國立虎尾科技大學 |

Multimodal Learning Analytics and Predictive Student Modeling for Game-Based Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Multimodal Learning Analytics and Predictive Student Modeling for Game-Based Learning./
Author:	Emerson, Andrew John.
Description:	1 online resource (165 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
Contained By:	Dissertations Abstracts International84-12A.
Subject:	Physiology. -
Online resource:	click for full text (PQDT)
ISBN:	9798379649845

Multimodal Learning Analytics and Predictive Student Modeling for Game-Based Learning.
Emerson, Andrew John.

Multimodal Learning Analytics and Predictive Student Modeling for Game-Based Learning. - 1 online resource (165 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: A.

Thesis (Ph.D.)--North Carolina State University, 2021.

Includes bibliographical references

A distinctive feature of game-based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor technologies (e.g., facial expression analysis and gaze tracking) and natural language processing have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics informed by multimodal data captured during students' interactions with game-based learning environments hold significant promise for developing a deeper understanding of game-based learning, designing game-based learning environments to detect unproductive student behaviors, and informing adaptive scaffolding to support game-based learning. Further, learning analytics frameworks that can accurately predict student learning outcomes early in students' interactions hold considerable promise for enabling environments to adapt to individual student needs.This dissertation investigates a multimodal, multi-task predictive student modeling framework for informing individualized learning in game-based learning environments. The framework is evaluated on two corpora of game-based learning interactions from two distinct student populations who interacted with two versions of CRYSTAL ISLAND, a game-based learning environment for microbiology education. The framework leverages available multimodal data channels from the corpora to simultaneously predict student post-test performance and interest. In CRYSTAL ISLAND - SENSOR-BASED, student facial expressions, eye gaze, and gameplay behaviors are used to predict these outcomes at early points during interactions, and in CRYSTAL ISLAND - REFLECTION, textual representations of student reflections and gameplay are used. In addition to inducing models for each corpus individually, this dissertation investigates the ability to leverage information from one corpus to improve models based on another (i.e., transfer learning through the use of pre-trained models).This dissertation reports on research on multimodal learning analytics, multi-task machine learning, and early prediction. Previous work has shown that multimodal models of student posttest performance and interest outperform unimodal models. Additionally, predictive models that incorporate multi-task learning have achieved improved accuracy compared to single-task models when predicting student performance on post-tests. Preliminary work has also demonstrated the efficacy of early prediction approaches for forecasting student performance. The dissertation research extends these approaches by combining them into a unified framework that makes predictions early during game-based learning.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379649845Subjects--Topical Terms:

673386
Physiology.
Index Terms--Genre/Form:

554714
Electronic books.

Multimodal Learning Analytics and Predictive Student Modeling for Game-Based Learning.
LDR:04054ntm a22003737 4500 001 1147831
005 20240916075410.5
006 m o d
007 cr bn ---uuuuu
008 250605s2021 xx obm 000 0 eng d
020 $a 9798379649845
035 $a (MiAaPQ)AAI30463924
035 $a (MiAaPQ)NCState_Univ18402040723
035 $a AAI30463924
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Emerson, Andrew John. $3 1473648
245 1 0 $a Multimodal Learning Analytics and Predictive Student Modeling for Game-Based Learning.
264 0 $c 2021
300 $a 1 online resource (165 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
500 $a Advisor: Chi, Min;Price, Thomason;Jiang, Shiyan;II, James Lester.
502 $a Thesis (Ph.D.)--North Carolina State University, 2021.
504 $a Includes bibliographical references
520 $a A distinctive feature of game-based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor technologies (e.g., facial expression analysis and gaze tracking) and natural language processing have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics informed by multimodal data captured during students' interactions with game-based learning environments hold significant promise for developing a deeper understanding of game-based learning, designing game-based learning environments to detect unproductive student behaviors, and informing adaptive scaffolding to support game-based learning. Further, learning analytics frameworks that can accurately predict student learning outcomes early in students' interactions hold considerable promise for enabling environments to adapt to individual student needs.This dissertation investigates a multimodal, multi-task predictive student modeling framework for informing individualized learning in game-based learning environments. The framework is evaluated on two corpora of game-based learning interactions from two distinct student populations who interacted with two versions of CRYSTAL ISLAND, a game-based learning environment for microbiology education. The framework leverages available multimodal data channels from the corpora to simultaneously predict student post-test performance and interest. In CRYSTAL ISLAND - SENSOR-BASED, student facial expressions, eye gaze, and gameplay behaviors are used to predict these outcomes at early points during interactions, and in CRYSTAL ISLAND - REFLECTION, textual representations of student reflections and gameplay are used. In addition to inducing models for each corpus individually, this dissertation investigates the ability to leverage information from one corpus to improve models based on another (i.e., transfer learning through the use of pre-trained models).This dissertation reports on research on multimodal learning analytics, multi-task machine learning, and early prediction. Previous work has shown that multimodal models of student posttest performance and interest outperform unimodal models. Additionally, predictive models that incorporate multi-task learning have achieved improved accuracy compared to single-task models when predicting student performance on post-tests. Preliminary work has also demonstrated the efficacy of early prediction approaches for forecasting student performance. The dissertation research extends these approaches by combining them into a unified framework that makes predictions early during game-based learning.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Physiology. $3 673386
650 4 $a Affect (Psychology). $3 1473649
650 4 $a Tutoring. $3 1473650
650 4 $a Behavior. $3 582559
650 4 $a Motivation. $3 655186
650 4 $a Deep learning. $3 1127428
650 4 $a Educational objectives. $3 1473651
650 4 $a School environment. $3 1104511
650 4 $a Real time. $3 1465795
650 4 $a Sensors. $3 1003702
650 4 $a Neural networks. $3 1011215
650 4 $a Learning analytics. $3 1473652
650 4 $a Subject specialists. $3 1473653
650 4 $a Games. $3 595446
650 4 $a Skills. $3 1207328
650 4 $a Education. $3 555912
650 4 $a Educational administration. $3 1148465
650 4 $a Psychology. $3 555998
655 7 $a Electronic books. $2 local $3 554714
690 $a 0719
690 $a 0800
690 $a 0515
690 $a 0514
690 $a 0621
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a North Carolina State University. $3 845424
773 0 $t Dissertations Abstracts International $g 84-12A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30463924 $z click for full text (PQDT)

	簡單查詢
	進階查詢
	權威查詢
	圖書館SDI清單
	讀者推薦閱讀清單(公開)

	指定參考書
	圖書借閱排行榜
	圖書預約排行榜
	主題查詢
	分類瀏覽
	新書通報

	個人資料
	書籤
	薦購
	預約/借閱記錄查詢
	訊息