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*Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, Neuroscience Training Program, Center for Neuroscience, University of Wisconsin School of Medicine and Public Health, Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, and Center for Biology Education, University of Wisconsin, Madison, WI 53705
Submitted November 3, 2007; Revised January 24, 2008; Accepted January 29, 2008
Monitoring Editor: Dennis Liu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The overarching learning goals of an imaging teaching unit early in the introductory biology curriculum are that students understand the integrated landscape of imaging science requires preparation through multidisciplinary studies and that students are better prepared for this rapidly advancing field whether as consumers or future providers of molecular medicine (Dzik-Jurasz, 2003).
Biological imaging is a technique that can bring quantification into biology education and offers new insights into biological structure and function through visualization of processes at the nano, molecular, cellular, and system-level scale. Innovations in biological imaging require translation from the laboratory bench to the undergraduate, graduate, and medical classroom. However, it is challenging to provide a context in the undergraduate curriculum that will highlight the multidisciplinary scope of imaging science and its integrated continuum with biology. Furthermore, instruction in biological imaging must be able to overcome student misconceptions (Table 1), to engage students with content that is interesting to all students but requires little background knowledge, and to encourage learning gains that are self-directed within an active-learning framework. Here, we introduce a teaching unit on biological imaging developed for three 1-h class periods in an introductory undergraduate biology course, Ways of Knowing Biology (WOK). We show that our course design, which drew from NIH Roadmap initiatives, was significantly associated with student learning gains in understanding key concepts and developing core skills in imaging.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Because imaging misconceptions develop where education does not keep up with technological advances (Hawkins and Dunn, 1996), we identified concepts in the imaging and biology literature that would help students integrate biology and modern imaging methods, that were reported as misconceptions, or that were recommended for incorporation into the biology curriculum (Sullivan, 2000; Hawkins and Dunn, 1996; Provenzale and Mukundan, 2005; Illes et al., 2006; Schnell et al., 2007). These concepts provided a basis for primary learning goals and specific learning outcomes (Table 1). We incorporated learning goals into student activities to delineate measurable criteria for assessment purposes. The activities to achieve these goals took the form of mini-lectures that progressed in scale from the nanoscale to system level scale, NIH ImageJ analysis, in-class NIH Roadmap activities, and the Gillespie (Muller et al., 2003), Student Assessment of Learning Gains (SALG; Wisconsin Center for Educational Research, 1997), and pre/postassessments.
In-class activities and course content were developed and unified around the NIH Roadmap initiatives. More specifically, assessment activities in nanoimaging, molecular imaging, and medical imaging were drawn from nanomedicine, tissue engineering in regenerative medicine, and nuclear medicine. The course activities progressed in biological and imaging scale from nano through macroscale. As a nanoimaging activity, students quantified electron microscopy images of carbon nanofibers arranged into the shape of Bucky Badger (NanoBucky) (Hamers, 2007). The molecular imaging activities involved interpreting fluorescent microscopy images of fluorescently labeled bovine pulmonary artery endothelial cells (available in NIH ImageJ), enhanced green fluorescent protein (eGFP) expressing stem cells (Zwaka and Thomson, 2005), and macroscopic fluorescent images of the eGFP rabbit Alba (Stewart, 2006). System level activities required clinical evaluation of a NIH positron emission tomography (PET) exercise (NIH, 2007) supplemented with a patient case study based on 18-FDG PET images collected at the University of Wisconsin-Madison Cyclotron and PET Research Center. In addition, students evaluated computed tomography (CT) images of Phineas Gage's skull (Ratiu and Talos, 2004) and MRI images of human brain in two-dimensional (2D) images, three-dimensional (3D) virtual reality (VR), and physical 3D formats. Virtual reality models of human brain and Phineas Gage's skull were generated and then printed in 3D as physical stereolithograph models at the UW-Madison New Media Center (Kelley, 2007). We viewed the VR brain and skull models in the virtual reality markup language (VRML) format with VRMLView (Systems in Motion; Norway).
To provide a better understanding of the actual lessons and student activities, we will provide an in-depth description and active-learning approach for the NanoBucky exercise. Students were asked to bring in their laptops for this in-class activity. Students organized themselves into groups and used the NIH ImageJ applet (http://rsb.info.nih.gov/ij/applets.html), which did not require prior installation but did require an Internet-enabled classroom. Students were provided with NIH ImageJ instructions to use NIH ImageJ applet software from the ImageJ Documentation Wiki (http://imagejdocu.tudor.lu/imagej-documentation-wiki). Students actively learned how to initiate the software, load an image, and analyze the height of NanoBucky. This activity familiarized students with nanomaterials that are being developed as part of the NIH Roadmap initiative and enabled students to understand the nanoimaging scale by calculating the height of NanoBucky based on the scale legend provided in the image (http://hamers.chem.wisc.edu/research/nanofibers/index2.htm); to understand how imaging provided scientists with ways of viewing and assessing nanomaterials and, in the future, nanodevices for quality control; and to recognize that imaging is a quantitative tool in biology by observing, measuring, and interpreting biological images. The NanoBucky activity was completed in 
30 min.
All the course activities aligned directly with the learning goals (Table 1) and exemplified how biological images advance the NIH Roadmap initiatives. The relationship between biological scale and imaging scale was exemplified by nanoimages of NanoBucky, molecular images of fluorescent stained endothelial cells available in Image J (Abramoff et al., 2004), eGFP labeled human embryonic stem cells, the eGFP rabbit Alba, MRI images of brain, and CT images of Phineas Gage's skull. Students could understand that imaging provides ways of knowing biological structure and function by interpreting the intensity of eGFP labeled gene expression during human embryonic stem cell differentiation and images of brain function using 18-FDG PET and functional MRI. The use of MRI and CT images to create stereolithograph models of brain and skull, respectively, exemplified the broader application of images and novel visualization approaches. In addition, students could develop their evaluation skills by comparing 2D images, 3D VR, and stereolithograph models in terms of visual information quality and biological utility. Computer skills could be developed by using the NIH ImageJ software to quantify NanoBucky's height and intensity and to apply this knowledge when interpreting fluorescently labeled endothelial and stem cells. With NanoBucky, students could gain an understanding that nanoimaging of nanomaterials influences the development of nanodevices, which have applications in nanomedicine; that molecular imaging of human embryonic stem cells can inform tissue engineering, which can advance regenerative medicine; and that PET imaging with radioactive tracers can influence nuclear medicine.
Diversity
This course addressed diversity in learning styles, but it was much more challenging to simultaneously address gender, cultural, and socioeconomic diversity. Diversity in students' educational backgrounds was accounted for in this teaching unit by incorporating multiple modes of teaching and assessment activities. Mini-lectures provided background information that minimized discrepancies in educational background. Audiovisual aids were used to clarify concepts in an interesting and understandable manner. The video "Powers of Ten" introduced the concept of scale, and a movie clip of the "Hulk" illustrated fluorescence. Using NIH ImageJ, students quantified images and observed how images can be utilized using hand-held models of a brain and of Phineas Gage's skull. We accounted for different test-taking styles by using a variety of formats to assess learning gains: oral discussion, written answers, online and in-class surveys, and online pre- and postcourse assessments. To address gender and cultural diversity, the significant contributions of minority and female scientists from various countries were specifically incorporated into the mini-lectures. For example, the work of the female Polish chemist and physicist Marie Curie and English biophysicist Rosalind Franklin were highlighted for the contributions to nuclear medicine and genetics, respectively. To address socioeconomic diversity, we built upon the common campus culture held by all UW-Madison students and incorporated the work of scientists who made contributions to imaging and who were either from the state of Wisconsin or were affiliated with UW-Madison. For example, we highlighted the work of Raymond Damadian, who attended UW-Madison on a Ford Foundation Scholarship as an undergraduate and went on to invent and develop the first MRI scanner, Indomitable.
Active Learning
Students were engaged in learning actively in multiple ways. For example, visualization of biological processes using images engaged students in scientific thinking. By viewing and analyzing nanoimages, molecular images, and medical images, students could gain an understanding of the relationship between biological and imaging scale and realize that biological science and imaging science are related by scale. By viewing and analyzing images with fluorescent probes and radionuclear markers, students were encouraged to understand how images provided ways of knowing biological structure and function. When introducing different imaging topics, we selected interesting images that students could observe, quantify, and interpret. As examples, students used a freely available software program, NIH ImageJ (Abramoff et al., 2004), to quantify images and evaluated image visualization and utility by comparing 2D slices of brain and of Phineas Gage's skull with 3D VR and printed 3D stereolithograph models. Through introduction of NIH ImageJ analysis software, students could extend their conceptual understanding of imaging analysis into a technical skill using real data. By evaluating images, students understood how biological images advanced nanomedicine, regenerative medicine, and nuclear medicine and contributed to the NIH Roadmap initiatives.
Several varieties of images were used in this course module and are described in Table 1. These can be categorized into 2D and 3D images. The 2D images included (1) nanoimages of NanoBucky; (2) molecular images of fluorescent probe intensity from (a) bovine pulmonary artery endothelial cells and (b) eGFP labeled human embryonic stem (ES) cells; (3) system-level images of (a) the eGFP rabbit Alba, (b) MRI images of human brain, and (c) CT image of Phineas Gage's skull. The 2D images of human brain and Phineas Gage's skull were rendered into a computerized, 3D volume in the VRML format and displayed to students through a projector. In addition, the computerized VRML models were printed in three dimensions as physical stereolithograph models which students held in their hands.
Assessment
Through these activities, we were able to evaluate student learning gains summatively by comparing the results of a pre- and post- survey and formatively throughout the teaching unit by using assessments for active-learning activities, the Gillespie scale (Muller et al., 2003) for rating visual information quality, and the SALG assessment (Wisconsin Center for Education Research, 1997), which is an instrument developed by Elaine Seymour at the University of Wisconsin Center for Educational Research to assess student perception of learning gains as a function of course design and delivery (Seymour et al., 2000).
Summative pre- and postassessments showed that the learning gains in the unit were achieved. Students were asked to define biological scale, list and explain three imaging modalities, and quantitatively interpret X-ray images of lung with and without pneumonia. To assess learning gains, a rubric was established to code responses with a 3-point scoring scale with 1 = incorrect/unknown, 2 = a basic understanding, 3 = an advanced explanation using quantitative terminology.
In-class assessments included group discussions, activities, and evaluations that indicated to teachers, as well as students, how well concepts were understood. Here we report technical gains in the ability to use the NIH ImageJ program to quantify biological images in partial fulfillment of our learning goals. Responses from student groups were collected and compared with repeated measures by the instructor. Student proficiency with ImageJ software was assessed with a t-score. We used the Gillespie rating scale (Muller et al., 2003) to assess students' perception of biological image models relative to a baseline model to examine whether students made an association between image visualization and utility in biology. In our assessment, 2D images were used as the baseline for assessing VRML and stereolithograph models for the quality of their visual information and for their biological utility. Gillespie ratings were coded on a 4-point Likert scale (1 = Inferior, 2 = Similar/Equivalent, 3 = Superior [similar information more rapidly assimilated], 4 = Superior [additional information provided]). Students also completed our implementation of the SALG instrument, which has been successfully administered to assess student learning (Anderson, 2006; Casem, 2006). Responses to the SALG were anonymous and collected online using Zoomerang software (MarketTools). Categorical responses were coded on a 5-point Likert scale (0 = Not applicable; 1 = Not at all, 2 = A Little, 3 = Somewhat, 4 = A Lot, 5 = A Great Deal). To clarify the choice of Likert scale coarseness, a 3-point scale was arbitrarily used for pre-post assessment, whereas the Gillespie and SALG ratings used the historically recommended 4- and 5-point scale, respectively. Questions in the student gains category focused on the gains in learning goals for this imaging course, and the course design questions were centered on engagement activities and course content. The association between student gains and course design was used to assess alignment between student learning gains and the activities that were developed to help students achieve the learning goals (Kelley and Johnson, 2007). The Gillespie associations and SALG associations were determined using polychoric (Fox, 2004) correlation software in R (Development Core Team, 2005). The polychoric correlation, rho, is a useful statistic to understand associations in categorical data and is preferred to the Spearman correlation because the discretizing latent variable thresholds are estimated (Wallenhammar et al., 2004). Two-tailed significance was assessed after a rho to t conversion on N-2 degrees of freedom. A corrected p value < = 0.05 was considered significant. Analyses were conducted in R version 2.4.1 and SPSS version 14.0.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Summative measures of student learning gains indicated that our learning goals were achieved. Based on pre- and post- assessment, students made significant gains in understanding biological scale, distinguishing among a variety of imaging modalities, and interpreting images (Figure 1). These results respectively aligned with our learning goals, which were for students to understand the continuity of biological and imaging scale, the quantitative utility of imaging across a wide range of biological scales, and the importance of images for interpretation of structure and function.
In-class metrics indicated that our activities contributed to student learning gains. Students demonstrated proficiency with quantifying images using NIH ImageJ during the NanoBucky exercise (Figure 2). Incorporation of ImageJ into the course design contributed positively to gains in using imaging software (r = 0.71; p = 0.00; n = 37) and to various aspects of student learning (Figure 4). Using the Gillespie rating scale, students evaluated and perceived a strong association between the quality of visual information contained in biological images and the utility of these images within model types and an inverse association across model types (Figure 3, Table 2). The SALG instrument identified a strong association between the overall course and student gains in our learning goals including recognizing biological scale (r = 0.84; p = 0.00; n = 37) and understanding structure and function from images (r = 0.77; p = 0.00; n = 37). Furthermore, the NIH Roadmap context in our course design was associated with gains in nanoimaging (r = 0.61; p = 0.01; n = 37), molecular imaging (r = 0.75; p = 0.00; n = 37), system level imaging (r = 0.71; p = 0.00; n = 37), biological imaging tools (r = 0.56; p = 0.04; n = 37), and using imaging software (r = 0.61; p = 0.01; n = 37). Interestingly, student learning gains were not strongly associated with instructor evaluations (data not shown). This suggested that student learning was self-directed and took place within an active-learning framework as intended by our course design.
The current module would be useful as an introductory imaging unit in an introductory biology course. In addition, the NIH Roadmap initiatives we used to design this module provide a useful framework to introduce imaging concepts into the undergraduate, graduate, or medical school curriculum. Faculty with an imaging background would be best suited to teach an imaging module; however, those without an imaging background may want to separate the nanoscale, molecular, and medical imaging subunits and build upon the subunit most applicable to their overall course. The module in its current form could be adapted to a classroom of any size provided that computers and Internet access were made available. Students were provided with an overview of how the imaging equipment actually works and collects data. For example, with GFP, students were given a historical and practical overview of GFP discovery, the physics of fluorescence, and the meaning of excitation and emission spectra. Because this was an introductory course, we provided an overview of imaging quantification using images previously acquired with, for example, scanning electron microscopy and fluorescent imaging microscopy. Future implementations of this module as a full length course should incorporate finer details of image quantification by focusing on capture methodology including the importance of different exposures and neutral density filters for image correction. Future assessments using this course design may want to compare the utility of in-class activities to a purely lecture-based curriculum among two cohorts comprising those who attended lecture and those who attended lecture and participated in the active-learning activities to confirm the utility of the activities we developed based on the NIH Roadmap.
By using a scientific teaching approach and the NIH Roadmap to guide activities, this course made positive contributions to student gains in understanding that imaging is a useful way of knowing biology. This conclusion was based on summative pre-post assessments and in-class assessment using activities, Gillespie associations, and SALG associations. Early exposure to an imaging teaching unit addressed several misconceptions and may generate interest in advanced imaging education. Both the multidisciplinary nature of imaging and the NIH Roadmap context make this teaching unit accessible not only to students of biology but also physiology, engineering, mathematics, and physics. This teaching unit was limited by the number of class periods in the WOK course but may serve as a useful framework on which to build a longer, more in-depth course.
Accessing Materials
Materials for this imaging unit (including the syllabus, slides, pre/post questionnaires, Gillespie survey, SALG instrument, and imaging Internet resources) are freely available through the Wisconsin Program for Scientific Teaching (WPST) Digital Library website (http://scientificteaching.wisc.edu/materials/molecularbiology.htm).
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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0. 05, corrected for multiple comparisons) for polychoric correlations in 