Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (11th, 2018).

Autor/inn/en	Fang, Ying; Shubeck, Keith; Lippert, Anne; Chen, Qinyu; Shi, Genghu; Feng, Shi; Gatewood, Jessica; Chen, Su; Cai, Zhiqiang; Pavlik, Philip; Frijters, Jan; Greenberg, Daphne; Graesser, Arthur
Titel	Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (11th, 2018).
Quelle	(2018), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Intelligent Tutoring Systems; Educational Technology; Technology Uses in Education; Adult Literacy; Teaching Methods; Student Characteristics; Adult Students; Reading Comprehension; Reading Skills; Reading Difficulties; Accuracy; Woodcock Johnson Tests of Achievement + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Teaching method; Lehrmethode; Unterrichtsmethode; Adult; Adults; Student; Students; Erwachsenenalter; Studentin; Schüler; Schülerin; Leseverstehen; Reading skill; Lesefertigkeit; Reading difficulty; Leseschwierigkeit
Abstract	A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. To do this, researchers must identify the learning patterns exhibited by those interacting with the system. In the present work, we use clustering analysis to capture learning patterns in over 250 adults who used the ITS, "CSAL" (Center for the Study of Adult Literacy) "AutoTutor," to gain reading comprehension skills. AutoTutor has conversational agents that teach literacy adults with low literacy skills comprehension strategies in 35 lessons. These comprehension strategies align with one or more of the following levels specified in the Graesser-McNamara theoretical framework of comprehension: "word," "textbase," "situation model" and "rhetorical structure." We used the adult learners' average response times per question and performance across lessons to cluster the students' learning behavior. Performance was measured as the proportion of 3-alternative-response questions answered correctly. Lessons were coded on one of the four theoretical levels of comprehension. Results of the cluster analyses converged on four types of learners: proficient readers, struggling readers, conscientious readers and disengaged readers. Proficient readers were fast and accurate; struggling readers worked slowly but were not accurate; conscientious readers worked slowly and performed comparatively well; disengaged readers were fast but did not perform well. Interestingly, the behaviors of learners in different clusters varied across the four theoretical levels. Identifying types of readers can enhance the adaptivity of AutoTutor by allowing for more personalized feedback and interventions designed for particular learning behaviors. [This paper was published in: K.E. Boyer & M. Yudelson (Eds.), "Proceedings of the 11th International Conference on Educational Data Mining" (pp.348-354). Buffalo, NY: Educational Data Mining Society.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01