Academic journal article Journal of Interactive Learning Research

Reliability and Factor Structure of the Attitude toward Tutoring Agent Scale (ATTAS)

Academic journal article Journal of Interactive Learning Research

Reliability and Factor Structure of the Attitude toward Tutoring Agent Scale (ATTAS)

Article excerpt

Pedagogical agents are gaining acceptance as effective learning tools (Baylor & Ryu, 2003; Moreno, Mayer, Spires & Lester 2001; Moreno, 2004). The increase in the use of agents highlights the need for standardized measurements for evaluating user performance in these environments. While learning gains are a primary variable of interest in such environments, the role of affective variables may be at least as important as learning gains (Anderson, 1995; Bardwell, 1984; Cognition and Technology Group at Vanderbilt, 1992; Kort, Reilly, & Picard, 2001). The purpose of this research was to design and validate an instrument, the Attitude Toward Tutoring Agent Scale (ATTAS), to measure users' perception of pedagogical agents who use conversational dialog to teach (i.e., as tutors). Items were developed from existing higher education teacher rating scales. Scale items were administered to 129 participants from three large urban universities in the south and northwest after interactions with AutoTutor, an animated pedagogical agent designed to teach conceptual physics. Results of factor analysis indicate a scale with three constructs: (a) conversation/pedagogy, (b) attitude toward student, and (c) student interest/attention. Reliability analyses showed strong reliability coefficients for each construct (alphas of .84, .87 and .89, respectively). Scales may be used independently or together in pedagogical agent tutoring environments.

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STATEMENT OF THE PROBLEM

Research has shown that individualized instruction and one-to-one tutoring that encourages students to provide in-depth explanations of their answers promotes learning gains (Chi, de Leeuw, Chiu, & LaVancher, 1994; Bloom, 1984). One of the goals of computer-based instruction is to recreate these types of tutoring environments. One of the more promising ways to do this is through the use of pedagogical agent environments. Pedagogical agents allow designers to create an environment in which learners can interact with a computer-based conversational partner to get advice, feedback, or instruction. These agents can take the form of humans, animals, and/or inanimate objects (e.g., the Microsoft Paperclip), or fantastic creatures (e.g., genies or space aliens). With the advent of more powerful computer technology, these agents are being designed to interact with the learner in much the same way as in a human teacher-student relationship, which allows designers to address the social aspects of human-computer interaction (HCI) while tapping the benefits of individualized instruction.

Reeves and Nass (1996) have presented evidence that people apply human social interaction rules to computer characters. This research has particular relevance for the design and evaluation of pedagogical agent environments; the extent to which a pedagogical agent can be effective will be largely influenced by its ability to mimic and support the application of those social rules in the learning environment. Because pedagogical agents are becoming more prevalent (Baylor, 2000; Baylor & Ryu, 2003; Graesser, Van Lehn, Jordan, Rose, & Harter, 2001; Johnson, 2004; Lester et al., 1997; Moreno, 2004; Moreno, Mayer, Spires, & Lester, 2001), it is of increasing importance to researchers to be able to evaluate the user perceptions of the pedagogical agents' ability to effectively reproduce a teacher-student environment.

While learning gains are an important variable of interest in the study of any pedagogical tool, the role that affective variables such as mood, motivation, attitude toward instruction, and attitude toward content can play in the learning process has been well documented, and many believe that these factors are at least as important as direct measures of learning gains (Anderson, 1995; Bardwell, 1984; Cognition and Technology Group at Vanderbilt, 1992; Kort, Reilly, & Picard, 2001; Lent, Brown, & Larkin, 1984; Lepper & Chabay, 1987; Marsh, Cairns, Relich, Barnes, & Debus, 1984; Picard, 1997; Sedighian & Sedighian, 1996; Shaw & Costanza, 1970; Smead & Chase, 1981). …

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