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- Genre: Masters Thesis
Description
Lots of previous studies have analyzed human tutoring at great depths and have shown expert human tutors to produce effect sizes, which is twice of that produced by an intelligent tutoring system (ITS). However, there has been no consensus on which factor makes them so effective. It is important to know this, so that same phenomena can be replicated in an ITS in order to achieve the same level of proficiency as expert human tutors. Also, to the best of my knowledge no one has looked at student reactions when they are working with a computer based tutor. The answers to both these questions are needed in order to build a highly effective computer-based tutor. My research focuses on the second question. In the first phase of my thesis, I analyzed the behavior of students when they were working with a step-based tutor Andes, using verbal-protocol analysis. The accomplishment of doing this was that I got to know of some ways in which students use a step-based tutor which can pave way for the creation of more effective computer-based tutors. I found from the first phase of the research that students often keep trying to fix errors by guessing repeatedly instead of asking for help by clicking the hint button. This phenomenon is known as hint refusal. Surprisingly, a large portion of the student's foundering was due to hint refusal. The hypothesis tested in the second phase of the research is that hint refusal can be significantly reduced and learning can be significantly increased if Andes uses more unsolicited hints and meta hints. An unsolicited hint is a hint that is given without the student asking for one. A meta-hint is like an unsolicited hint in that it is given without the student asking for it, but it just prompts the student to click on the hint button. Two versions of Andes were compared: the original version and a new version that gave more unsolicited and meta-hints. During a two-hour experiment, there were large, statistically reliable differences in several performance measures suggesting that the new policy was more effective.
ContributorsRanganathan, Rajagopalan (Author) / VanLehn, Kurt (Thesis advisor) / Atkinson, Robert (Committee member) / Burleson, Winslow (Committee member) / Arizona State University (Publisher)
Created2011
Description
Online learning platforms such as massive online open courses (MOOCs) and
intelligent tutoring systems (ITSs) have made learning more accessible and personalized. These systems generate unprecedented amounts of behavioral data and open the way for predicting students’ future performance based on their behavior, and for assessing their strengths and weaknesses in learning.
This thesis attempts to mine students’ working patterns using a programming problem solving system, and build predictive models to estimate students’ learning. QuizIT, a programming solving system, was used to collect students’ problem-solving activities from a lower-division computer science programming course in 2016 Fall semester. Differential mining techniques were used to extract frequent patterns based on each activity provided details about question’s correctness, complexity, topic, and time to represent students’ behavior. These patterns were further used to build classifiers to predict students’ performances.
Seven main learning behaviors were discovered based on these patterns, which provided insight into students’ metacognitive skills and thought processes. Besides predicting students’ performance group, the classification models also helped in finding important behaviors which were crucial in determining a student’s positive or negative performance throughout the semester.
intelligent tutoring systems (ITSs) have made learning more accessible and personalized. These systems generate unprecedented amounts of behavioral data and open the way for predicting students’ future performance based on their behavior, and for assessing their strengths and weaknesses in learning.
This thesis attempts to mine students’ working patterns using a programming problem solving system, and build predictive models to estimate students’ learning. QuizIT, a programming solving system, was used to collect students’ problem-solving activities from a lower-division computer science programming course in 2016 Fall semester. Differential mining techniques were used to extract frequent patterns based on each activity provided details about question’s correctness, complexity, topic, and time to represent students’ behavior. These patterns were further used to build classifiers to predict students’ performances.
Seven main learning behaviors were discovered based on these patterns, which provided insight into students’ metacognitive skills and thought processes. Besides predicting students’ performance group, the classification models also helped in finding important behaviors which were crucial in determining a student’s positive or negative performance throughout the semester.
ContributorsMandal, Partho Pratim (Author) / Hsiao, I-Han (Thesis advisor) / Davulcu, Hasan (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2017
Description
In the daily life of an individual problems of varying difficulty are encountered.
Each problem may include a different number of constraints placed upon the problem
solver. One type of problem commonly used in research are multiply-constrained
problems, such as the compound remote associates. Since their development they have
been related to creativity and insight. Moreover, research has been conducted to
determine the cognitive abilities underlying problem solving abilities. We sought to fully
evaluate the range of cognitive abilities (i.e., working memory, episodic and semantic
memory, and fluid and crystallized intelligence) linked to multiply-constrained problem
solving. Additionally, we sought to determine whether problem solving ability and
strategies (analytical or insightful) were task specific or domain general through the use
of novel problem solving tasks (TriBond and Location Bond). Results indicated that
multiply-constrained problem solving abilities were domain general, solutions derived
through insightful strategies were more often correct than analytical, and crystallized
intelligence was the only cognitive ability that provided unique predictive value.
Each problem may include a different number of constraints placed upon the problem
solver. One type of problem commonly used in research are multiply-constrained
problems, such as the compound remote associates. Since their development they have
been related to creativity and insight. Moreover, research has been conducted to
determine the cognitive abilities underlying problem solving abilities. We sought to fully
evaluate the range of cognitive abilities (i.e., working memory, episodic and semantic
memory, and fluid and crystallized intelligence) linked to multiply-constrained problem
solving. Additionally, we sought to determine whether problem solving ability and
strategies (analytical or insightful) were task specific or domain general through the use
of novel problem solving tasks (TriBond and Location Bond). Results indicated that
multiply-constrained problem solving abilities were domain general, solutions derived
through insightful strategies were more often correct than analytical, and crystallized
intelligence was the only cognitive ability that provided unique predictive value.
ContributorsEllis, Derek M (Author) / Brewer, Gene A. (Thesis advisor) / Homa, Donald (Committee member) / Goldinger, Stephen (Committee member) / Arizona State University (Publisher)
Created2019
Description
Past research has focused on the important role humor plays in interpersonal relationships; however, researchers have also identified intrapersonal applications of humor, showing that people often use humor to alleviate negative affect, and that humor has generally been found to beneficially influence mental health. The purpose of this study is to examine whether humor-based coping can be utilized as an intrapersonal tool to aid or facilitate creative thinking and problem solving when faced with a distressing situation. The current study posits reduced rumination as the mechanism by which humor facilitates creativity. To measure creativity, a task was devised that had individuals brainstorm under some distress; participants were asked to recall and describe an ongoing, unresolved problem they were facing, followed by a rumination induction, as rumination is characterized by perseverative thoughts that hinder constructive action. After the rumination induction, participants were randomly assigned to a control condition or either of two emotion regulation conditions: positive reappraisal or humor-based reappraisal. Following this, participants were asked to complete an “alternate solutions” task, based on Guilford’s Alternate Uses Task, generating solutions for their own unresolved problem. Results of the study showed that the use of humor was indeed related to a decrease in rumination, but that the humor condition did not outperform either control condition on any measure of creativity (performing worse in some cases). Limits of this study and future directions are discussed.
ContributorsPages, Erika (Author) / Shiota, Michelle N. (Thesis advisor) / Kenrick, Douglas T. (Committee member) / Varnum, Michael E.W. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Numerous psychosocial and health factors contribute to perceived stress, social support, and problem-solving coping relating to overall well-being and life satisfaction in older adults. The effect of social support and problem-solving coping, however, remains largely untested as potential moderators. The present study was conducted to test whether social support and problem- solving coping would moderate the relation between perceived stress and life satisfaction in older adults. First, I anticipated that stress will be negatively related to life satisfaction at low levels of social support, while at high social support; stress will be unrelated to life satisfaction. Second, I expected that with low problem- solving coping, stress will be negatively related to life satisfaction, whereas, at levels of high problem- solving coping, stress will be unrelated to life satisfaction. Using an experimental survey and interview design with hierarchical regression analyses, I found no support that social support would moderate the relation between stress and life satisfaction. I found support that problem-solving coping moderated the relation between stress and life satisfaction. For individuals who engage in higher levels of problem- solving coping, higher levels of stress predicted lower levels of life satisfaction. On the other hand, at lower levels of problem-solving coping, more stress predicted lower levels of life satisfaction.
ContributorsKaur, Gurjot (Author) / Miller, Paul A. (Thesis advisor) / Hall, Deborah L. (Committee member) / Roberts, Nicole A. (Committee member) / Arizona State University (Publisher)
Created2017
Description
Building computational models of human problem solving has been a longstanding goal in Artificial Intelligence research. The theories of cognitive architectures addressed this issue by embedding models of problem solving within them. This thesis presents an extended account of human problem solving and describes its implementation within one such theory of cognitive architecture--ICARUS. The document begins by reviewing the standard theory of problem solving, along with how previous versions of ICARUS have incorporated and expanded on it. Next it discusses some limitations of the existing mechanism and proposes four extensions that eliminate these limitations, elaborate the framework along interesting dimensions, and bring it into closer alignment with human problem-solving abilities. After this, it presents evaluations on four domains that establish the benefits of these extensions. The results demonstrate the system's ability to solve problems in various domains and its generality. In closing, it outlines related work and notes promising directions for additional research.
ContributorsTrivedi, Nishant (Author) / Langley, Patrick W (Thesis advisor) / VanLehn, Kurt (Committee member) / Kambhampati, Subbarao (Committee member) / Arizona State University (Publisher)
Created2011