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Description
Most collegiate organizations aim to unite students with common interests and engage them in a like-minded community of peers. A significant sub-group of these organizations are classified under sororities and fraternities and commonly known as Greek Life. Member involvement is a crucial element for Greek Life as participation in philanthropic events, chapter meetings, rituals, recruitment events, etc. often reflects the state of the organization. The purpose of this project is to create a web application that allows members of an Arizona State University sorority to view their involvement activity as outlined by the chapter. Maintaining the balance between academics, sleep, a social life, and extra-curricular activities/organizations can be difficult for college students. With the use of this website, members can view their attendances, absences, and study/volunteer hours to know their progress towards the involvement requirements set by the chapter. This knowledge makes it easier to plan schedules and alleviate some stress associated with the time-management of sorority events, assignments/homework, and studying. It is also designed for the sorority leadership to analyze and track the participation of the membership. Members can submit their participation in events, making the need for manual counting and calculations disappear. The website administrator(s) can view and approve data from any and all members. The website was developed using HTML, CSS, and JavaScript in conjunction with Firebase for the back-end database. Human-Computer Interaction (HCI) tools and techniques were used throughout the development process to aide in prototyping, visual design, and evaluation. The front-end appearance of the website was designed to mimic the data manipulation used in the current involvement tracking system while presenting it in a more personalized and aesthetically pleasing manner.
ContributorsKolker, Madysen (Author) / McDaniel, Troy (Thesis director) / Tadayon, Arash (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
This paper introduces MisophoniAPP, a new website for managing misophonia. It will briefly discuss the nature of this chronic syndrome, which is the experience of reacting strongly to certain everyday sounds, or “triggers”. Various forms of Cognitive Behavioral Therapy and the Neural Repatterning Technique are currently used to treat misophonia, but they are not guaranteed to work for every patient. Few apps exist to help patients with their therapy, so this paper describes the design and creation of a new website that combines exposure therapy,
relaxation, and gamification to help patients alleviate their misophonic reflexes.
relaxation, and gamification to help patients alleviate their misophonic reflexes.
ContributorsNoziglia, Rachel Elisabeth (Author) / McDaniel, Troy (Thesis director) / Anderson, Derrick (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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