Mark Maloney, Associate Professor of Biology, Spelman College
Ikhike Imumorin, Associate Professor of Biology, Spelman College
Cynthia Bauerle, Professor of Biology, Spelman College
Modern media is presenting the new generation of students with an unprecedented blurring of information and infomercials, news and hype, reality and reality show entertainment. Information (and the rest) arrives non-stop through laptops, cell phones, cable/direct TV, blackberries, and iPods. Managing available information, finding useful information in a sea of sometimes related but largely irrelevant data, and determining which sources are credible and which should be substantiated from additional sources are major challenges confronting students today.
In this information age, scientific information increasingly falls into the category of “high throughput.” Traditional biology textbooks cannot keep pace with the rate of discovery in this new era. The Internet is full of information, but which articles are objective? Which have gone through rigorous scientific review? Students have to learn how to use online software to efficiently search databases and to identify sources that are reliable. Certainly students need to advance beyond Google if they are to be competitive in a modern curriculum (Fadel, 2006). But as new search engines and databases come online and the flow of information increases exponentially, it is increasingly difficult to keep up-to-date.
Millennial students have available to them a vast amount of accumulated knowledge, with new entries appearing at an incredible pace. With advances in technology and the variety of information sources come new challenges for teaching data management and for motivating and holding the interest and attention of the students.
These students have learned to work in groups, they are very accepting of diversity and variation, and many have a somewhat global perspective based on personal experiences through online contacts or travel. They prefer hands-on projects to lecture exams, Internet surfing to textbook reading. They do not fear technology, but few have looked into or understand the programming behind the menu. Most have mastered the technology of PowerPoint presentations, but many need to improve their writing skills, especially in the sciences.
Although Millennial students present a new challenge to the science classroom and laboratory, their technological inclinations may in fact serve them well as they prepare for 21st century careers so long as precautions are taken to ensure that they are building upon a strong foundation of basic concepts and skills. Technological sophistication is not a replacement for creativity and analytical thought. A curriculum must be designed carefully if it is to promote investigative and critical thinking skills, technical savvy, and literacy.
The Biology curriculum at Spelman College is currently being restructured to better suit the needs of the Millennial student. The new curriculum emphasizes progressive development of basic skills (observation, oral and written communication, investigation/analysis) in the first two years of study, followed by completion of a guided sequence of advanced electives chosen by the student. We have designed the new four introductory core courses as “studio” lab courses, in order to improve student: instructor ratio, and enhance the interactive learning environment for students in their first two years of biology study.
The four-course introductory core (Biological Communities, Evolution and Biodiversity; Organismal Form and Function; Biology of the Cell; Molecular Biology and Genomics) emphasizes skills development in the areas of “reading science,” “talking science,” “writing science,” and “doing science.” Remaining coursework is based on elective courses which elaborate on skills introduced in the first two years. Students must select at least one course from each of the four biology content areas (population, organismal, cellular, molecular/genomic) and each of the three emphases (literacy, experimental, technical/analytical). Additionally, students may opt to fulfill part of their elective requirements with an approved “science as a way of knowing” course offered outside of the biology department that considers biological science in a social, political, or historical context (e.g. History of Medicine, Epidemiology, etc.). A sophomore/senior seminar provides a weekly venue for Biology students, faculty, and staff to come together as a learning community.
We are currently in the process of working with Mathematics faculty to revise our mathematics requirement for majors. We are also working with an interdisciplinary group of STEM faculty to develop two new courses in computer science that will strengthen preparation in this area for our biology majors and provide additional elective options for other STEM majors.
21st Century Technological Learning Spaces
How should we design learning spaces that students are comfortable with, that support their strengths, and encourage independent investigation? With a new curriculum focused on collaborative learning activities and ready access to Internet resources and information, the traditional instructor-centered lecture hall is certainly not an optimal design. Grouped clusters of computer desks serve to promote discussion within groups and provide easy access to the Internet, online databases, and data analysis programs. Web-based learning environments provide access 24/7 beyond the classroom, through use of WebCT or Blackboard and similar technology platforms. In this environment, the lecture hall student may be transformed into investigator and passive listener into active learner. Simultaneously, biology is presented as a progressive community of thinkers rather than a collective of unchallengeable textbook facts.
Case Study: The Bioinformatics/Molecular Genomics and Proteomics Course
Our Bioinformatics/Molecular Genomics and Proteomics Course serves as an example of an upper level course emphasizing technical and analytical skills necessary in this new age of information. The high throughput nature of modern science is nowhere more evident than in bioinformatics and molecular genetics. With the dawning of the genomics era including the recent completion of the Human Genome Project, it is undeniable that the skill set required for literacy in the life sciences has changed. The assembling of entire genomes and the use of microarrays for analyzing changes in expression of these tens of thousands of genes simultaneously are becoming commonplace. A basic knowledge of statistics, scoring matrices, and computer programming are necessary to understand the power (and the limitations) of bioinformatics software and databases. As discussed above, the Biology curriculum is changing to provide just such a background for our students. For now, the background necessary for the course exercises must be provided within the Bioinformatics course itself.
We have offered a two-credit elective bioinformatics course since 2003. In 2006, the course was expanded to a four-credit course, Molecular Genomics and Proteomics. The class was held in a computer laboratory with IT support, and background material and assignments were posted on WebCT. Students learned through a variation of watch one, perform one, teach one. Time devoted to lecture covering introductory material was kept to a minimum. Students then performed an exercise in class involving bioinformatics software, and a similar exercise was assigned as homework. A future classroom discussion based on the outcomes followed. In addition, each student was required to become the instructor for some aspect of the course: providing background information, describing uses for a bioinformatics program such as a version of BLAST, use of Cn3D, etc., and/or creating a take-home assignment for the class. Final assignments required use of multiple software programs for completion, largely at the discretion of the students themselves.
The National Center for Biotechnology Information (NCBI) Web site provides free access to many bioinformatics software programs as well as tutorials for many of the programs available (http://www.ncbi.nlm.nih.gov). Most of the programs are user friendly and can be utilized by novices if the default settings are sufficient. The San Diego Supercomputer Center Biology WorkBench site has multiple programs and databases all accessible from a centralized menu (http://workbench.sdsc.edu). Although most programs can be accessed through the NCBI site or the SDSC Biology Workbench site, others such as Cn3D and Chromas must be downloaded to the classroom computers. It is strongly recommended that IT support be arranged prior to introducing such a course.
The NCBI site was the primary site used in our Molecular Genomics and Proteomics Course. A myriad of different types of data and related software programs are available through NCBI, including nucleotide and amino acid sequences, gene locus information, protein structure and conserved domain data, disease-related information, PubMed entries, etc. Data can be searched for and retrieved using the (ever expanding) Entrez System, which can be used to search across a multitude of databases simultaneously through the Entrez homepage.
The Information Problem
As an example of the challenges associated with large databases, if we want information on hemoglobin and we enter “hemoglobin” in the Entrez query box, the search output contains over 113,000 hemoglobin-related citations in PubMed, over 15,000 nucleotide sequences, and over 3,500 protein sequences identified from the databases. Some of the relevant sequences are identified as, for example, beta globin rather than hemoglobin beta chain. Others are not hemoglobin sequences but are sequences for proteins or genes associated with hemoglobin in some way. Strategies for refining search parameters using advanced search options are essential, though sometimes finding specific data involves searching by eye through search results or combining multiple strategies.
Additional class activities and assignments (examples given below) were designed to help students understand the following:
What is the bioinformatics computer program doing?
How is the graph, alignment or histogram generated?
What can and cannot be determined from the data as presented?
What assumptions/shortcuts are built into the technology to allow for data analysis of large data sets so quickly?
Examples of Exercises from Bioinformatics Class:
“You are the computer program” exercises:
1). Students must search by eye through a short DNA sequence for start and stop codons to identify potential genes. This assists in their understanding of basic concepts such as open reading frames and frame shift mutation, etc. and gives them an idea of how software such as Open Reading Frame Finder (ORF Finder) works and how much faster research can move with bioinformatics programs for database searching and manipulation. The difficulties involved in identifying genes within a DNA sequence are also made apparent.
2.) Students are required to align two amino acid sequences with limited sequence similarity using only a word processing program. Choices must be made such as what penalty to assign for gaps in order to improve alignment of the sequences, which amino acids are to be considered similar, etc. Students come to the (for some of them uncomfortable) realization that often there is no one “absolutely correct” answer.
“The answers are here, what is your question?”
3). Large databases and datasets are available that contain biologically relevant information that can be readily accessed by scientists, students or anyone else. No longer is the emphasis necessarily on scientists putting forth hypotheses, performing experiments, analyzing the data and presenting the results. Hypotheses often can be proved or disproved using data already contained in public databases. Examples of related learning assignments include those provided by the Bioinformatics Education Dissemination: Reaching Out, Connecting, and Knitting-together (BEDROCK) project. This is an NSF-funded project aimed at integrating bioinformatics throughout the undergraduate biology curriculum, using an inquiry-based approach in which students explore and analyze actual clinical or experimental data. BEDROCK problem spaces (http://www.bioquest.org/bedrock) require students to create their own hypotheses and use the data to refute or support them. The students found the clinical dataset relating to HIV mutation rate and changes in CD4-positive T cell counts during infection especially engaging.
As they progress in their science education, students increasingly will have to find and analyze information in support of their thesis rather than generate their own data. This is an unavoidable consequence of the high throughput nature of many areas of modern science. Bioinformatics techniques are now essential components of any biology curriculum, and science education at all levels must adjust to prepare students for the new era (Honts, 2003, Campbell, 2003).
A technology-based interactive learning environment is preferred by most Millennial students. Learning spaces should be designed to promote discovery-based learning, group interaction and access to databases. Development of basic science skills relating to observation, communication, investigation and analysis must now include those required for searching large databases and assessing the information available from them. Many of the issues regarding benefits, challenges and outcomes of interactive, technology-based approaches have been addressed by various authors (Allen and Tanner, 2002; Cech, 2003; Campbell, 2003; Honts, 2003). These include potential improvements in student learning, retention, confidence and the perception of modern science, as well as increases in student independence and critical thinking abilities. Such improvements are in line with the overall framework of recommendations by various committees to advance science in general and biology in particular (NRC, 2003).
Allen, D. and Tanner, K. (2002). Approaches to cell biology teaching: Questions about questions. Cell Biol Educ 1: 63 67.
Campbell, A.M. (2003). Public access for teaching genomics, proteomics and bioinformatics. Cell Biol Educ. 2: 98 111.
Cech, T. (2003). Rebalancing teaching and research. Science 299: 165.
Fadel, L. (2006). Students don’t know much beyond Google. Fort-Worth Star Telegram: July 27,: B1
Honts, J.E. (2003). Evolving strategies for the incorporation of bioinformatics within the undergraduate cell biology curriculum. Cell Biol Educ. 2: 233 247.
National Research Council, Committee on Undergraduate Biology Education to Prepare Research Scientists for the 21st Century: BIO 2010. (2003). Transforming undergraduate education for future research biologists. National Academy Press, Washington, DC.