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ERIC Number: ED560792
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 12
ISBN: N/A
ISSN: N/A
Towards Understanding How to Leverage Sense-Making, Induction and Refinement, and Fluency to Improve Robust Learning
Doroudi, Shayan; Holstein, Kenneth; Aleven, Vincent; Brunskill, Emma
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
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.]
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Grade 4; Intermediate Grades; Elementary Education; Grade 5; Middle Schools
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: International Educational Data Mining Society
IES Funded: Yes
Grant or Contract Numbers: R305A130215