Interactive Communication

The PSLC Interactive Communication cluster

Abstract

The studies in the Interactive Communication deal primarily with learning environments where there are two agents, one of which is the student. The other agent is typically a second student, a human tutor or a tutoring system. Both agents are capable of doing the instructional activity, albeit with varying degrees of success. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. The main variables are:

• What part of the work is done by which agent? On one extreme, the student does all the work while the other agent watches. On the other extreme, the student watches while the other agent does all the work. In the middle, the two agents collaborate somehow.
• Who makes the choice about which work is done by which agent? The student, the other agent or a fixed policy of some kind?

Our hypothesis is that learning by doing is the best, except that as the student takes on more work or more challenging work, the error frequency or the time to recover from errors may begin to interfere with learning. Communication also can interfere when learning, in that it takes time and cognitive resources, and that it is never perfect. Thus, learning can be optimized by somehow balancing the work done by the student, the work done by the agent and the work done by both in communicating.

Background and Significance

Educational dialogue has mostly been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993). In order to investigate more tractable albeit still complex situations, most of our research focuses on dyadic dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student.

Some studies of naturally occurring dyadic dialogues (e.g., Fox, 1993; Graesser, Bowers, Hacker, & Person, 1997; MacArthur, Stasz, & Zmuidzinas, 1990) sought their underlying structure. They found that the dialogue structure was strongly determined by the task that the participants were working on. For instance, if the task was solving a problem, then both dyads and students working alone tended to follow paths in the problem space.

Other studies compared the learning gains of dyadic dialogue-based instruction to non-interactive instruction from text, video, etc. (e.g., VanLehn, Graesser et al., in press; Katz, Connelly & Allbritton, 2003; Cohen, Kulik & Kulik, 1982). These studies found surprisingly mixed results. Although most studies showed that interactive communication was more effective than less interactive instruction, it was not always better than non-interactive instruction.

Having preliminary answers to the research questions of what dialogue is and whether it is effective, the next step in this important line of research is to determine when different types of interactive communication are effective and why. The studies in the Interactive Communication cluster tend

Glossary

To be developed, but will probably include:

• Agent: Something that can perform the instructional activity. Typically a student, a tutor, a tutoring system or a simulated student. In the extreme case, an agent can be a passive medium, such as text or a video, that presents a performance of the activity. For instance, if the instructional activity is solving physics problems, then a worked example, such as the ones shown in a textbook, is an agent.

• Communication.

• Initiative. This measures the ratio of the work initiated by the two agents. A dialogue with lots of student initiative is one where the student spontaneously initiates work on the activity. A dialogue with lots of tutor initiative is one where the tutor either does the work or requests (in the speech act sense of “request”) the student to do the work. The “initiative” term comes from linguistics, whereas a synonymous distinction, learn control vs. teacher control, comes from education.

• Zone of proximal development. When instruction is laid out on a scale of difficulty from easy to hard, there is a region where the instruction is too hard for the student to learn effectively from it without help, but still just easy enough that the student can learn if given help, typically from a second agent. This region is called the zone of proximal development (ZPD), a term from developmental psychology.

Research question

How can instructional activities that involve two agents, the student and another agent, increase robust learning?

Independent

• The type of second agent (peer, tutor, computer program, passive media) and how it communicates with the student,
• the allocation of work between the two agents,
• how that schedule is controlled,
• and the difficulty of the instruction.

Explanation

1. The tutor provides the value and prompts the student to self-explain it by providing the justification. 1.1. The student self explains the line  Exit, with learning 1.2. The student use shallow strategies such as guessing etc.  Exit, no learning 1.3. The student’s self-explanation is incorrect and the tutor gives feedback  Start 2. The student generates the step (all two parts) via a shallow strategy such as guessing or copying it from a hint 2.1. The line is correct  Exit, with little learning 2.2. The line is incorrect and the tutor gives feedback  Start 3. The student generates the value by trying to apply geometry knowledge 3.1. The value is correct  some learning and move to path 4.. 3.2. The line is incorrect and the tutor gives feedback  Start 4. The value was determined by the student and the student is to explain it by providing the justification. 4.1 The student self explains the line  Exit, with learning 4.2 The student use shallow strategies such as guessing etc.  Exit, with a bit of learning (via path 3.1) 4.2 The student’s self-explanation is incorrect and the tutor gives feedback  Start 5. The student asks for and receives a hint  Start

Descendents

• Deep-level questions during example studying (Craig & Chi)

• Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)

• Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)

• The effects of interaction on robust learning (Hausmann & VanLehn, 2007)

• The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)

• Reflective Dialogues (Katz)

• Post-practice reflection (Katz)

• Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)

• Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)

• Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren) [Moved to Refinement and Fluency, Was in Coordinative Learning]

• Understanding culture from film (Ogan, Aleven & Jones)

• Does learning from worked-out examples improve tutored problem solving? (Renkl, Aleven & Salden) [Was in Coordinative Learning]

• Visual-Verbal Learning (Aleven & Butcher) -- Elaborated Explanation condition is the relevant manipulation

• The self-correction of speech errors (McCormick, O’Neill & Siskin) [Was in Fluency and in Coordinative Learning]

• Scaffolding Problem Solving with Embedded Example to Promote Deep Learning (Ringenberg & VanLehn)

Annotated bibliography

Forthcoming