Suffolk University Academic Catalog, College of Arts and Sciences and Sawyer Business School, 2019-2020

This catalog contains information for the undergraduate programs in the College of Arts and Sciences and the Sawyer Business School. The catalog is a captured pdf version of the Suffolk website, so some pages have repeated information and many links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included, with lists of CAS and SBS courses starting on page 1258. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1181/thumbnail.jp

2014-2015 Graduate Catalog

https://digitalcommons.sacredheart.edu/g_cat/1000/thumbnail.jp

Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy

By tracking, aggregating, and analyzing student profiles along with students’ digital and analog behaviors captured in information systems, universities are beginning to open the black box of education using learning analytics technologies. However, the increase in and usage of sensitive and personal student data present unique privacy concerns. I argue that privacy-as-control of personal information is autonomy promoting, and that students should be informed about these information flows and to what ends their institution is using them. Informed consent is one mechanism by which to accomplish these goals, but Big Data practices challenge the efficacy of this strategy. To ensure the usefulness of informed consent, I argue for the development of Platform for Privacy Preferences (P3P) technology and assert that privacy dashboards will enable student control and consent mechanisms, while providing an opportunity for institutions to justify their practices according to existing norms and values

Eliciting students' preferences for the use of their data for learning analytics. A crowdsourcing approach.

Research on student perspectives of learning analytics suggests that students are generally unaware of the collection and use of their data by their learning institutions, and they are often not involved in decisions about whether and how their data are used. To determine the influence of risks and benefits awareness on students’ data use preferences for learning analytics, we designed two interventions: one describing the possible privacy risks of data use for learning analytics and the second describing the possible benefits. These interventions were distributed amongst 447 participants recruited using a crowdsourcing platform. Participants were randomly assigned to one of three experimental groups – risks, benefits, and risks and benefits – and received the corresponding intervention(s). Participants in the control group received a learning analytics dashboard (as did participants in the experimental conditions). Participants’ indicated the motivation for their data use preferences. Chapter 11 will discuss the implications of our findings in relation to how to better support learning institutions in being more transparent with students about the practice of learning analytics

Eliciting students' preferences for the use of their data for learning analytics. A crowdsourcing approach.

Research on student perspectives of learning analytics suggests that students are generally unaware of the collection and use of their data by their learning institutions, and they are often not involved in decisions about whether and how their data are used. To determine the influence of risks and benefits awareness on students’ data use preferences for learning analytics, we designed two interventions: one describing the possible privacy risks of data use for learning analytics and the second describing the possible benefits. These interventions were distributed amongst 447 participants recruited using a crowdsourcing platform. Participants were randomly assigned to one of three experimental groups – risks, benefits, and risks and benefits – and received the corresponding intervention(s). Participants in the control group received a learning analytics dashboard (as did participants in the experimental conditions). Participants’ indicated the motivation for their data use preferences. Chapter 11 will discuss the implications of our findings in relation to how to better support learning institutions in being more transparent with students about the practice of learning analytics