Matching Items (22)
Description
The moderating effects of five characteristics of peers--their effortful control, anger, sadness, aggression, and positive peer behavior--were investigated in two separate series of analyses of preschooler's social behavior: (a) the relation between children's own effortful control and social behavior, and (b) the relation between children's shyness and reticent behavior. Latent variable interactions were conducted in a structural equation framework. Peer context anger and effortful control, albeit with unexpected results, interacted with children's own characteristics to predict their behavior in both the EC and shy model series; these were the only significant interactions obtained for the EC model series. The relation between shyness and reticent behavior, however, showed the greatest impact of peer context and, conversely, the greatest susceptibility to environmental variations; significant interactions were obtained in all five models, despite the limited range of peer context sadness and aggression observed in this study.
ContributorsHuerta, Snježana (Author) / Eisenberg, Nancy (Thesis advisor) / Spinrad, Tracy (Committee member) / Pina, Armando (Committee member) / Geiser, Christian (Committee member) / Arizona State University (Publisher)
Created2012
Description
Assemblers and compilers provide feedback to a programmer in the form of error messages. These error messages become input to the debugging model of the programmer. For the programmer to fix an error, they should first locate the error in the program, understand what is causing that error, and finally resolve that error. Error messages play an important role in all three stages of fixing of errors. This thesis studies the effects of error messages in the context of teaching programming. Given an error message, this work investigates how it effects student’s way of 1) understanding the error, and 2) fixing the error. As part of the study, three error message types were developed – Default, Link and Example, to better understand the effects of error messages. The Default type provides an assembler-centric single line error message, the Link type provides a program-centric detailed error description with a hyperlink for more information, and the Example type provides a program centric detailed error description with a relevant example. All these error message types were developed for assembly language programming. A think aloud programming exercise was conducted as part of the study to capture the student programmer’s knowledge model. Different codes were developed to analyze the data collected as part of think aloud exercise. After transcribing, coding, and analyzing the data, it was found that the Link type of error message helped to fix the error in less time and with fewer steps. Among the three types, the Link type of error message also resulted in a significantly higher ratio of correct to incorrect steps taken by the programmer to fix the error.
ContributorsBeejady Murthy Kadekar, Harsha Kadekar (Author) / Sohoni, Sohum (Thesis advisor) / Craig, Scotty D. (Committee member) / Jordan, Shawn S (Committee member) / Gary, Kevin A (Committee member) / Arizona State University (Publisher)
Created2017
Description
Each programming language has a compiler associated with it which helps to identify logical or syntactical errors in the program. These compiler error messages play important part in the form of formative feedback for the programmer. Thus, the error messages should be constructed carefully, considering the affective and cognitive needs of programmers. This is especially true for systems that are used in educational settings, as the messages are typically seen by students who are novice programmers. If the error messages are hard to understand then they might discourage students from understanding or learning the programming language. The primary goal of this research is to identify methods to make the error messages more effective so that students can understand them better and simultaneously learn from their mistakes. This study is focused on understanding how the error message affects the understanding of the error and the approach students take to solve the error. In this study, three types of error messages were provided to the students. The first type is Default type error message which is an assembler centric error message. The second type is Link type error message which is a descriptive error message along with a link to the appropriate section of the PLP manual. The third type is Example type error message which is again a descriptive error message with an example of the similar type of error along with correction step. All these error types were developed for the PLP assembly language. A think-aloud experiment was designed and conducted on the students. The experiment was later transcribed and coded to understand different approach students take to solve different type of error message. After analyzing the result of the think-aloud experiment it was found that student read the Link type error message completely and they understood and learned from the error message to solve the error. The results also indicated that Link type was more helpful compare to other types of error message. The Link type made error solving process more effective compared to other error types.
ContributorsTanpure, Siddhant Bapusaheb (Author) / Sohoni, Sohum (Thesis advisor) / Gary, Kevin A (Committee member) / Craig, Scotty D. (Committee member) / Arizona State University (Publisher)
Created2018
Description
This study examined whether changes in intervention related gains from the REACH for Personal and Academic Success program, an indicated anxiety prevention school-based protocol, vary as a function of participant youth's exposure to overprotective parenting. This study also examined if ethnicity/race (Caucasian vs. Hispanic/Latino) interacts with overprotective parenting to predict program response. A total of 98 children (M age = 9.70, SD = .07; 77.60% girls; 60.20% Hispanic/Latino) received 1 of 2 protocols (REACH or academic support) and responses were measured at post-treatment and 1-year follow-up. Findings showed that child self-regulation skills improved in the school program (REACH) for children of parents with low levels of overprotection, and child self-regulation skills improved in the control program (academic support) for children of parents with high levels of overprotection. These findings were significant in the Hispanic/Latino subsample, but not in Caucasian youth.
ContributorsBromich, Bobbi Lynn (Author) / Pina, Armando (Thesis director) / Presson, Clark (Committee member) / Hahs, Adam (Committee member) / Department of Psychology (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Frequent flyers are students who make repeated, unplanned visits to the school nurse, mostly presenting with somatic symptoms like headaches, stomachaches, and musculoskeletal pain. Somatic symptoms are characteristic of pediatric anxiety symptoms and disorders, but the relation between anxiety symptoms and frequent flyer status never has been systematically examined. This study employs data corresponding to 209 students in the 4th and 5th grade (Mage = 9.51, 43.5% girls, 50.9% 51.2% Caucasian, 23.9% Hispanic/Latino) to examine the relation between students' visits to the school nurse (frequent flyer status) and anxiety, including possible variations by children's socio-demographic characteristics, including sex and race/ethnicity. Findings showed statistically significant relations between anxiety and an increased number of nurse visits. A relation between anxiety and sex leading to increased nurse visits was not statistically significant. The statistical model testing race/ethnicity and anxiety in relation to increased nurse visits was found to be significant but driven solely by anxiety. Implications for this study include reframing how frequent flyers are viewed by teachers and addressing possible anxiety in these students.
ContributorsCarnesi, Elizabeth Dora (Author) / Pina, Armando (Thesis director) / Glenberg, Arthur (Committee member) / Stoll, Ryan (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Longitudinal data from European-American (EA) and Mexican-American (MA) families (n = 179 mothers, fathers, and youth; 41% MA) was used to test a bio-psycho-cultural model of the effect of non-responsive parenting on externalizing problems in young adult offspring through the effect on the stress response system. Parenting behavior (acceptance, rejection, harsh discipline) was assessed when children were in late childhood (12-13 years), cortisol samples were collected during late adolescence (18-19 years), and externalizing problems were measured in young adulthood (21-22 years). Latent profile analyses were used to examine patterns of parenting behavior in EA and MA families. A path analysis framework was used to examine how non-responsive parenting interacted with acceptance to predict adolescent stress response and subsequent externalizing problems in EA and MA young adults. Results showed different patterns of parenting behavior in EA versus MA families, with MA families demonstrating a profile of high acceptance and high non-responsiveness at higher rates than EA families. In MA families, youth adherence to the traditional cultural value of familismo related to more positive perceptions of parenting behavior. Across ethnic groups, parent rejection only predicted higher externalizing problems in young adults when acceptance was high. The effect of parent harsh discipline on offspring stress response differed by ethnicity. In MA families, harsh discipline predicted dysregulated stress response in youth when acceptance was low. In EA families, harsh discipline did not relate to youth stress response. Overall, results increase the understanding of normative and adaptive parenting behaviors in MA families. Findings inform the development of culturally-competent parenting-focused interventions that can better prevent dysregulated stress response and externalizing behavior problems in ethnically diverse youth.
ContributorsMahrer, Nicole Eva (Author) / Luecken, Linda (Thesis advisor) / Wolchik, Sharlene (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Pina, Armando (Committee member) / Arizona State University (Publisher)
Created2015
Description
Software engineering education today is a technologically advanced and rapidly evolving discipline. Being a discipline where students not only design but also build new technology, it is important that they receive a hands on learning experience in the form of project based courses. To maximize the learning benefit, students must conduct project-based learning activities in a consistent rhythm, or cadence. Project-based courses that are augmented with a system of frequent, formative feedback helps students constantly evaluate their progress and leads them away from a deadline driven approach to learning.
One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.
A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.
A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
ContributorsXavier, Suhas (Author) / Gary, Kevin A (Thesis advisor) / Bansal, Srividya K (Committee member) / Sohoni, Sohum (Committee member) / Arizona State University (Publisher)
Created2016
Description
One of the most common errors developers make is to provide incorrect string
identifiers across the HTML5-JavaScript-CSS3 stack. The existing literature shows that a
significant percentage of defects observed in real-world codebases belong to this
category. Existing work focuses on semantic static analysis, while this thesis attempts to
tackle the challenges that can be solved using syntactic static analysis. This thesis
proposes a tool for quickly identifying defects at the time of injection due to
dependencies between HTML5, JavaScript, and CSS3, specifically in syntactic errors in
string identifiers. The proposed solution reduces the delta (time) between defect injection
and defect discovery with the use of a dedicated just-in-time syntactic string identifier
resolution tool. The solution focuses on modeling the nature of syntactic dependencies
across the stack, and providing a tool that helps developers discover such dependencies.
This thesis reports on an empirical study of the tool usage by developers in a realistic
scenario, with the focus on defect injection and defect discovery times of defects of this
nature (syntactic errors in string identifiers) with and without the use of the proposed
tool. Further, the tool was validated against a set of real-world codebases to analyze the
significance of these defects.
identifiers across the HTML5-JavaScript-CSS3 stack. The existing literature shows that a
significant percentage of defects observed in real-world codebases belong to this
category. Existing work focuses on semantic static analysis, while this thesis attempts to
tackle the challenges that can be solved using syntactic static analysis. This thesis
proposes a tool for quickly identifying defects at the time of injection due to
dependencies between HTML5, JavaScript, and CSS3, specifically in syntactic errors in
string identifiers. The proposed solution reduces the delta (time) between defect injection
and defect discovery with the use of a dedicated just-in-time syntactic string identifier
resolution tool. The solution focuses on modeling the nature of syntactic dependencies
across the stack, and providing a tool that helps developers discover such dependencies.
This thesis reports on an empirical study of the tool usage by developers in a realistic
scenario, with the focus on defect injection and defect discovery times of defects of this
nature (syntactic errors in string identifiers) with and without the use of the proposed
tool. Further, the tool was validated against a set of real-world codebases to analyze the
significance of these defects.
ContributorsKalsi, Manit Singh (Author) / Gary, Kevin A (Thesis advisor) / Lindquist, Timothy E (Committee member) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2016
Description
In the last decade, the number of people who own a mobile phone or portable electronic communication device has grown exponentially. Recent advances in smartphone technology have enabled mobile devices to provide applications (“mHealth apps”) to support delivering interventions, tracking health treatments, or involving a healthcare team into the treatment process and symptom monitoring. Although the popularity of mHealth apps is increasing, few lessons have been shared regarding user experience design and evaluation for such innovations as they relate to clinical outcomes. Studies assessing usability for mobile apps primarily rely on survey instruments. Though surveys are effective in determining user perception of usability and positive attitudes towards an app, they do not directly assess app feature usage, and whether feature usage and related aspects of app design are indicative of whether intended tasks are completed by users. This is significant in the area of mHealth apps, as proper utilization of the app determines compliance to a clinical study protocol. Therefore it is important to understand how design directly impacts compliance, specifically what design factors are prevalent in non-compliant users. This research studies the impact of usability features on clinical protocol compliance by applying a mixed methods approach to usability assessment, combining traditional surveys, log analysis, and clickstream analysis to determine the connection of design to outcomes. This research is novel in its construction of the mixed methods approach and in its attempt to tie usability results to impacts on clinical protocol compliance. The validation is a case study approach, applying the methods to an mHealth app developed for early prevention of anxiety in middle school students. The results of three empirical studies are shared that support the construction of the mixed methods approach.
ContributorsPatwardhan, Mandar (Author) / Gary, Kevin A (Thesis advisor) / Pina, Armando (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2016
Description
For the past decade, mobile health applications are seeing greater acceptance due to their potential to remotely monitor and increase patient engagement, particularly for chronic disease. Sickle Cell Disease is an inherited chronic disorder of red blood cells requiring careful pain management. A significant number of mHealth applications have been developed in the market to help clinicians collect and monitor information of SCD patients. Surveys are the most common way to self-report patient conditions. These are non-engaging and suffer from poor compliance. The quality of data gathered from survey instruments while using technology can be questioned as patients may be motivated to complete a task but not motivated to do it well. A compromise in quality and quantity of the collected patient data hinders the clinicians' effort to be able to monitor patient's health on a regular basis and derive effective treatment measures. This research study has two goals. The first is to monitor user compliance and data quality in mHealth apps with long and repetitive surveys delivered. The second is to identify possible motivational interventions to help improve compliance and data quality. As a form of intervention, will introduce intrinsic and extrinsic motivational factors within the application and test it on a small target population. I will validate the impact of these motivational factors by performing a comparative analysis on the test results to determine improvements in user performance. This study is relevant, as it will help analyze user behavior in long and repetitive self-reporting tasks and derive measures to improve user performance. The results will assist software engineers working with doctors in designing and developing improved self-reporting mHealth applications for collecting better quality data and enhance user compliance.
ContributorsRallabhandi, Pooja (Author) / Gary, Kevin A (Thesis advisor) / Gaffar, Ashraf (Committee member) / Bansal, Srividya (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2017