CoLABnet helps chemistry students focus on principles rather than process

By Theresa Maddix

A team of students enter their project data into the CoLABnet class data bank last winter. The graduate student instructor looking on with the white lab coat is Uju Inya-Agha. Students, from left, are David Haligowski, Laura Hall and Anuja Deo. Photo Services file photo by Paul Jaronski

While sitting in an introductory Excel class, chemistry lecturer Nancy Konigsberg Kerner realized that the spreadsheet she was working on was identical in layout to well plates her students used in her Chemistry 125 lab course. With this in mind, she went to the Information Technology Division (ITD) and said, “I’m not a programmer, but would there be some way of collecting team observations from well plates and converting them to a spreadsheet format?” CoLABnet was born.

CoLABnet, the Collaborative Laboratories Through Networked Computers project, “redesigned a first-year chemistry course to support collaborative team inquiry using networked computers,” Kerner explains.

Teams of students now conduct experiments and enter data into an evolving class data bank. By the time their discussion sections meet, student teams have entered at least 70 data sets for each experiment and posted them on the Internet. This allows the discussion to center on the principles and processes behind the experiments, rather than whether the team found the “right answer.”

“Students [in discussions] are getting up, saying, ‘This is our data,’” Kerner says. “‘This is what we’ve come up with. These are the connections we’re making.’”

When surveyed at the beginning of the course, these same students reported low levels of knowledge, experience and confidence in the scientific inquiry process.

Most traditional laboratory settings are more like illustrations of well established principles than actual research, Kerner says. The instructors and students expect certain outcomes and grading is based on how close the students come to the expected result. With such a limited data set students cannot draw out scientific principles. Sharing their “answers” is not encouraged and a competitive atmosphere results.

In a real-life research setting, says Kerner, “scientists have a lot of data from which patterns can emerge or become evident. Teams are essential not only to the data collection process but because dialogue is often a key ingredient in discovery.”

Kerner’s lab uses data sets drawn from the more than 700 students who take the class each term—large enough to be significant. A team-approach allows the students to focus more on what is happening while it is happening.

During the project’s pilot phase, Kerner sat outside in the hall with a stopwatch during discussion sections to help determine “if the software shifted focus from data collection and organization to data analysis. She found that even in the pilot phase, computer-assisted sections were spending, on average, twice as much time on the analysis and implications of class data as compared to non-computer sections.

As the project has progressed, students have become more focused on data analysis, trying to convince their peers that “we really see a pattern here.” Students in discussion sections this term are taking their data and extrapolating further to guess at untested scenarios.

At the end of the course, Kerner again gives her students the questionnaire they responded to when they began the term. Sections surveyed have shown statistically significant leaps in response to two questions. For example, students in the fall 1996 semester moved, on average, from a 2.7 (out of 5) rating to a 3.8 rating in response to their perceived ability to “identify unknown samples,” and moved from 2.7 to 3.9 in their perceived ability to “design an experiment to test a hypothesis.”

“A primary goal,” Kerner says, “is to educate the students to think and develop qualitative reasoning skills. Such skills are relevant to solving real-life problems.”