Choosing to Interact: Exploring the Relationship between Learner Personality, Attitudes, and Tutorial Dialogue Participation

Autor/inn/en	Ezen-Can, Aysu; Boyer, Kristy Elizabeth
Institution	International Educational Data Mining Society
Titel	Choosing to Interact: Exploring the Relationship between Learner Personality, Attitudes, and Tutorial Dialogue Participation
Quelle	(2015), (4 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Intelligent Tutoring Systems; Natural Language Processing; Computer Science Education; Problem Solving; Student Characteristics; Personality Traits; Profiles; Student Attitudes; Predictor Variables; Interaction; Learner Engagement; Task Analysis; Student Participation; Extraversion Introversion; Models; Individual Differences; Multiple Regression Analysis; College Students; Man Machine Systems + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Natürliche Sprache; Computer science lessons; Informatikunterricht; Problemlösen; Individual characteristics; Personality characteristic; Persönlichkeitsmerkmal; Charakterisierung; Profilanalyse; Schülerverhalten; Prädiktor; Interaktion; Aufgabenanalyse; Schülermitarbeit; Schülermitwirkung; Studentische Mitbestimmung; Analogiemodell; Individueller Unterschied; Collegestudent; Mensch-Maschine-System
Abstract	The tremendous effectiveness of intelligent tutoring systems is due in large part to their interactivity. However, when learners are free to choose the extent to which they interact with a tutoring system, not all learners do so actively. This paper examines a study with a natural language tutorial dialogue system for computer science, in which students interacted with the JavaTutor system through natural language dialogue over the course of problem solving. We explore the relationship between students' level of dialogue interaction and learner characteristics including personality profile and pre-existing attitudes toward the learning task. The results show that these learner characteristics are significant predictors of the extent to which students engage in dialogue with the tutoring system, as well as the number of task actions students make. By identifying students who may not engage with tutoring systems as readily, this work constitutes a step toward building adaptive systems that successfully support a variety of students with different attitudes and personalities. [For complete proceedings, see ED560503.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01