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Machine Learning Methods for Buildin...

Liang, Chen.

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Machine Learning Methods for Building Educational Applications: Concept Prerequisite Learning and Automatic Distractor Generation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Methods for Building Educational Applications: Concept Prerequisite Learning and Automatic Distractor Generation./
Author:	Liang, Chen.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	141 p.
Notes:	Source: Dissertation Abstracts International, Volume: 80-04(E), Section: A.
Contained By:	Dissertation Abstracts International80-04A(E).
Subject:	Information science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13804006
ISBN:	9780438717084

Machine Learning Methods for Building Educational Applications: Concept Prerequisite Learning and Automatic Distractor Generation.
Liang, Chen.

Machine Learning Methods for Building Educational Applications: Concept Prerequisite Learning and Automatic Distractor Generation. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 141 p.

Source: Dissertation Abstracts International, Volume: 80-04(E), Section: A.

Thesis (Ph.D.)--The Pennsylvania State University, 2018.

The increasing amount of education-related data provides a valuable research opportunity for developing data-driven machine learning methods for building educational applications. This dissertation investigates machine learning solutions for two educational applications: concept prerequisite learning and automatic distractor generation.

ISBN: 9780438717084Subjects--Topical Terms:

554358
Information science.

Machine Learning Methods for Building Educational Applications: Concept Prerequisite Learning and Automatic Distractor Generation.
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245 1 0 $a Machine Learning Methods for Building Educational Applications: Concept Prerequisite Learning and Automatic Distractor Generation.
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300 $a 141 p.
500 $a Source: Dissertation Abstracts International, Volume: 80-04(E), Section: A.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2018.
520 $a The increasing amount of education-related data provides a valuable research opportunity for developing data-driven machine learning methods for building educational applications. This dissertation investigates machine learning solutions for two educational applications: concept prerequisite learning and automatic distractor generation.
520 $a A prerequisite relation describes a fundamental directed relation among concepts in knowledge structures. The first part of this dissertation focuses on the concept prerequisite learning problem, the study of machine learning methods for automatic concept prerequisite discovery. Specifically, this dissertation explores the use of Wikipedia -- the largest free online encyclopedia -- for concept prerequisite learning and presents the following studies towards automatically measuring concept prerequisite relations. First, a simple but effective link-based feature, RefD, is proposed for measuring prerequisite relations among concepts. Second, how concept prerequisite relations can be recovered from university course dependencies is explored. Third, active learning of concept prerequisite learning is studied to deal with the lack of large-scale concept prerequisite labels. The dissertation explores the mathematical nature of prerequisite relation being a strict partial order and proposes an active learning framework tailored for such relation. The proposed approach incorporates relational reasoning not only in finding new unlabeled pairs whose labels can be deduced from an existing label set, but also in devising new query strategies that consider the relational structure of labels.
520 $a Multiple choice questions (MCQs) are widely used to assess students' knowledge and skills. Among all methods for creating good MCQs, finding reasonable distractors is crucial and usually the most time-consuming. The second part of this dissertation investigates automatic distractor generation (DG). In contrast with previous similarity-based methods, this dissertation presents two studies on machine learning methods for DG. The first study proposes a generative model learned from training generative adversarial nets to create useful distractors for automatically creating fill-in-the-blank questions. The second work investigates how ranking models can be used to select useful distractors for MCQs. The proposed models can learn to select distractors that resemble those in actual exam questions.
520 $a Finally, the dissertation introduces BBookX, a computer-facilitated book-creation system. Using information retrieval techniques, BBookX is designed to facilitate the online book-creation process by searching OERs. BBookX is an actual educational application where the proposed methods for concept prerequisite learning and automatic distractor generation could be applied.
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