Matching Items (65)
Description
Continuous Delivery, as one of the youngest and most popular member of agile model family, has become a popular concept and method in software development industry recently. Instead of the traditional software development method, which requirements and solutions must be fixed before starting software developing, it promotes adaptive planning, evolutionary development and delivery, and encourages rapid and flexible response to change. However, several problems prevent Continuous Delivery to be introduced into education world. Taking into the consideration of the barriers, we propose a new Cloud based Continuous Delivery Software Developing System. This system is designed to fully utilize the whole life circle of software developing according to Continuous Delivery concepts in a virtualized environment in Vlab platform.
ContributorsDeng, Yuli (Author) / Huang, Dijiang (Thesis advisor) / Davulcu, Hasan (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2013
Description
The focus of this document is the examination of a new robot simulator developed to aid students in learning robotics programming and provide the ability to test their programs in a simulated world. The simulator, accessed via a website, provides a simulated environment, programming interface, and the ability to control a simulated robot. The simulated environment consists of a user-customizable maze and a robot, which can be controlled manually, via Web service, or by utilizing the Web programming interface. The Web programming interface provides dropdown boxes from which the users may select various options to program their implementations. It is designed to aid new students in the learning of basic skills and thought processes used to program robots. Data was collected and analyzed to determine how effective this system is in helping students learn. This included how quickly students were able to program the algorithms assigned to them and how many lines of code were used to implement them. Students' performance was also monitored to determine how well they were able to use the program and if there were any significant problems. The students also completed surveys to communicate how well the website helped them learn and understand various concepts. The data collected shows that the website was a helpful learning tool for the students and that they were able to use the programming interface quickly and effectively.
ContributorsDrown, Garrett (Author) / Tsai, Wei-Tek (Thesis advisor) / Chen, Yinong (Thesis advisor) / Claveau, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
Debugging is a boring, tedious, time consuming but inevitable step of software development and debugging multiple threaded applications with user interactions is even more complicated. Since concurrency and synchronism are normal features in Android mobile applications, the order of thread execution may vary in every run even with the same input. To make things worse, the target erroneous cases may happen just in a few specific runs. Besides, the randomness of user interactions makes the whole debugging procedure more unpredictable. Thus, debugging a multiple threaded application is a tough and challenging task. This thesis introduces a replay mechanism for debugging user interactive multiple threaded Android applications. The approach is based on the 'Lamport Clock' concept, 'Event Driven' implementation and 'Client-Server' architecture. The debugger tool described in this thesis provides a user controlled debugging environment where users or developers are allowed to use modified record application to generate a log file. During the record time, all the necessary events like thread creation, synchronization and user input are recorded. Therefore, based on the information contained in the generated log files, the debugger tool can replay the application off-line since log files provide the deterministic order of execution. In this case, user or developers can replay an application as many times as they need to pinpoint the errors in the applications.
ContributorsLu, He (Author) / Lee, Yann-Hang (Thesis advisor) / Fainekos, Georgios (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2012
Description
This study investigated the effects of concurrent audio and equivalent onscreen text on the ability of learners of English as a foreign language (EFL) to form associations between textual and aural forms of target vocabulary words. The study also looked at the effects of learner control over an audio sequence on the association of textual and aural forms of target words. Attitudes towards experimental treatments and reported level of cognitive load were also examined in the context of a computer-based multimedia instructional program. A total of 200 college students took part in the study. Participants were randomly assigned to experimental conditions in a 2 x 3 factorial design with level of learner control (learner-controlled vs. not-learner-controlled) and format of presentation of information (audio + no text vs. audio + full text vs. audio + keyword text) as factors. The subjects completed a pretest, a posttest, cognitive load questions, and an attitude questionnaire. The results revealed the following findings: (a) groups in the audio + keyword text conditions outperformed those in the audio + no text and audio + full text conditions on text-sound association, (b) within the audio + keyword text conditions, the learner-controlled group outperformed the not-learner-controlled group on text-sound association, (c) within the learner-controlled conditions, the audio + keyword group outperformed the audio + no text and audio + full text groups on text-sound association, (d) a redundancy effect was not found for any treatment condition, and (e) overall, participants had positive attitudes towards the treatments. Implications, limitations, and future directions are discussed within the frameworks of cognitive load theory and cognitive theory of multimedia learning.
ContributorsEnciso Bernal, Ana Maria (Author) / Nelson, Brian C. (Thesis advisor) / Savenye, Wilhelmina (Committee member) / Atkinson, Robert K. (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation describes Space Vector 1 and Space Vector 2, two video games that introduce Newtonian mechanics concepts. Space Vector 1 is a side-scrolling game, in which players choose to drop bombs or supplies. Players had to identify if the physics was correct during a mission, or they had to plot the trajectory of a falling object, which was then simulated. In Space Vector 2, players were given velocity and acceleration values and had to plot the trajectory of a spaceship across a grid, or players were given a trajectory of a spaceship on a grid and had to program the velocity and acceleration values to produce the trajectory. Space Vector 1 was evaluated with 65 college undergraduates. Space Vector 2 was evaluated with 18 high school students. All participants were given a subset of the Force Concept Inventory, a standard assessment tool in physics education, as a pretest and posttest. Space Vector 1 was evaluated with a single group pretest-posttest design. Space Vector 2 was evaluated with a 2 x 2 ANOVA, where the factors were game mechanic (prediction mechanic or programming mechanic) and bonus questions (bonus question after a mission or no bonus question). Bayesian statistical methods were used for the data analysis. The best estimate for the average change in test scores for Space Vector 1 was a score gain of 1.042 (95% Highest Density Interval (HDI) [0.613, 1.487]) with an effect size of 0.611 (95% HDI [0.327, 0.937]). The best estimate for the grand mean of change scores in Space Vector 2 was an increase of 0.78 (95% HDI [-0.3, 1.85]) with an effect size of 0.379 (95% HDI [-0.112, 0.905]). The prediction
o bonus question version produced the largest change in score, where the best estimate for the mean change score was an increase of 1.2. The estimation intervals for the Space Vector 2 results were wide, and all included zero as a credible value.
o bonus question version produced the largest change in score, where the best estimate for the mean change score was an increase of 1.2. The estimation intervals for the Space Vector 2 results were wide, and all included zero as a credible value.
ContributorsKeylor, Eric Karl (Author) / Gee, James P. (Thesis advisor) / Stevens, Scott M. (Committee member) / Nelson, Brian C. (Committee member) / Atkinson, Robert K. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Computational thinking, the creative thought process behind algorithmic design and programming, is a crucial introductory skill for both computer scientists and the population in general. In this thesis I perform an investigation into introductory computer science education in the United States and find that computational thinking is not effectively taught at either the high school or the college level. To remedy this, I present a new educational system intended to teach computational thinking called Genost. Genost consists of a software tool and a curriculum based on teaching computational thinking through fundamental programming structures and algorithm design. Genost's software design is informed by a review of eight major computer science educational software systems. Genost's curriculum is informed by a review of major literature on computational thinking. In two educational tests of Genost utilizing both college and high school students, Genost was shown to significantly increase computational thinking ability with a large effect size.
ContributorsWalliman, Garret (Author) / Atkinson, Robert (Thesis advisor) / Chen, Yinong (Thesis advisor) / Lee, Yann-Hang (Committee member) / Arizona State University (Publisher)
Created2015
Description
This study purposed to determine the effect of an endogenously designed instructional game on conceptual understanding of the associative and distributive properties of multiplication. Additional this study sought to investigate if performance on measures of conceptual understanding taken prior to and after game play could serve as predictors of game performance. Three versions of an instructional game, Shipping Express, were designed for the purposes of this study. The endogenous version of Shipping Express integrated the associative and distributive properties of multiplication within the mechanics, while the exogenous version had the instructional content separate from game play. A total of 111 fourth and fifth graders were randomly assigned to one of three conditions (endogenous, exogenous, and control) and completed pre and posttest measures of conceptual understanding of the associative and distributive properties of multiplication, along with a questionnaire. The results revealed several significant results: 1) there was a significant difference between participants' change in scores on the measure of conceptual understanding of the associative property of multiplication, based on the version of Shipping Express they played. Participants who played the endogenous version of Shipping Express had on average higher gains in scores on the measure of conceptual understanding of the associative property of multiplication than those who played the other versions of Shipping Express; 2) performance on the measures of conceptual understanding of the distributive property collected prior to game play were related to performance within the endogenous game environment; and 3) participants who played the control version of Shipping Express were on average more likely to have a negative attitude towards continuing game play on their own compared to the other versions of the game. No significant differences were found in regards to changes in scores on the measure of conceptual understanding of the distributive property based on the version of Shipping Express played, post hoc pairwise comparisons, and changes on scores on question types within the conceptual understanding of the associative and distributive property of multiplication measures. The findings from this study provide some support for a move towards the design and development of endogenous instructional games. Additional implications for the learning through digital game play and future research directions are discussed.
ContributorsDenham, Andrew (Author) / Nelson, Brian C. (Thesis advisor) / Atkinson, Robert K. (Committee member) / Middleton, James (Committee member) / VanLehn, Kurt (Committee member) / Arizona State University (Publisher)
Created2012
Description
Guided by cognitive, socio-cognitive, and socio-cultural learning theories, large-scale studies over multiple semesters, multiple instructors and at two different institutions have been performed in order to understand the factors that contribute to student performance in general organic chemistry. Students’ cognitive abilities were assessed in a new way based on a categorization of problem types in a standard organic chemistry curriculum. Problem types that required higher cognitive load were found to be more predictive of overall course performance. However, student performance on high cognitive load problems was different when compared in terms of non-cognitive factors, e.g. whether they were pre-health students or not. These results suggested that organic chemistry performance may be significantly influenced by non-cognitive factors. Students’ motivation and related self-regulation factors were then studied using an instrument specifically designed for general organic chemistry, the Organic Chemistry Motivation Survey. Of all the factors examined, self-efficacy was found to be the most significant predictor of performance. Socio-cultural factors were also studied using a newly developed instrument for measuring college students’ cultural and social capital, the Science Capital Questionnaire (SCQ). Of the different socio-cultural variables measured by the SCQ, students’ social connections in college were found to be most predictive of organic chemistry performance. Finally, cognitive and socio-cognitive variables were studied together in the context of gender differences in organic chemistry. Females were found to underperform in comparison to the males. This gap was found to be alarmingly large on the basis of final letter grade, in some semesters the percentage of males earning an A grade was twice as large as that for females. Spatial ability was not a factor that contributed to this difference, nor was the gender of the instructor. Instead, self-efficacy was found to be both significantly different between males and females, and also the factor that connected most strongly to course performance. It is suggested that sociocultural factors be the subject of further study in college science courses.
ContributorsAustin, Ara Cho (Author) / Gould, Ian R. (Thesis advisor) / Atkinson, Robert K. (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2018
Description
Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. MAR labs extends Arizona State University’s Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments.
ContributorsDe La Rosa, Matthew Lee (Author) / Chen, Yinong (Thesis advisor) / Collofello, James (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2018
Description
There currently exist various challenges in learning cybersecuirty knowledge, along with a shortage of experts in the related areas, while the demand for such talents keeps growing. Unlike other topics related to the computer system such as computer architecture and computer network, cybersecurity is a multidisciplinary topic involving scattered technologies, which yet remains blurry for its future direction. Constructing a knowledge graph (KG) in cybersecurity education is a first step to address the challenges and improve the academic learning efficiency.
With the advancement of big data and Natural Language Processing (NLP) technologies, constructing large KGs and mining concepts, from unstructured text by using learning methodologies, become possible. The NLP-based KG with the semantic similarity between concepts has brought inspiration to different industrial applications, yet far from completeness in the domain expertise, including education in computer science related fields.
In this research work, a KG in cybersecurity area has been constructed using machine-learning-based word embedding (i.e., mapping a word or phrase onto a vector of low dimensions) and hyperlink-based concept mining from the full dataset of words available using the latest Wikipedia dump. The different approaches in corpus training are compared and the performance based on different similarity tasks is evaluated. As a result, the best performance of trained word vectors has been applied, which is obtained by using Skip-Gram model of Word2Vec, to construct the needed KG. In order to improve the efficiency of knowledge learning, a web-based front-end is constructed to visualize the KG, which provides the convenience in browsing related materials and searching for cybersecurity-related concepts and independence relations.
With the advancement of big data and Natural Language Processing (NLP) technologies, constructing large KGs and mining concepts, from unstructured text by using learning methodologies, become possible. The NLP-based KG with the semantic similarity between concepts has brought inspiration to different industrial applications, yet far from completeness in the domain expertise, including education in computer science related fields.
In this research work, a KG in cybersecurity area has been constructed using machine-learning-based word embedding (i.e., mapping a word or phrase onto a vector of low dimensions) and hyperlink-based concept mining from the full dataset of words available using the latest Wikipedia dump. The different approaches in corpus training are compared and the performance based on different similarity tasks is evaluated. As a result, the best performance of trained word vectors has been applied, which is obtained by using Skip-Gram model of Word2Vec, to construct the needed KG. In order to improve the efficiency of knowledge learning, a web-based front-end is constructed to visualize the KG, which provides the convenience in browsing related materials and searching for cybersecurity-related concepts and independence relations.
ContributorsLin, Fanjie (Author) / Huang, Dijiang (Thesis advisor) / Hsiao, I-Han (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2018