About: Making Sense of Student Success and Risk Through Unsupervised Machine Learning and Interactive Storytelling

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Making Sense of Student Success and Risk Through Unsupervised Machine Learning and Interactive Storytelling
Creator	Al-Doulat, Ahmad Al-Hossami, Erfan Benedict, Aileen Dorodchi, Mohsen Dou, Wenwen Karduni, Alireza Maher, Mary Niu, Xi Nur, Nasheen
Source	PMC
abstract	This paper presents an interactive AI system to enable academic advisors and program leadership to understand the patterns of behavior related to student success and risk using data collected from institutional databases. We have worked closely with advisors in our development of an innovative temporal model of student data, unsupervised k-means algorithm on the data, and interactive user experiences with the data. We report on the design and evaluation of FIRST, Finding Interesting stoRies about STudents, that provides an interactive experience in which the advisor can: select relevant student features to be included in a temporal model, interact with a visualization of unsupervised learning that present patterns of student behavior and their correlation with performance, and to view automatically generated stories about individual students based on student data in the temporal model. We have developed a high fidelity prototype of FIRST using 10 years of student data in our College. As part of our iterative design process, we performed a focus group study with six advisors following a demonstration of the prototype. Our focus group evaluation highlights the sensemaking value in the temporal model, the unsupervised clusters of the behavior of all students in a major, and the stories about individual students.
has issue date	2020-06-09(xsd:dateTime)
bibo:doi	10.1007/978-3-030-52237-7_1
has license	no-cc
sha1sum (hex)	7eb3f01260d391b00849d06dc8951420a4ea60e8
schema:url	https://doi.org/10.1007/978-3-030-52237-7_1
resource representing a document's title	Making Sense of Student Success and Risk Through Unsupervised Machine Learning and Interactive Storytelling
has PubMed Central identifier	PMC7334148
schema:publication	Artificial Intelligence in Education
resource representing a document's body	covid:7eb3f01260d391b00849d06dc8951420a4ea60e8#body_text
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