We evaluated an affect-sensitive version of AutoTutor, a dialogue based ITS that simulates human tutors. While the original AutoTutor is sensitive to students’ cognitive states, the affect-sensitive tutor (Supportive tutor) also responds to students’ affective states (boredom, confusion, and frustration) with empathetic, encouraging, and motivational dialogue moves that are accompanied by appropriate emotional expressions. We conducted an experiment that compared the Supportive and Regular (non-affective) tutors over two 30-minute learning sessions with respect to perceived effectiveness, fidelity of cognitive and emotional feedback, engagement, and enjoyment. The results indicated that, irrespective of tutor, students’ ratings of engagement, enjoyment, and perceived learning decreased across sessions, but these ratings were not correlated with actual learning gains. In contrast, students’ perceptions of how closely the computer tutors resembled human tutors increased across learning sessions, was related to the quality of tutor feedback, the increase was greater for the Supportive tutor, and was a powerful predictor of learning. Implications of our findings for the design of affect-sensitive ITSs are discussed.

The plurality of taxonomies, the group of researchers have attempted to make ITS differences amongst available features, and the techniques interactions more naturalistic and conversational. In order used have yielded a variety of approaches. Verbee et al. to accomplish this goal, researchers have analyzed corpora (2006) examined the features used by 16 dialogue act of human-human tutorial dialogues to better understand tagging studies and identified 24 features that have been both individual dialogue acts and patterns of acts that occur previously used. While an extensive discussion of these in human tutoring (Graesser & Person, 1994; Graesser, features is outside the scope of the present paper, the Person, & Magliano, 1995; Litman & Forbes-Riley, 2006; features fall loosely into four categories: word based (e.g.

Affective computing (AC) is an emerging field that aspires to narrow the communicative gap between the highly emotional human and the emotionally challenged computer by developing computational systems that recognize and respond to the affective states (such as moods and emotions) of the user. One of the basic tenets behind AC is that automatically recognizing and responding to a user's affective states during interactions with a computer can enhance the quality of the interaction, thereby making the computer interface more usable, enjoyable, and effective. For example, an affect-sensitive learning environment that detects and responds to student frustration is expected to increase motivation, engagement, and learning gains. This special track will serve as a forum to unite researchers from the interdisciplinary arena that encompasses computer science, engineering, HCI, psychology, and education to exchange ideas, frameworks, methods, and tools relating to affective computing. Although the last decade has been ripe with theory and applications relevant to AC, these advances are accompanied by a new set of challenges.

It is widely acknowledged that one-on-one human tutoring is one of the most effective ways to provide learning, however, the source of its effectiveness is still unclear. Tutor-centered, student-centered, and interaction hypotheses have been proposed as possible explanations of the effectiveness of human tutoring. Most research has addressed this question by analyzing tutorial sessions at the dialogue move or speech act level. The present paper adopts a different approach by focusing on word usage patterns in 50 naturalistic tutorial sessions between human students and expert tutors. Specifically, each unique word in the session was designated as a student initiative word, a tutor initiative word, or a shared-initiative word. Comparisons of the frequencies as well as the weights of the words assigned to each of these categories indicated that the student and tutor share initiative even though the tutor’s are considerably more verbose. The implications of the results for the development of an ITS that aspires to model expert tutors are discussed.