Towards Understanding How to Leverage Sense-Making, Induction and Refinement, and Fluency to Improve Robust Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Doroudi, Shayan; Holstein, Kenneth; Aleven, Vincent; Brunskill, Emma
Institution	International Educational Data Mining Society
Titel	Towards Understanding How to Leverage Sense-Making, Induction and Refinement, and Fluency to Improve Robust Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (4 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Information Retrieval; Data Analysis; Learning Activities; Grade 4; Grade 5; Elementary School Students; Pretests Posttests; Hierarchical Linear Modeling; Scores; Intelligent Tutoring Systems; Teaching Methods; Mathematics Instruction; Elementary School Mathematics; Problem Solving; Technology Uses in Education; Educational Technology; Instructional Effectiveness; Models + Suchen Sie Ihr Suchwort? Auswertung; Lernaktivität; School year 04; 4. Schuljahr; Schuljahr 04; School year 05; 5. Schuljahr; Schuljahr 05; Intelligentes Tutorsystem; Teaching method; Lehrmethode; Unterrichtsmethode; Mathematics lessons; Mathematikunterricht; Elementare Mathematik; Schulmathematik; Problemlösen; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Unterrichtsmedien; Unterrichtserfolg; Analogiemodell
Abstract	The field of EDM has focused more on modeling student knowledge than on investigating what sequences of different activity types achieve good learning outcomes. In this paper we consider three activity types, targeting sense-making, induction and refinement, and fluency building. We investigate what mix of the three types might be most effective in supporting robust student learning. To do so, we collected data from students in grades 4 and 5 who completed sequences of activities in largely random order. Students significantly improved from pretest to posttest, suggesting that incorporating all three types can support learning gains. Using hierarchical linear modeling, we found that students who get relatively more fluency problems achieve higher posttest scores. This finding suggests that urgency-building activities are most effective in helping students learn, although our data do not allow us to conclude that fluency alone is sufficient. This work represents a step towards better under- standing what combination of different learning mechanisms may best support robust learning. [For the 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