Matching Items (13)
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- Creators: Computer Science and Engineering Program
- Creators: Mazzola, Daniel
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Member of: Theses and Dissertations
Description
CourseKarma is a web application that engages students in their own learning through peer-driven social networking. The influence of technology on students is advancing faster than the school system, and a major gap still lingers between traditional learning techniques and the fast-paced, online culture of today's generation. CourseKarma enriches the educational experience of today's student by creating a space for collaborative inquiry as well as illuminating the opportunities of self and group learning through online collaboration. The features of CourseKarma foster this student-driven environment. The main focus is on a news-feed and Question and Answer component that provides a space for students to share instant updates as well ask and answer questions of the community. The community can be as broad as the entire ASU student body, as specific as students in BIO155, or even more targeted via specific subjects and or skills. CourseKarma also provides reputation points, which are the sum of all of their votes received, identifying the individual's level and or ranking in each subject or class. This not only gamifies the usual day-to-day learning environment, but it also provides an in-depth analysis of the individual's skills, accomplishments, and knowledge. The community is also able to input and utilize course and professor descriptions/feedback. This will be in a review format providing the students an opportunity to share and give feedback on their experience as well as providing incoming students the opportunity to be prepared for their future classes. All of the student's contributions and collaborative activity within CourseKarma is displayed on their personal profile creating a timeline of their academic achievements. The application was created using modern web programming technologies such as AngualrJS, Javascript, jQuery, Bootstrap, HTML5, CSS3 for the styling and front-end development, Mustache.js for client side templating, and Firebase AngularFire as the back-end and NoSQL database. Other technologies such as Pivitol Tracker was used for project management and user story generation, as well as, Github for version control management and repository creation. Object-oreinted programming concepts were heavily present in the creation of the various data structures, as well as, a voting algorithm was used to manage voting of specific posts. Down the road, CourseKarma could even be a necessary add-on within LinkedIn or Facebook that provides a quick yet extremely in-depth look at an individuals' education, skills, and potential to learn \u2014 based all on their actual contribution to their academic community rather than just a text they wrote up.
ContributorsCho, Sungjae (Author) / Mayron, Liam (Thesis director) / Lobock, Alan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Arts, Media and Engineering (Contributor)
Created2015-05
Description
Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.
ContributorsLi, Vincent (Author) / Turaga, Pavan (Thesis director) / Buman, Matthew (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Compass portal features tools that help teachers, psychologists, behavioral specialists gain insights on students’ performance through activities they have completed.
ContributorsNallagula, Nithin Sagar (Co-author) / Shah, Neha (Co-author) / Gary, Kevin (Thesis director) / Mehlhase, Alexadnra (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
COMPASS portal features tools that help teachers, psychologists, behavioral Specialists gain insights on students’ performance through activities they have completed.
ContributorsShah, Neha Manish (Co-author) / Nallagula, Nithin Sagar (Co-author) / Gary, Kevin (Thesis director) / Mehlhase, Alexandra (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Anthemy is a web app that I created so that Spotify users could connect with other uses and see their listening statistics. The app has a chat feature that matches concurrent users based on a variety of search criteria, as well as a statistics page that contains a breakdown of a user's top artists, songs, albums, and genres as well as a detailed breakdown of each of their liked playlists.
ContributorsJackman, Benjamin (Author) / Roumina, Kavous (Thesis director) / Mazzola, Daniel (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2021-12
ContributorsJackman, Benjamin (Author) / Roumina, Kavous (Thesis director) / Mazzola, Daniel (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2021-12
ContributorsJackman, Benjamin (Author) / Roumina, Kavous (Thesis director) / Mazzola, Daniel (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2021-12
Description
The aim of this project was to provide college applicants with the ability to apply using a video instead of an essay. These videos are analyzed automatically and their scripts are taken and submitted with the application. This was implemented through the use of Amazon Web Services (AWS) and their S3 buckets along with their speech to text transcription service. This type of application process can give admissions teams the opportunity to get to know who will potentially be attending their university and allows the applicants to express themselves to admissions teams in a new and unique way.
ContributorsStephan, Meagan (Co-author) / Pratt, Devan (Co-author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
The Tutoring Center Management System is a web-based application for ASU’s University Academic Success Programs (UASP) department, particularly the Math Tutoring Center. It is aimed at providing a user-friendly interface to track queue requests from students visiting the tutoring centers and convert that information into actionable data with the potential to live-track and assess the performance of each tutoring center and each tutor. Numerous UASP processes are streamlined to create an efficient and integrated workflow, such as tutor scheduling, tutor search, shift coverage requests, and analytics. The intended users of the application feature ASU students and the UASP staff, including tutors and supervisors.
ContributorsJain, Prakshal (Co-author) / Gulati, Sachit (Co-author) / Nakamura, Mutsumi (Thesis director) / Selgrad, Justin (Committee member) / Department of Information Systems (Contributor) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Although Spotify’s extensive library of songs are often seen broken up by “Top 100” and main lyrical genres, these categories are primarily based on popularity, artist and general mood alone. If a user wanted to create a playlist based on specific or situationally specific qualifiers from their own downloaded library, he/she would have to hand pick songs that fit the mold and create a new playlist. This is a time consuming process that may not produce the most efficient result due to human error. The objective of this project, therefore, was to develop an application to streamline this process, optimize efficiency, and fill this user need.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
ContributorsDiMuro, Louis (Author) / Balasooriya, Janaka (Thesis director) / Chen, Yinong (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05