Matching Items (3)
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- All Subjects: Website
- All Subjects: tutoring
- Creators: Ahmad, Altaf
Description
For students on a college campus, many courses can present challenges to them academically. Some universities have taken an initiative to respond to this by offering tutoring opportunities at a central location. Generally this provides help for some struggling students, but others are left with many questions unanswered. Two primary reasons for this are that some tutoring services are broad in scope and that there may not be sufficient one-on-one time with a tutor. With the development of a mobile application, a solution is possible to improve upon the tutoring experience for all students. The concept revolves around the formation of a labor market of freelancers, known as a gig economy, to create a large supply of tutors who can provide their services to a student looking for help in a specific course. A strategic process was followed to develop this mobile application, called Tuzee. To begin, an early concept and design was drafted to shape a clear vision statement and effective user experience. Planning and research followed, where technical requirements including an efficient database and integrated development environment were selected. After these prerequisites, the development stage of the application started and a working app produced. Subsequently, a business model was devised along with possible features to be added upon a successful launch. With a peer-to-peer approach powering the app, monitoring user engagement lies as a core principle for consistent growth. The vision statement will frequently be referred to: enhance university academics by enabling the interaction of students with each other.
ContributorsArcaro, Daniel James (Author) / Ahmad, Altaf (Thesis director) / Sopha, Matthew (Committee member) / Department of Information Systems (Contributor) / WPC Graduate Programs (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis paper contains all the information, processes, and scripts used to create the final SQL database and website for use by University Housing at Arizona State University. This project aims to resolve problems currently facing University Housing's Community Assistants with their resource distribution and processes.
ContributorsZugelder, Micayla Ann (Author) / Moser, Kathleen (Thesis director) / Ahmad, Altaf (Committee member) / Department of Information Systems (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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