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Document Chunking and Learning Objective Generation for Instruction Design

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Title: Document Chunking and Learning Objective Generation for Instruction Design
Language: English
Authors: Tran, Khoi-Nguyen, Lau, Jey Han, Contractor, Danish, Gupta, Utkarsh, Sengupta, Bikram, Butler, Christopher J., Mohania, Mukesh
Source: International Educational Data Mining Society. 2018.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Peer Reviewed: Y
Page Count: 10
Publication Date: 2018
Document Type: Speeches/Meeting Papers
Reports - Descriptive
Descriptors: Behavioral Objectives, Instructional Design, Reference Materials, Prediction, Verbs, Computational Linguistics, Teaching Methods, Semantics, Banking, Pharmacology
Abstract: Instructional Systems Design is the practice of creating of instructional experiences that make the acquisition of knowledge and skill more efficient, effective, and appealing [18]. Specifically in designing courses, an hour of training material can require between 30 to 500 hours of effort in sourcing and organizing reference data for use in just the preparation of course material. In this paper, we present the first system of its kind that helps reduce the effort associated with sourcing reference material and course creation. We present algorithms for document chunking and automatic generation of learning objectives from content, creating descriptive content metadata to improve content-discoverability. Unlike existing methods, the learning objectives generated by our system incorporate pedagogically motivated Bloom's verbs. We demonstrate the usefulness of our methods using real world data from the banking industry and through a live deployment at a large pharmaceutical company. [For the full proceedings, see ED593090.]
Abstractor: As Provided
Entry Date: 2019
Accession Number: ED593104
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Document Chunking and Learning Objective Generation for Instruction Design
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  Data: Instructional Systems Design is the practice of creating of instructional experiences that make the acquisition of knowledge and skill more efficient, effective, and appealing [18]. Specifically in designing courses, an hour of training material can require between 30 to 500 hours of effort in sourcing and organizing reference data for use in just the preparation of course material. In this paper, we present the first system of its kind that helps reduce the effort associated with sourcing reference material and course creation. We present algorithms for document chunking and automatic generation of learning objectives from content, creating descriptive content metadata to improve content-discoverability. Unlike existing methods, the learning objectives generated by our system incorporate pedagogically motivated Bloom's verbs. We demonstrate the usefulness of our methods using real world data from the banking industry and through a live deployment at a large pharmaceutical company. [For the full proceedings, see ED593090.]
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      – TitleFull: Document Chunking and Learning Objective Generation for Instruction Design
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