amadlung@pugetsound.edu

ABSTRACT
There is increasing enthusiasm for teaching approaches that combine mathematics and biology. The call for integrating more quantitative work in biology education has led to new teaching tools that improve quantitative skills. Little is known, however, about whether increasing interdisciplinary work can lead to adverse effects, such as the development of broader but shallower skills or the possibility that math anxiety causes some students to disengage in the classroom, or, paradoxically, to focus so much on the mathematics that they lose sight of its application for the biological concepts in the center of the unit at hand. We have developed and assessed an integrative learning module and found disciplinary learning gains to be equally strong in first-year students who actively engaged in embedded quantitative calculations as in those students who were merely presented with quantitative data in the context of interpreting biological and biostatistical results. When presented to advanced biology students, our quantitative learning tool increased test performance significantly. We conclude from our study that the addition of mathematical calculations to the first year and advanced biology curricula did not hinder overall student learning, and may increase disciplinary learning and data interpretation skills in advanced students.

Show MeSH