Interactive Communication

The PSLC Interactive Communication cluster

Abstract

The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other agent is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it introduces the right content, or it steers students to consider the right content, or it increases the effectiveness of the cognitive processes used in encoding or studying a piece of content.

Background and Significance

Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: dyadic instructional 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. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

Glossary

See Interactive Communication Glossary

Research question

The research problem addressed by this cluster is: What properties of interactive communication promote robust learning?

Independent

• Some studies in the Interactive Communication cluster examine the impact on robust learning of different types of interactive communication, by contrasting two forms of interactive communication. Examples include compare scripted vs. unscripted peer collaborative problem solving.

• Other studies compare instruction with and without specific kinds interactive communication, e.g., by having students work alone or in pairs, or comparing self-explanation done alone to dyadic, interactive explanation generation.

• A third class of manipulation holds most of an interactive communication constant and varies only a small part of it. For instance, a video may be viewed with and without interactive prompts inserted at key points. It should perhaps be noted that, as in the physical sciences, this study-it-in-isolation strategy is risky. Just as a heart extracted from an animal doesn’t behave exactly like one that still resides in the animal, the process studied in isolation may not behave exactly like the one that occurs in interactive communication. Nonetheless, significant progress has been made in the physical sciences by using this isolation strategy, so it may help the science of learning as well.

Dependent variables

Measures of normal and robust learning.

Hypothesis

When student engage in collaborative learning with another agent, the learning will be more robust is the manipulation either (1) introduces essential content, or (2) guides students to cover content that they would otherwise avoid, or (3) increases the effectiveness of the cognitive processes used to encode or study the content. These 3 methods are described in more detail below.

Explanation

If we view a short episode of interactive communication as a learning event space, there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the topology of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the path choices of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the path effects might differ in the treatment vs. the control.

Descendents

Collaboration

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

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

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

• 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)

Assistance during example studying

• 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)

• Understanding culture from film (Ogan, Aleven & Jones) [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

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

Assistance during problem solving

• Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)

• Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)

• Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren) [Also relevant to Refinement & Fluency, Knowledge component analysis]

• Scaffolding Problem Solving with Embedded Example to Promote Deep Learning (Ringenberg & VanLehn) [Also relevant to Coordinative Learning, Examples]

Assistance after problem solving

• Reflective Dialogues (Katz, Connelly & Treacy)

• Post-practice reflection (Katz & Connelly)

• The self-correction of speech errors (McCormick, O’Neill & Siskin)

Annotated bibliography

Forthcoming