Improving undergraduate STEM education: The efficacy of discipline-based professional development

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Science Advances  15 Feb 2017:
Vol. 3, no. 2, e1600193
DOI: 10.1126/sciadv.1600193
  • Fig. 1 Percentage of 2004, 2009, and 2012 survey respondents who report spending more than 20% of class time on student activities, questions, and discussions (light green bars) and percentage of respondents who report active learning (orange bars).
  • Fig. 2 Pattern of cluster variable mean values for the three faculty types demonstrating strong differences in reported behavior between groups; data were combined across all three survey years (2004, 2009, and 2012).

    The blue line represents education-focused faculty, the red line represents geoscience research–focused faculty, and the green line represents teaching faculty. Error bars represent 95% confidence interval (table S8).

  • Fig. 3 Percentage of 2012 survey respondents (n = 1642) within each cluster (education-focused faculty, geoscience research–focused faculty, and teaching faculty) classified as reporting traditional lecture (green), active lecture (yellow), and active learning teaching strategies (gray).
  • Fig. 4 RTOP scores obtained from observations of faculty who had (i) neither participated in a Cutting Edge workshop nor used the website, (ii) used the website only, or (iii) both used the website and participated in a Cutting Edge workshop.

    The full range of scores for each group is indicated by a thin vertical line. The interquartile range (approximately 50% of scores) is represented with a box with a horizontal line delineating the median. *Bonferroni post hoc tests from an analysis of variance (ANOVA) test (F = 22.6; P < 0.001) indicate a significant difference in mean RTOP scores between group 3 [those who use the Cutting Edge website and attend workshops (M = 48.2, SD = 16.2)] and the other two groups [group 1 (M = 33.1, SD = 13.6); P < 0.001; group 2 (M = 37.2, SD = 13.1); P < 0.001].

  • Table 1 Interactive class time and teaching strategies versus survey year.
    Survey yearClass time spent on student
    activities, questions,
    and discussions
    Teaching strategy
    Less than or equal
    to 20% (n)
    More than
    20% (n)
    Total (n)Traditional
    lecture (n)
    lecture (n)
    learning (n)
    Total (n)
  • Table 2 Summary of qualitative interview cases sampled for the retrospective study.
    YearsProjectTotal number
    of interviews
    Number of interviews
    included in the
    retrospective study
    2005Phone interviews of 2002–2004
    workshop participants about workshop impact
    2005–2009Phone interviews of participants
    and nonparticipants about website
    2007Face-to-face interviews of both
    Cutting Edge participants and nonparticipants

Supplementary Materials

  • Supplementary material for this article is available at

    fig. S1. Venn diagram showing survey year and number of participant responses for each survey.

    fig. S2. Frequency by survey year of percentage spent on interactive class time.

    table S1. Frequency by survey year of percentage spent on interactive class time.

    table S2. Percentage of survey respondents reporting that more than 20% of class time is interactive by year.

    table S3. Percentage of faculty group reporting that more than 20% of class time is interactive by year.

    table S4. Percentage of responses reporting that more than 20% of class time is interactive for each teaching strategy by year.

    table S5. Percentage of responses reporting that more than 20% of class time is interactive for each course level by year.

    table S6. Spearman correlation between teaching strategy and other measures of engaged teaching.

    table S7. Faculty types by survey year.

    table S8. Cluster variables by faculty type: mean value of cluster variable (95% confidence interval).

    table S9. Reporting of teaching strategy and interactive class time by faculty type.

    table S10. Reporting of other measures of engaged teaching by faculty type.

    table S11. Background and teaching characteristics by faculty types from the 2012 survey.

    table S12. Relationship between faculty type and institution type in 2012 survey responses determined using cross-tabulation analysis in SPSS 22.

    table S13. Engagement in learning about pedagogy characteristics by faculty type from the 2009 and 2012 surveys.

    table S14. Logistic regression model for predicting more than 20% of class time on student activities, questions, and discussion.

    table S15. Logistic regression results for predicting active learning (versus active lecture).

    table S16. RTOP score ranges, quartiles, mean, and medians as reported for 203 observations.

    table S17. Cutting Edge participation by faculty type.

    table S18. Growth in Cutting Edge participants in U.S. geoscience faculty population and in survey sample.

    Interview protocols

    Coding book for qualitative analysis

  • Supplementary Materials

    This PDF file includes:

    • note S1. Details of the derivation of invariant-based reconstruction.
    • note S2. Error estimates for observables from sampled invariant density.
    • note S3. Reconstruction evaluation.
    • note S4. Moderate influence of link density.
    • note S5. Reconstructing homogeneous and heterogeneous networks.
    • note S6. Reconstruction of systems near fixed points.
    • note S7. Reconstruction of chaotic systems.
    • note S8. Performance compared with available standard baselines.
    • note S9. Distinguishing activating from inhibiting interactions.
    • note S10. The effect of missing information.
    • note S11. Model descriptions.
    • note S12. The effect of various driving conditions on reconstruction quality.
    • note S13. Compressed sensing.
    • fig. S1. Approximating the center of mass of invariant densities by the sample mean.
    • fig. S2. Sparser networks require fewer experiments for robust reconstruction.
    • fig. S3. Reconstruction is robust across network topologies.
    • fig. S4. The quality of reconstruction increases with the number of experiments
      for a network of genetic regulators.
    • fig. S5. Reconstruction of a network of Rössler oscillators exhibiting chaotic dynamics.
    • fig. S6. Comparison of reconstruction quality across different approaches.
    • fig. S7. Comparison of reconstruction quality against transfer entropy.
    • fig. S8. Separate reconstruction of activating and inhibiting interactions enhances the quality of reconstruction.
    • fig. S9. Quality of reconstruction (AUC score) decreases gradually with the fraction of hidden units in the network.
    • fig. S10. Quality of reconstruction increases as driving signals overcome noise and finite sampling effects.
    • References (48–50)

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