What matters most to you in your teaching?
How are you using technology as a tool to achieve your teaching goals?
How have your students responded to your use of technology?
What new goals do you have for using technology in teaching?
How could the University better facilitate the use of technology in instruction?
Work with real data
Analyze complex systems
Thinking like a researcher
Teaching fundamental idea
Providing Students with a True Research Experience
A different approach, which underlies my use of technology in teaching, is to make students part of the research mission and experience of the university. What I’ve done is create a research project just for undergraduates—the sequencing of a microbial genome.
The thing that is most important to me in terms of teaching with technology is to engage students so as to capitalize on their natural, innate curiosity. This makes them more excited about what they’re learning. Of course there is payoff for me personally. It’s a lot more fun teaching students who are engaged and want to know something because they are intrinsically interested in the subject. When students follow their own curiosity, they are motivated to learn. Empowering students this way is much more enjoyable and effective than what is, too often, the traditional model of force-feeding a lot of facts for students to memorize and re-state on a test. In the sciences, especially, the culture is such that there is so much material to master that this seems expedient, but it blunts curiosity.
I use technology as a tool to cultivate curiosity. Genomic technology enables students to become part of the research mission. What I’ve done is create a research project specifically for undergraduates—the sequencing of a microbial genome. This is a major research undertaking and involves, of course, very complex technology. The students become experts during a quarter on this technology.
Another important consideration here is that life sciences research has changed a great deal over the last ten years. Today's scientist spends much of her time at a computer designing experiments and actually developing the context for experiments through data mining, through bioinformatics, and so on. Likewise, we use computers almost ubiquitously in the laboratories for carrying out experiments. Computers are used to run the instrumentation, for visualizing the data, and for analyzing the data. This revolution in the laboratory setting in the modern research lab really hasn’t made it into to the undergraduate curriculum yet.
Students do need to become more discerning users of computers, of algorithms, of the data that they can find on the web. I also want to encourage them to be able to do something more than using the default settings in a program. Setting up this microbial genome sequencing project necessitated getting in a DNA sequencer which is, of course, controlled by computers. Students are working with real data, not something from a recipe, not something they’re just following along in a lab manual protocol. They’re doing real research, something that hasn’t been done before, and they’re encountering the ambiguities, the messiness—all those aspects of real data. By having to grapple with some of the things that naturally happen in the course of research, they have to become more discerning users of the instrumentation.
I’ve created a research project centered on a lot of computer technology and state-of-the-art algorithms for analysis and data mining. What is also really key is that the instrumentation has allowed students to be in “the big leagues” in terms of research. This is intrinsically motivating for them. Students really take a great deal of pride in their sequencing project. Also, students get feedback. Because they are doing real research, they feel part of the research community. Their data is deposited in the National Center for Biotechnology Information’s database, which is the world-wide repository for sequenced data of all kinds that is used by researchers. In lecture I also teach them the theory behind a particular algorithm which is sort of the “bread and butter” algorithm of bioinformatics. It’s called BLAST. These students really understand how BLAST works, and so become skilled users of the software and databases.
An interesting thing about the DNA sequencer is that it generates a lot of data that can be used by a large number of students. I’d like to further augment the experience for all students. The microbial genome sequencing project is set up in such a way that a number of students in other courses are doing small parts of the research and feeding it into the hub course in which the students actually use the DNA sequencer. What I’d like to do is deepen the courses that are out there on the spokes by making them a bigger part of the whole research project. I want these students in other courses to use some of the algorithms that are only being used by the core course at the moment.
A second area that I’m really interested in is using more bioinformatics in my courses. The logic of these algorithms is built on biological principles, so they can be creatively used to help students understand biology. Right now I’m working with a couple of different databases, one for example curated by the Joint Genome Institute which is part of the Department of Energy. I’d like to develop tools so students can annotate genomes, so they’ll be doing all kinds of bioinformatics experiments in order to interpret data.
What we’ll have in the end will be an annotated genome and that’s a publishable product. So we might have 600 authors on it—all UCLA undergraduates— and it might take three consecutive quarters to just complete the annotation, but, nevertheless, I think we can accomplish this. I see bioinformatics as a whole new way of illustrating biological principles for students. These algorithms that we use to analyze research data at all levels are based on biological principles. For example, when you are comparing two amino acid sequences, you have to consider: “well, this comes from one organism, this comes from another organism.” How do the differences that we see when we line the sequences up illustrate the function of the gene product, reveal the evolutionary history and so on.
More students can be involved in real research this way. There have been issues—most specifically of space and setting up wet labs—that have kept us from bringing more undergraduate students into the research mission. Because bioinformatics research can be done on computers, these issues are no longer obstacles. There is such a rich curriculum potential in bioinformatics and making our students part of UCLA’s research mission in bioinformatics. And I think it’s tractable. And that’s where I’d like to go next.
Oral Interview, February, 2006