Trade-off between interoperability and data collection performance when designing an architecture for learning analytics

2017 ◽  
Vol 68 ◽  
pp. 31-37 ◽  
Author(s):  
Juan Manuel Dodero ◽  
Enrique Juan González-Conejero ◽  
Guillermo Gutiérrez-Herrera ◽  
Sonia Peinado ◽  
José Tomás Tocino ◽  
...  
Keyword(s):  
Data Collection ◽  
Learning Analytics ◽  
Trade Off

Achieving a Balance Between Privacy Protection and Data Collection: A Field Experimental Examination of a Theory-Driven Information Technology Solution

2021 ◽  
Author(s):  
Bailing Liu ◽  
Paul A. Pavlou ◽  
Xiufeng Cheng
Keyword(s):  
Information Technology ◽  
Data Collection ◽  
Customer Service ◽  
Privacy Protection ◽  
Information Disclosure ◽  
Personal Information ◽  
Privacy Policy ◽  
Trade Off ◽  
Policy Application ◽  
Mobile Banking Applications

Companies face a trade-off between creating stronger privacy protection policies for consumers and employing more sophisticated data collection methods. Justice-driven privacy protection outlines a method to manage this trade-off. We built on the theoretical lens of justice theory to integrate justice provision with two key privacy protection features, negotiation and active-recommendation, and proposed an information technology (IT) solution to balance the trade-off between privacy protection and consumer data collection. In the context of mobile banking applications, we prototyped a theory-driven IT solution, referred to as negotiation, active-recommendation privacy policy application, which enables customer service agents to interact with and actively recommend personalized privacy policies to consumers. We benchmarked our solution through a field experiment relative to two conventional applications: an online privacy statement and a privacy policy with only a simple negotiation feature. The results showed that the proposed IT solution improved consumers’ perceived procedural justice, interactive justice, and distributive justice and increased their psychological comfort in using our application design and in turn reduced their privacy concerns, enhanced their privacy awareness, and increased their information disclosure intentions and actual disclosure behavior in practice. Our proposed design can provide consumers better privacy protection while ensuring that consumers voluntarily disclose personal information desirable for companies.

The Marginal Syllabus

2018 ◽  
pp. 17-58 ◽  
Author(s):  
Jeremiah H. Kalir ◽  
Francisco Perez
Keyword(s):  
Discourse Analysis ◽  
Data Collection ◽  
Learning Analytics ◽  
Meaning Making ◽  
Educational Equity ◽  
Political Agency ◽  
Design Experiment ◽  
Social Design ◽  
Web Annotation

This case study examines educator learning as mediated by open web annotation among sociopolitical texts and contexts. The chapter introduces annotation practices and conceptualizes intertextuality to describe how open web annotation creates dialogic spaces which gather together people and texts, coordinates meaning-making, and encourages political agency. This perspective on texts-as-contexts is used to present and analyze educator participation in the Marginal Syllabus, a social design experiment that leverages open web annotation to foster conversation about educational equity. One conversation from the Marginal Syllabus is analyzed using mixed method approaches to data collection, analysis, and the presentation of findings. Learning analytics and discourse analysis detail how open web annotation mediated educator participation among sociopolitical texts and contexts of professional relevance. The chapter concludes by discussing open web annotation as a means of coordinating educator participation in public conversations about sociopolitical issues related to educational equity.

scholarly journals Disparities in Students’ Propensity to Consent to Learning Analytics

2020 ◽  
Author(s):  
Warren Li ◽  
Kaiwen Sun ◽  
Florian Schaub ◽  
Christopher Brooks
Keyword(s):  
Data Collection ◽  
University Students ◽  
Predictive Models ◽  
Learning Analytics ◽  
Online Survey ◽  
Data Access ◽  
Institutional Trust ◽  
Perceived Benefits ◽  
Opt Out ◽  
And Gender

Use of university students’ educational data for learning analytics has spurred a debate about whether and how to provide students with agency regarding data collection and use. A concern is that students opting out of learning analytics may skew predictive models, in particular if certain student populations disproportionately opt out and biases are unintentionally introduced into predictive models. We investigated university students’ propensity to consent to learning analytics through an email prompt, and collected respondents’ perceived benefits and privacy concerns regarding learning analytics in a subsequent online survey. In particular, we studied whether and why students’ consent propensity differs among student subpopulations by sending our email prompt to a sample of 4,000 students at our institution stratified by ethnicity and gender. 272 students interacted with the email, of which 119 completed the survey. We identified that institutional trust, concerns with the amount of data collection versus perceived benefits, and comfort with instructors’ data access were key determinants in students’ decision to participate in learning analytics. We find that students identifying ethnically as Black were significantly less likely to respond and self-reported lower levels of institutional trust. Female students reported concerns with data collection but were also more comfortable with use of their data by instructors . Students’ comments corroborate these findings and we discuss the implications of these concerns on educational data collection.

scholarly journals Disparities in Students’ Propensity to Consent to Learning Analytics

2021 ◽  
Author(s):  
Warren Li ◽  
Kaiwen Sun ◽  
Florian Schaub ◽  
Christopher Brooks
Keyword(s):  
Data Collection ◽  
University Students ◽  
Predictive Models ◽  
Learning Analytics ◽  
Online Survey ◽  
Institutional Trust ◽  
Perceived Benefits ◽  
Learning Engagement ◽  
Opt Out ◽  
And Gender

AbstractUse of university students’ educational data for learning analytics has spurred a debate about whether and how to provide students with agency regarding data collection and use. A concern is that students opting out of learning analytics may skew predictive models, in particular if certain student populations disproportionately opt out and biases are unintentionally introduced into predictive models. We investigated university students’ propensity to consent to learning analytics through an email prompt, and collected respondents’ perceived benefits and privacy concerns regarding learning analytics in a subsequent online survey. In particular, we studied whether and why students’ consent propensity differs among student subpopulations bysending our email prompt to a sample of 4,000 students at our institution stratified by ethnicity and gender. 272 students interacted with the email, of which 119 also completed the survey. We identified that institutional trust, concerns with the amount of data collection versus perceived benefits, and comfort with instructors’ data use for learning engagement were key determinants in students’ decision to participate in learning analytics. We find that students identifying ethnically as Black were significantly less likely to respond and self-reported lower levels of institutional trust. Female students reported concerns with data collection but were also more comfortable with use of their data by instructors for learning engagement purposes. Students’ comments corroborate these findings and suggest that agency alone is insufficient; institutional leaders and instructors also play a large role in alleviating the issue of bias.

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