I've adapted the following idea from the "Paideia Project", developed in the 1970s by Mildred Henry and Joseph Katz.

Goal: Help all students succeed in your course if at all possible, no matter how different they are in abilities, needs, and learning styles. It's important to teach for all students, not just the majority or just for the students who are most like you. But what works for some students in a course may not work for others (and sometimes for surprising reasons). Ordinarily students who are 'different' may be relatively invisible to you, especially if there are only one or two of each kind of 'different.' Use this interview approach to help you see how things are going even for these 'invisible' students, so you can invent ways to teach that will work for everyone, or almost everyone in your course.

Method (summary): In order to adjust your teaching and course materials so that all students can learn, (1) select a few students who are different in ways that are likely to affect their experience in the course, and (2) ask them periodically about what in the course has worked for them since your last conversation, and what has given them problems.

Method (detailed): (1) Selecting students to interview. Select a small group to interview regularly (weekly?) as the course unfolds. If your course consisted of 11 type X students, 2 type Y students, and one type Z, you would select an interview group of three students: one X, one Y and one Z. From your later weekly conversations with them, you'd be trying to get insights to help assure that the course works for all three types of students, not just the majority (X) or the ones most like you (Y).

What kinds of variation among students are important enough to consider when picking the group of students to interview?

• Comfort with technology. There are sometimes surprising differences among students with little computer experience, students with a moderate amount of experience, and students who are so experienced that they are (over?) confident.

• Students who value right answers versus students who are comfortable with ambiguity, exploration and creative work. This variation could be important in an engineering design course, for example.

• For a course where students will work in teams, students who experienced in making teams work, students who are inexperienced, and students who have had bad experiences with teams and don't like them.

• Students who vary in their ability for mathematical reasoning

1. Diagnostic data already collected about students by the institution, including test scores and grades in prior courses.

2. Tests of their ability to perform

3. Ask them to rate themselves, after explaining why you want the information. Be as precise as possible in your questions, and try not to let one option sound like the one 'good' students would pick. Ideally a student of each type would say about one option, "That's me" and feel quite comfortable in describing themselves that way.

However you do it, select a group of about four to seven students who represent the most important variations of learners and learning in your courses: personal characteristics that might affect their encounters with your technology-related teaching or assignments.

Once you've selected your group of interviewees, arrange to talk with them. You might do this just once, but it seems more useful (if more time-consuming) to do it every week or two. You'll need to convince them that you're going to use these conversations to make the course better for students like them. When talking with them, just listen to what they say and take notes. Try not to be defensive. Here are some questions you might ask:

• Since we last talked, what has happened in the course (class meetings, readings, assignments, etc.) that you liked the best?

• What has happened that was the most confusing or unpleasant?

Suppose you're trying to improve a particular use of technology. For example, you teach biology and you've asked students to do simulated breeding experiments.

If time is short, you might just ask "What did you think of the simulated breeding experiments?"

If you've got more time, you might get some basic information from them first?

• What has been your previous experience in the lab and/or with simulated lab experiences in your science courses? (If students have a history of great experiences, or bad experiences, in labs, it's important to find out why because their past will usually influence the way they use the new simulation, and what they learn from it.)

• Did you try the simulated lab in this course? If not, what got in the way?

• If you've done this lab, what were the best aspects of it for you?

• What was most frustrating or confusing about it?

If you have a suggestion for rewriting this page, please send e-mail to Steve Ehrmann at ehrmann@tltgroup.org.