How should intelligent tutoring systems sequence multiple graphical representations of fractions? A multi-methods study.

Autor/inn/en	Rau, Martina A.; Aleven, Vincent; Rummel, Nikol; Pardos, Zachary
Titel	How should intelligent tutoring systems sequence multiple graphical representations of fractions? A multi-methods study.
Quelle	In: International journal of artificial intelligence in education, 24 (2014) 2, S. 125-161Infoseite zur Zeitschrift PDF als Volltext Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	1560-4292; 1560-4306
DOI	10.1007/s40593-013-0011-7
Schlagwörter	Evaluation; Multimedia; Algorithmus; Lernen; Unterricht; Didaktik; Evaluation; Repräsentation; Multimedia; Intelligentes Tutorsystem; Schuljahr 04; Schuljahr 05; Lernen; Didaktik; Intelligentes Tutorsystem; Unterricht; Algorithmus; Tutorensystem; Bayes-Formel; Repräsentation
Abstract	Providing learners with multiple representations of learning content has been shown to enhance learning outcomes. When multiple representations are presented across consecutive problems, [teachers] have to decide in what sequence to present them. Prior research has demonstrated that interleaving tasks types (as opposed to blocking them) can foster learning. Do the same advantages apply to interleaving representations? [The authors] addressed this question using a variety of research methods. First, [they] conducted a classroom experiment with an intelligent tutoring system for fractions. [The authors] compared four practice schedules of multiple graphical representations: blocked, fully interleaved, moderately interleaved, and increasingly interleaved. Based on data from 230 4th and 5th-grade students, [they] found that interleaved practice leads to better learning outcomes than blocked practice on a number of measures. Second, [they] conducted a think-aloud study to gain insights into the learning mechanisms underlying the advantage of interleaved practice. Results show that students make connections between representations only when explicitly prompted to do so (and not spontaneously). This finding suggests that reactivation, rather than abstraction, is the main mechanism to account for the advantage of interleaved practice. Third, [the writers] used methods derived from Bayesian knowledge tracing to analyze tutor log data from the classroom experiment. Modeling latent measures of students' learning rates, [they] find higher learning rates for interleaved practice than for blocked practice. This finding extends prior research on practice schedules, which shows that interleaved practice (compared to blocked practice) impairs students' problem-solving performance during the practice phase when using raw performance measures such as error rates. [The] findings have implications for the design of multi-representational learning materials and for research on adaptive practice schedules in intelligent tutoring systems. (Orig.).
Erfasst von	Externer Selbsteintrag
Update	2016/1