argues that the value of data lies, not only in opportunities for increased personalization within MOOCs themselves, but in their potential to inform decisions about more traditional learning environments as well. For the Chronicle of Higher Education, Jeffrey R. Young sat down for a conversation with L. Todd Rose to discuss the opportunities that data afford for personalizing content delivery, but also the challenges of data sharing, particularly in the case of MOOCs, where sharing of educational data is largely precluded as a consequence of existing business models. Lastly, in an article written for ACM Queue, Daries, et al add that the sharing of MOOC data is not only limited by business considerations, but also out of respect for student privacy. They observe that anonymization processes often function to radically undermine the possibilities for future analysis. The authors argue that, even if researchers can identify individuals and their actions, privacy can still be upheld by if those researchers are bound to an ethical and legal framework.
Another set of ethical issues that were raised this week involve the intersection of analytics and the humanities. Joshua Kim sparked a conversation about the place of analytics in the liberal arts. In the discussion following Kim’s post, greatest attention was paid to issues of definition: What is ‘assessment’? What are the ‘Liberal Arts’? (Mike Sharkey, for example, suggests that the liberal arts simply imply “small classes and a high-touch environment,” and argues that analytics offers very little value in such contexts. Timothy Harfield argues that the liberal arts provide a critical perspective on analytics, and are crucial to ensuring that educational institutions are learning-driven rather than data-driven). Lastly, in an article for Educause Review Online, James E. Willis discusses the failure of ethical discussions in learning analytics, and offers an ethical framework that highlights some of the complexities involved in the debate. He categorizes ethical questions in terms of three distinct philosophical perspectives, what he calls “Moral Utopianism,” “Moral Ambiguity,” and “Moral Nihilism.” The framework itself is at once overly pedantic and lacking in the clarity and sophistication that one would expect from a piece with tacit claims to a foundation in the history of philosophy, but nevertheless represents an interesting attempt to push the debate outside of the more comfortable legal questions that most often frame conversations about data and privacy.
Recent Blog Posts
- Why Students Should Own Their Educational Data by Jeffrey R. Young
- Learning Analytics and Ethics: A Framework beyond Utilitarianism by James E. Willis, III
- The Modern Classroom: Students, Teachers and Data-Driven Education by James O’Brien
- Analytics and the Liberal Arts by Joshua Kim
- MOOCs: learning about online learning, one click at a time by Gregor Kennedy
Privacy, Anonymity, and Big Data in the Social Sciences
Jon P. Daries, Justin Reich, Jim Waldo, Elise M. Young, Jonathan Whittinghill, Daniel Thomas Seaton, Andrew Dean Ho, Isaac Chuang
Open data has tremendous potential for science, but, in human subjects research, there is a tension between privacy and releasing high-quality open data. Federal law governing student privacy and the release of student records suggests that anonymizing student data protects student privacy. Guided by this standard, we de-identified and released a data set from 16 MOOCs (massive open online courses) from MITx and HarvardX on the edX platform. In this article, we show that these and other de-identification procedures necessitate changes to data sets that threaten replication and extension of baseline analyses. To balance student privacy and the benefits of open data, we suggest focusing on protecting privacy without anonymizing data by instead expanding policies that compel researchers to uphold the privacy of the subjects in open data sets. If we want to have high-quality social science research and also protect the privacy of human subjects, we must eventually have trust in researchers. Otherwise, we’ll always have the strict tradeoff between anonymity and science illustrated here.
Using Learning Analytics to Analyze Writing Skills of Students: A Case Study in a Technological Common Core Curriculum Course
Chi-Un Lei, Ka Lok Man, and T. O. Ting
Pedagogy with learning analytics is shown to facil- itate the teaching-learning process through analyzing student’s behaviours. In this paper, we explored the possibility of using learning analytics tools Coh-Metrix and Lightside for analyzing and improving writing skills of students in a technological common core curriculum course. In this study, we i) investigated linguistic characteristics of student’s essays, and ii) applied a machine learning algorithm for giving instant sketch feedback to students. Results illustrated the necessity of improving student’s writing skills in their university learning through e- learning technologies, so that students can effectively circulate their ideas to the public in the future.
Calls for Papers
CALL FOR CHAPTERS: Developing Effective Educational Experiences through Learning Analytics
Edge Hill University Press (ABSTRACT SUBMISSION DEADLINE: 15 September 2014)
CALL FOR PAPERS: 5th International Learning Analytics and Knowledge (LAK) Conference
Marist College (Poughkeepsie, NY) | 16-20 March 2015 (SUBMISSION DEADLINE: 14 October 2014)
swirl: Learn R, in R
swirl teaches you R programming and data science interactively, at your own pace, and right in the R console!
6-9 October 2014
Learning Analytics Week
École polytechnique fédérale de Lausanne
15 October 2014
ALE Speaker Series: Charles Dziuban on Engaging Students in an Engaging Educational Environment
Emory University (Streaming Available)
20 October 2014
Data, Analytics and Learning: An introduction to the logic and methods of analysis of data to improve teaching and learning
University of Texas Arlington | EdX