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Exploring Strategy Learning and Data-Driven Subgoal Interventions to Aid Current and Future Problem Solving.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Exploring Strategy Learning and Data-Driven Subgoal Interventions to Aid Current and Future Problem Solving./
作者:	Shabrina, Preya.
面頁冊數:	1 online resource (206 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: A.
Contained By:	Dissertations Abstracts International85-01A.
標題:	Problem solving. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30516322click for full text (PQDT)
ISBN:	9798379879440

Exploring Strategy Learning and Data-Driven Subgoal Interventions to Aid Current and Future Problem Solving.
Shabrina, Preya.

Exploring Strategy Learning and Data-Driven Subgoal Interventions to Aid Current and Future Problem Solving. - 1 online resource (206 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: A.

Thesis (Ph.D.)--North Carolina State University, 2023.

Includes bibliographical references

Intelligent tutors and learning platforms have been proven to improve students' learning outcomes. Subgoaling is an important component of learning that prepares students for future or novel problem solving. Also, research has shown that the availability of subgoals reduces problem-solving difficulties and improves students' current problem-solving or learning experiences. Researchers have explored various subgoal-based interventions within learning environments. However, subgoal-based problem-solving strategies remained unexplored in this regard. Also, existing literature mostly focused on using subgoal-based interventions to reduce problem-solving difficulties or to optimize only superficial metrics such as test scores. Only a little research has investigated the impact of a subgoal-based intervention on students' problem-solving and subgoaling approaches or skills.To address these gaps, in this dissertation, I present four studies exploring the impact of subgoal-based problem-solving strategies and data-driven subgoal-based interventions on students' learning experience, performance, and problem-solving skills. In studies 1 and 3, I integrated Backward problem-solving strategy and Mixed problem-solving strategy (which is inspired by means-ends analysis) into training problems within an intelligent logic tutor. In both of these problem-solving strategies, sequential iterative subgoaling is carried out. The impact of the integration of these strategies is evaluated based on students' learning experience and problem-solving (and subgoaling) skills. In studies 2 and 4, I explored data-driven subgoal-based interventions within a novice programming environment and an intelligent logic tutor. Study 2 explored how the availability and quality of data-driven subgoals and subgoal-completion-based feedback impact students' programming efficiency and behavior. Study 4 explored data-driven subgoals presented as chunks in a Parsons Problem fashion within an intelligent logic tutor. Here, each chunk is a piece of the complete solution and achieves a subgoal contributing to the final goal. The impact of the chunky presentation of data-driven subgoals is again analyzed based on both students' learning experience and problem-solving (and subgoaling) skills. Overall, the studies presented in this dissertation explored subgoal-based problem-solving strategies and interventions to improve students' problem-solving skills (i.e. to aid future problem solving) with an emphasis on subgoaling skills while taking into consideration their training problem-solving or learning experience (i.e. while aiding current problem solving).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379879440Subjects--Topical Terms:

516855
Problem solving.
Index Terms--Genre/Form:

542853
Electronic books.

Exploring Strategy Learning and Data-Driven Subgoal Interventions to Aid Current and Future Problem Solving.
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245 1 0 $a Exploring Strategy Learning and Data-Driven Subgoal Interventions to Aid Current and Future Problem Solving.
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500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: A.
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502 $a Thesis (Ph.D.)--North Carolina State University, 2023.
504 $a Includes bibliographical references
520 $a Intelligent tutors and learning platforms have been proven to improve students' learning outcomes. Subgoaling is an important component of learning that prepares students for future or novel problem solving. Also, research has shown that the availability of subgoals reduces problem-solving difficulties and improves students' current problem-solving or learning experiences. Researchers have explored various subgoal-based interventions within learning environments. However, subgoal-based problem-solving strategies remained unexplored in this regard. Also, existing literature mostly focused on using subgoal-based interventions to reduce problem-solving difficulties or to optimize only superficial metrics such as test scores. Only a little research has investigated the impact of a subgoal-based intervention on students' problem-solving and subgoaling approaches or skills.To address these gaps, in this dissertation, I present four studies exploring the impact of subgoal-based problem-solving strategies and data-driven subgoal-based interventions on students' learning experience, performance, and problem-solving skills. In studies 1 and 3, I integrated Backward problem-solving strategy and Mixed problem-solving strategy (which is inspired by means-ends analysis) into training problems within an intelligent logic tutor. In both of these problem-solving strategies, sequential iterative subgoaling is carried out. The impact of the integration of these strategies is evaluated based on students' learning experience and problem-solving (and subgoaling) skills. In studies 2 and 4, I explored data-driven subgoal-based interventions within a novice programming environment and an intelligent logic tutor. Study 2 explored how the availability and quality of data-driven subgoals and subgoal-completion-based feedback impact students' programming efficiency and behavior. Study 4 explored data-driven subgoals presented as chunks in a Parsons Problem fashion within an intelligent logic tutor. Here, each chunk is a piece of the complete solution and achieves a subgoal contributing to the final goal. The impact of the chunky presentation of data-driven subgoals is again analyzed based on both students' learning experience and problem-solving (and subgoaling) skills. Overall, the studies presented in this dissertation explored subgoal-based problem-solving strategies and interventions to improve students' problem-solving skills (i.e. to aid future problem solving) with an emphasis on subgoaling skills while taking into consideration their training problem-solving or learning experience (i.e. while aiding current problem solving).
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538 $a Mode of access: World Wide Web
650 4 $a Problem solving. $3 516855
650 4 $a Impact analysis. $3 3544937
650 4 $a Feedback. $3 677181
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650 4 $a Pedagogy. $3 2122828
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