Modeling Evolution in the Classroom: An Interactive LEGO Simulation

Evolutionary theory is critical for a comprehensive understanding of biology, yet students often fail to grasp its underlying principles. This results partially from ineffective teaching; however, the use of interactive activities could alleviate this problem. In this guided investigation of evolutionary mechanisms, students use LEGO bricks to simulate how mutation, migration, genetic drift, and natural selection can affect the evolution of a population. This exercise was undertaken and assessed with college introductory biology students, but is also appropriate for advanced high school students

Barriers to Integration of Bioinformatics into Undergraduate Life Sciences Education

Bioinformatics, a discipline that combines aspects of biology, statistics, and computer science, is increasingly important for biological research. However, bioinformatics instruction is rarely integrated into life sciences curricula at the undergraduate level. To understand why, the Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE, “nibbles”) recently undertook an extensive survey of life sciences faculty in the United States. The survey responses to open-ended questions about barriers to integration were subjected to keyword analysis. The barrier most frequently reported by the ~1,260 respondents was lack of faculty training. Faculty at associate’s-granting institutions report the least training in bioinformatics and the least integration of bioinformatics into their teaching. Faculty from underrepresented minority groups (URMs) in STEM reported training barriers at a higher rate than others, although the number of URM respondents was small. Interestingly, the cohort of faculty with the most recently awarded PhD degrees reported the most training but were teaching bioinformatics at a lower rate than faculty who earned their degrees in previous decades. Other barriers reported included lack of student interest in bioinformatics; lack of student preparation in mathematics, statistics, and computer science; already overly full curricula; and limited access to resources, including hardware, software, and vetted teaching materials. The results of the survey, the largest to date on bioinformatics education, will guide efforts to further integrate bioinformatics instruction into undergraduate life sciences education

Demographics of survey respondents.

<p>The number of responses (y-axes) for each of the demographic variables (x-axes) on the survey, as follows: (A) Gender. (B) Race (People of Color and White); four categories in Race—American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or other Pacific Islander—were combined into People of Color (POC) due to very small sample numbers for each category. (C) Ethnicity (Hispanic-Latino, non-Hispanic/Latino). (D) Minority Serving (whether or not the respondent’s home institution is classified as minority-serving). (E) Highest Degree (highest degree earned: Bachelor’s, Master’s, Professional Degree, PhD). (F) Year Earned (year that the highest degree was earned; responses were grouped in the following bins: Before 1980, 1980 to 1989, 1990 to 1999, 2000 to 2009, and After 2009). (G) Training (level of bioinformatics training: None, Self-taught, Short workshop, Undergraduate/PostBacc training, Graduate class, and Graduate degree); four categories in Training—Undergraduate course, Undergraduate certificate, Undergraduate degree, and Post-baccalaureate certificate—were grouped together into “Undergrad” (undergraduate/post-baccalaureate training) due to small sample numbers in these categories. (H) Carnegie (Carnegie classification of the respondent’s home institution: Associate’s, Baccalaureate, Master’s, Doctoral). (I) Total Students (total number of students at the respondent’s home institution). (J) Total Undergraduates (number of undergraduates at the respondent’s home institution). (K) Undergraduate Majors (number of undergraduate majors in the respondent’s home department). (L) Faculty (number of faculty in the respondent’s home department).</p

Summary of bioinformatics skills ratings.

<p>The total number of responses (y-axes) by Likert-scale rating from 1 to 5 (x-axes)—1 being “Not at all important” to 5 being “Extremely important”—for each of the fifteen survey skills, S1 to S15, labeled in sequence from (A) to (O). As discussed in Results, these skills were divided into two broad categories: skills that just required familiarity (“knowing” skills: S1 to S4, S6, S8, S10), and those that required direct engagement (“practicing” skills: S5, S7, S9, S11 to S15).</p

Geographic distribution of NIBLSE survey respondents.

<p>The location (city/state) of each response to the survey was obtained using e-mail and/or IP addresses. The distribution of responses for the contiguous U.S. is shown (<i>n</i> = 1,081). A light circle represents one response at a particular location; a darker circle represents multiple responses at the same location (the darker the circle, the more responses). Note that the legend applies to the states themselves—e.g., there were more than seventy-five responses from California—and that there are no states with no responses. Responses (not shown) were also received from Alaska, Hawaii, Argentina, Australia, Canada, Denmark, France, Italy, Korea, New Zealand, Norway, Puerto Rico, the Republic of Poland, Switzerland, and the United Kingdom.</p

Mean Likert responses for S3 (<i>Statistics</i>) and S13 (<i>Scripting</i>).

<p>Mean Likert responses are shown for (A) S3 (<i>Statistics</i>) and (B) S13 (<i>Scripting</i>) for three categories: Carnegie (Carnegie Classification of the respondent’s home institution: Associate’s, Baccalaureate, Master’s, Doctoral), Year Earned (year that the highest degree was earned; responses were grouped in the following bins: Before 1980, 1980 to 1989, 1990 to 1999, 2000 to 2009, and After 2009), and Training (level of bioinformatics training: None, Self-taught, Short workshop, Undergraduate/PostBacc training, Graduate class, and Graduate degree). Means and <i>P</i> values from pairwise KS tests are reported in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196878#pone.0196878.ref034" target="_blank">34</a>].</p

Histogram of number of skills covered per syllabus.

<p>The number of syllabi that addressed the specified number of survey skills (S1 to S15). For example, ten syllabi addressed only one of the fifteen skills. On average, a syllabus covered 5.5 skills, with a median of 6 skills addressed. In aggregate, the submitted syllabi covered all fifteen skills, but no single syllabus covered more than thirteen out of the fifteen skills.</p

Importance of the fifteen bioinformatics skills as rated by survey respondents compared to coverage of the skills in the syllabi.

<p>Skills are shown with the proportion of survey responses rating the skill as either “Very Important” or “Extremely Important” (blue bars) and the proportion of submitted syllabi that exhibited evidence of the skill (grey bars). Skills requiring familiarity with a concept (“knowing” skills) are to the left of the vertical dashed line; skills requiring direct engagement (“practice” skills) are to the right. In their respective categories, skills are presented in order of decreasing proportion of survey responses rating the skill as Very or Extremely Important.</p

Responses by degree.

<p>Responses by degree.</p