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ERIC Number: ED560773
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 7
Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences
Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features individually for each pattern. Consequently, MFH-SPAM operates on a larger space of patterns in the activity sequences. In this paper, we employ a differential version of MFH-SPAM to extract a small set of patterns that best differentiate students with different learning behavior profiles in the Betty's Brain system. Our results illustrate that: (1) MFH-SPAM identifies important patterns missed by traditional sequence mining approaches; and (2) the differential patterns provide additional information for characterizing learning behaviors. This has implications for developing targeted and adaptive scaffolding in open-ended learning environments. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Middle Schools; Secondary Education; Junior High 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: R305A120186