Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences /

Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences / Cheng Ye, James R. Segedy and John S. Kinnebrew.

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 spec...

Full description

Saved in:
Bibliographic Details
Online Access: Full Text (via ERIC)
Main Authors: Ye, Cheng, Segedy, James R. (Author), Kinnebrew, John S. (Author), Biswas, Gautam (Author)
Corporate Author: International Educational Data Mining Society
Format: eBook
Language:English
Published: [Place of publication not identified] : Distributed by ERIC Clearinghouse, 2015.
Subjects:
Learning Activities.
Learning Processes.
Data Collection.
Student Behavior.
Sequential Approach.
Middle School Students.
Secondary School Science.
Science Instruction.
Problem Solving.
Classification.
Comparative Analysis.
Sequential Learning.
Information Retrieval.
Mathematical Concepts.
Data Analysis.

Internet

Full Text (via ERIC)

Online

Holdings details from Online
Call Number: ED560773
ED560773 Available

Similar Items