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- Member of: ASU Electronic Theses and Dissertations
Description
This study investigated the ability to relate a test taker’s non-verbal cues during online assessments to probable cheating incidents. Specifically, this study focused on the role of time delay, head pose and affective state for detection of cheating incidences in a lab-based online testing session. The analysis of a test taker’s non-verbal cues indicated that time delay, the variation of a student’s head pose relative to the computer screen and confusion had significantly statistical relation to cheating behaviors. Additionally, time delay, head pose relative to the computer screen, confusion, and the interaction term of confusion and time delay were predictors in a support vector machine of cheating prediction with an average accuracy of 70.7%. The current algorithm could automatically flag suspicious student behavior for proctors in large scale online courses during remotely administered exams.
ContributorsChuang, Chia-Yuan (Author) / Femiani, John C. (Thesis advisor) / Craig, Scotty D. (Thesis advisor) / Bekki, Jennifer (Committee member) / Arizona State University (Publisher)
Created2015
Description
Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
ContributorsAzarnoush, Bahareh (Author) / Runger, George C. (Thesis advisor) / Bekki, Jennifer (Thesis advisor) / Pan, Rong (Committee member) / Saghafian, Soroush (Committee member) / Arizona State University (Publisher)
Created2014
Description
Undeclared undergraduates participated in an experimental study designed to explore the impact of an Internet-delivered "growth mindset" training on indicators of women's engagement in science, engineering, technology, and mathematics ("STEM") disciplines. This intervention was hypothesized to increase STEM self-efficacy and intentions to pursue STEM by strengthening beliefs in intelligence as malleable ("IQ attitude") and discrediting gender-math stereotypes (strengthening "stereotype disbelief"). Hypothesized relationships between these outcome variables were specified in a path model. The intervention was also hypothesized to bolster academic achievement. Participants consisted of 298 women and 191 men, the majority of whom were self-identified as White (62%) and 18 years old (85%) at the time of the study. Comparison group participants received training on persuasive writing styles and control group participants received no training. Participants were randomly assigned to treatment, comparison, or control groups. At posttest, treatment group scores on measures of IQ attitude, stereotype disbelief, and academic achievement were highest; the effects of group condition on these three outcomes were statistically significant as assessed by analysis of variance. Results of pairwise comparisons indicated that treatment group IQ attitude scores were significantly higher than the average IQ attitude scores of both comparison and control groups. Treatment group scores on stereotype disbelief were significantly higher than those of the comparison group but not those of the control group. GPAs of treatment group participants were significantly higher than those of control group participants but not those of comparison group participants. The effects of group condition on STEM self-efficacy or intentions to pursue STEM were not significant. Results of path analysis indicated that the hypothesized model of the relationships between variables fit to an acceptable degree. However, a model with gender-specific paths from IQ attitude and stereotype disbelief to STEM self-efficacy was found to be superior to the hypothesized model. IQ attitude and stereotype disbelief were positively related; IQ attitude was positively related to men's STEM self-efficacy; stereotype disbelief was positively related to women's STEM self-efficacy, and STEM self-efficacy was positively related to intentions to pursue STEM. Implications and study limitations are discussed, and directions for future research are proposed.
ContributorsFabert, Natalie Shay (Author) / Bernstein, Bianca L. (Thesis advisor) / Kinnier, Richard (Committee member) / Dawes, Mary (Committee member) / Bekki, Jennifer (Committee member) / Arizona State University (Publisher)
Created2014
Description
A defining feature of many United States (U.S.) doctoral engineering programs is their large proportion of international students. Despite the large student body and the significant impacts that they bring to the U.S. education and economy, a scarcity of research on engineering doctoral students has taken into consideration the existence of international students and the consequential diversity in citizenship among all students. This study was designed to bridge the research gap to improve the understanding of sense of belonging from the perspective of international engineering doctoral students.
A multi-phase mixed methods research approach was taken for this study. The qualitative strand focused on international engineering doctoral students’ sense of belonging and its constructs. Semi-structured interview data were collected from eight international students enrolled at engineering doctoral programs at four different institutions. Thematic analysis and further literature review produced a conceptual structure of sense of belonging among international engineering doctoral students: authentic-self, problem behavior, academic self-efficacy, academic belonging, sociocultural belonging, and perceived institutional support.
The quantitative strand of this study broadened the study’s population to all engineering doctoral students, including domestic students, and conducted comparative analyses between international and domestic student groups. An instrument to measure the Engineering Doctoral Students’ Quality of Interaction (EDQI instrument) was developed while considering the multicultural nature of interactions and the discipline-specific characteristics of engineering doctoral programs. Survey data were collected from 653 engineering doctoral students (383 domestic and 270 international) at 36 R1 institutions across the U.S. Exploratory Factor Analysis results confirmed the construct validity and reliability of the data collected from the instrument and indicated the factor structures for the students’ perceived quality interactions among domestic and international student groups. A set of separate regression analyses results indicated the significance of having meaningful interactions to students’ sense of belonging and identified the groups of people who make significant impacts on students’ sense of belonging for each subgroup. The emergent findings provide an understanding of the similarities and differences in the contributors of sense of belonging between international and domestic students, which can be used to develop tailored support structures for specific student groups.
A multi-phase mixed methods research approach was taken for this study. The qualitative strand focused on international engineering doctoral students’ sense of belonging and its constructs. Semi-structured interview data were collected from eight international students enrolled at engineering doctoral programs at four different institutions. Thematic analysis and further literature review produced a conceptual structure of sense of belonging among international engineering doctoral students: authentic-self, problem behavior, academic self-efficacy, academic belonging, sociocultural belonging, and perceived institutional support.
The quantitative strand of this study broadened the study’s population to all engineering doctoral students, including domestic students, and conducted comparative analyses between international and domestic student groups. An instrument to measure the Engineering Doctoral Students’ Quality of Interaction (EDQI instrument) was developed while considering the multicultural nature of interactions and the discipline-specific characteristics of engineering doctoral programs. Survey data were collected from 653 engineering doctoral students (383 domestic and 270 international) at 36 R1 institutions across the U.S. Exploratory Factor Analysis results confirmed the construct validity and reliability of the data collected from the instrument and indicated the factor structures for the students’ perceived quality interactions among domestic and international student groups. A set of separate regression analyses results indicated the significance of having meaningful interactions to students’ sense of belonging and identified the groups of people who make significant impacts on students’ sense of belonging for each subgroup. The emergent findings provide an understanding of the similarities and differences in the contributors of sense of belonging between international and domestic students, which can be used to develop tailored support structures for specific student groups.
ContributorsLee, Eunsil (Author) / Bekki, Jennifer (Thesis advisor) / Carberry, Adam (Thesis advisor) / Kellam, Nadia (Committee member) / Arizona State University (Publisher)
Created2020
Description
Although knowledge about effective teaching and learning exists, and theories of change strategies are considered, the lack of the understanding of the behavior of engineering faculty during curricular change remains a major contributor against robust efforts for change. In this work, faculty adaptability is conceptualized as self-regulation during curricular change. Faculty participants were recruited from two divergent curricular change contexts: one that is prescribed with interdependence while the other is emergent with uncertainty. In this study, attitude toward context’s strength is conceptualized along the four dimensions of clarity, consistency, constraints, and consequences of the context, while faculty’s self-efficacy and willingness for adaptability are conceptualized along the three dimensions of planning, reflecting, and adjusting. This study uses a mixed method, quantitative-qualitative, sequential explanatory research design. The quantitative phase addresses the question of “How does faculty group in the first context differ from faculty group in the second context in terms of self-efficacy and willingness for planning, adjusting, and reflecting?” The qualitative phase addresses the question of “How do faculty respond to curricular change, as exhibited in their activities of planning, adjusting, and reflecting during change?” Findings point to differences in patterns of correlations between attitude toward context with both self-efficacy and willingness across the two contexts, even though analysis showed no significant differences between attitude toward context, self-efficacy, and willingness across the two contexts. Moreover, faculty participants’ willingness for adjusting, in both contexts, was not correlated with neither attitude toward context’s clarity nor constraints. Furthermore, in the prescribed context, Group A faculty (self-identified as Lecturers, Senior Lecturers, or Adjunct Faculty) showed higher willingness for planning, adjusting, and reflecting activities, compared to Group B faculty (self-identified as Assistant, Associate or Full Professors). Also, in the prescribed context, Group A faculty showed no overall significant correlation with attitude toward context. This study has implications on the way change is conceived of, designed, and implemented, when special attention is given to faculty as key change agents. Without the comprehensive understanding of the adaptability of faculty as key change agents in the educational system, the effective enacting of curricular change initiatives will remain unfulfilled.
ContributorsAli, Hadi (Author) / McKenna, Ann (Thesis advisor) / Bekki, Jennifer (Committee member) / Roscoe, Rod (Committee member) / Arizona State University (Publisher)
Created2021
Description
The mastery learning pedagogical approach recognizes that mastery is not always achieved on the first attempt, and learning from mistakes and perseverance is fundamental to learning. Research has suggested that mastery learning can have a positive influence on underrepresented engineering students’ learning and course performance. Yet, there is a lack of understanding of the specific ways mastery learning could benefit students and what creates those benefits. This dissertation aims to advance our understanding of how mastery learning can serve engineering students by examining their academic performance and mindsets. Study 1 is a systematic literature review of mastery learning in engineering education. It explored how mastery learning has been applied to undergraduate engineering courses, its effects on student performance, student experiences, and instructor feedback about the application of mastery learning. Study 1 revealed that mastery learning can be feasibly applied to a variety of engineering courses and that many articles say students learn better through mastery learning. Study 2 used longitudinal interview data to understand how students from a Hispanic-Serving Institution (HSI) reacted to mistakes and learned from their mistakes in mastery learning courses. I interviewed 7 participants, many of who were in mechanical engineering majors and identified as Latinx and first-generation college students. In this study, I found that mastery learning positively impacted women’s sense of belonging, and all students’ wellbeing and learning from mistakes. Study 3 applied a linear mixed modeling approach to show how mastery learning and classroom goal structures impacted first-generation college students’ growth mindsets. I surveyed 148 first-generation college students, most of whom were Latinx. Mastery learning was shown to positively influence the growth mindsets of approximately 40% of students. Even students enrolled in non-mastery learning courses showed an increase in their growth mindsets score the more they perceived that the classroom environment was focused on mastery. Collectively, these studies reveal ways mastery learning can positively contribute to the academic development, mindsets, and wellbeing of engineering students. The evidence provided can be used to help make decisions about employing mastery learning to better serve HSI’s underrepresented student body.
ContributorsPerez, Carlos L (Author) / Verdín, Dina (Thesis advisor) / Bekki, Jennifer (Committee member) / Allen, Emily (Committee member) / Arizona State University (Publisher)
Created2024
Description
Hypertensive disorders of pregnancy (HDP) affect up to 5%-15% of pregnancies around the globe, and form a leading cause of maternal and neonatal morbidity and mortality. HDP are progressive disorders for which the only cure is to deliver the baby. An increasing trend in the prevalence of HDP has been observed in the recent years. This trend is anticipated to continue due to the rise in the prevalence of diseases that strongly influence hypertension such as obesity and metabolic syndrome. In order to lessen the adverse outcomes due to HDP, we need to study (1) the natural progression of HDP, (2) the risks of adverse outcomes associated with these disorders, and (3) the optimal timing of delivery for women with HDP.
In the first study, the natural progression of HDP in the third trimester of pregnancy is modeled with a discrete-time Markov chain (DTMC). The transition probabilities of the DTMC are estimated using clinical data with an order restricted inference model that maximizes the likelihood function subject to a set of order restrictions between the transition probabilities. The results provide useful insights on the progression of HDP, and the estimated transition probabilities are used to parametrize the decision models in the third study.
In the second study, the risks of maternal and neonatal adverse outcomes for women with HDP are quantified with a composite measure of childbirth morbidity, and the estimated risks are compared with respect to type of HDP at delivery, gestational age at delivery, and type of delivery in a retrospective cohort study. Furthermore, the safety of child delivery with respect to the same variables is assessed with a provider survey and technique for order performance by similarity to ideal solution (TOPSIS). The methods and results of this study are used to parametrize the decision models in the third study.
In the third study, the decision problem of timing of delivery for women with HDP is formulated as a discrete-time Markov decision process (MDP) model that minimizes the risks of maternal and neonatal adverse outcomes. We additionally formulate a robust MDP model that gives the worst-case optimal policy when transition probabilities are allowed to vary within their confidence intervals. The results of the decision models are assessed within a probabilistic sensitivity analysis (PSA) that considers the uncertainty in the estimated risk values. In our PSA, the performance of candidate delivery policies is evaluated using a large number of problem instances that are constructed according to the orders between model parameters to incorporate physicians' intuition.
In the first study, the natural progression of HDP in the third trimester of pregnancy is modeled with a discrete-time Markov chain (DTMC). The transition probabilities of the DTMC are estimated using clinical data with an order restricted inference model that maximizes the likelihood function subject to a set of order restrictions between the transition probabilities. The results provide useful insights on the progression of HDP, and the estimated transition probabilities are used to parametrize the decision models in the third study.
In the second study, the risks of maternal and neonatal adverse outcomes for women with HDP are quantified with a composite measure of childbirth morbidity, and the estimated risks are compared with respect to type of HDP at delivery, gestational age at delivery, and type of delivery in a retrospective cohort study. Furthermore, the safety of child delivery with respect to the same variables is assessed with a provider survey and technique for order performance by similarity to ideal solution (TOPSIS). The methods and results of this study are used to parametrize the decision models in the third study.
In the third study, the decision problem of timing of delivery for women with HDP is formulated as a discrete-time Markov decision process (MDP) model that minimizes the risks of maternal and neonatal adverse outcomes. We additionally formulate a robust MDP model that gives the worst-case optimal policy when transition probabilities are allowed to vary within their confidence intervals. The results of the decision models are assessed within a probabilistic sensitivity analysis (PSA) that considers the uncertainty in the estimated risk values. In our PSA, the performance of candidate delivery policies is evaluated using a large number of problem instances that are constructed according to the orders between model parameters to incorporate physicians' intuition.
ContributorsDemirtas, Aysegul (Author) / Gel, Esma S (Thesis advisor) / Saghafian, Soroush (Thesis advisor) / Bekki, Jennifer (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2018
Description
This increasing role of highly automated and intelligent systems as team members has started a paradigm shift from human-human teaming to Human-Autonomy Teaming (HAT). However, moving from human-human teaming to HAT is challenging. Teamwork requires skills that are often missing in robots and synthetic agents. It is possible that adding a synthetic agent as a team member may lead teams to demonstrate different coordination patterns resulting in differences in team cognition and ultimately team effectiveness. The theory of Interactive Team Cognition (ITC) emphasizes the importance of team interaction behaviors over the collection of individual knowledge. In this dissertation, Nonlinear Dynamical Methods (NDMs) were applied to capture characteristics of overall team coordination and communication behaviors. The findings supported the hypothesis that coordination stability is related to team performance in a nonlinear manner with optimal performance associated with moderate stability coupled with flexibility. Thus, we need to build mechanisms in HATs to demonstrate moderately stable and flexible coordination behavior to achieve team-level goals under routine and novel task conditions.
ContributorsDemir, Mustafa, Ph.D (Author) / Cooke, Nancy J. (Thesis advisor) / Bekki, Jennifer (Committee member) / Amazeen, Polemnia G (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
The American Heart Association (AHA) estimates that there are approximately 200,000 in-hospital cardiac arrests (IHCA) annually with low rates of survival to discharge at about 22%. Training programs for cardiac arrest teams, also termed code teams, have been recommended by the Institute of Medicine (IOM) and in the AHA's consensus statement to help improve these dismal survival rates. Historically, training programs in the medical field are procedural in nature and done at the individual level, despite the fact that healthcare providers frequently work in teams. The rigidity of procedural training can cause habituation and lead to poor team performance if the situation does not match the original training circumstances. Despite the need for team training, factors such as logistics, time, personnel coordination, and financial constraints often hinder resuscitation team training. This research was a three-step process of: 1) development of a metric specific for the evaluation of code team performance, 2) development of a communication model that targeted communication and leadership during a code blue resuscitation, and 3) training and evaluation of the code team leader using the communication model. This research forms a basis to accomplish a broad vision of improving outcomes of IHCA events by applying conceptual and methodological strategies learned from collaborative and inter-disciplinary science of teams.
ContributorsHinski, Sandra T. (Author) / Cooke, Nancy J. (Thesis advisor) / Roscoe, Rod (Committee member) / Bekki, Jennifer (Committee member) / Arizona State University (Publisher)
Created2017
Description
For decades, engineering scholarship has presented data to address the underrepresentation of Black womxn in the engineering doctoral community. American Society of Engineering Education (ASEE)’s Engineering by the Numbers Report (2021) statistically showed that only 57 Black womxn out of 10,037 scholars received engineering doctorates in 2021. Engineering scholars have theorized about constructs ranging from whiteness to explain the system, to doctoral socialization to explain the culture, to retention explain the experiences. Yet, even with the plethora of scholarship, the problem of underrepresentation has remained consistent with limited action towards change from the faculty, the program, or the institution. Therefore, I aim to address this problem by cultivating emotional resonance toward action within the engineering community regarding engineering doctoral program underrepresentation for Black womxn. Using Arts-Based Research (ABR) and Black Feminist Thought (BFT), this dissertation illustrates the engineering PhD spirit-murdering experiences of Black womxn. Six Homegirls intellectually contributed to this study by sharing their time and experiences through artistic expressions and homegirl conversations. Through the lens of BFT’s matrix of domination, the composite blog shows that spirit-murdering for these Homegirls: 1) is a targeted act that is dehumanizing 2) occurs because of the aloof nature and capitalist ideals of the engineering academy, and 3) causes further conflict in negotiating identities as Black, woman, professional, researcher, and student. Leaning on BFT’s grounding as an Afrocentric methodological approach, the composite poem illustrates that these Homegirls: 1) have a common, understood epistemology because of their shared experiences of being Black and woman in their current, multi-layered social locations, 2) identify strongly with their positionality and values while describing their outsider-within status, and 3) experience spirit-murdering in an emotional, intellectual, and spiritual way that then results in physical manifestations. Rooted in BFT’s ethic of caring, the hip-hop mixtape’s progression describes homegirl’s spirit-renewal tactics as: 1) owning their professional identity, 2) dispelling projected biases, stereotypes, and aggressions, 3) calling out inequities in their interpersonal relationships and program culture, 4) learning to set boundaries to protect themselves, and 5) standing on their ways of knowing and being.
ContributorsNicole, Fantasi (Author) / Coley, Brooke C. (Thesis advisor) / Bekki, Jennifer (Committee member) / Holly, Jr., James (Committee member) / Arizona State University (Publisher)
Created2023