Data Analytics in Higher Education: Key Concerns and Open Questions

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas

Two systems developed for purifying inert atmospheres

Two systems, one for helium and one for argon, are used for purifying inert atmospheres. The helium system uses an activated charcoal bed at liquid nitrogen temperature to remove oxygen and nitrogen. The argon system uses heated titanium sponge to remove nitrogen and copper wool beds to remove oxygen. Both use molecular sieves to remove water vapor

Use of steel and tantalum apparatus for molten Cd-Mg-Zn alloys

Steel and tantalum apparatus contains various ternary alloys of cadmium, zinc, and magnesium used in pyrochemical processes for the recovery of uranium-base reactor fuels. These materials exhibit good corrosion resistance at the high temperatures necessary for fuel separation in liquid metal-molten salt solvents

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

Advising the whole student: eAdvising analytics and the contextual suppression of advisor values

Institutions are applying methods and practices from data analytics under the umbrella term of “learning analytics” to inform instruction, library practices, and institutional research, among other things. This study reports findings from interviews with professional advisors at a public higher education institution. It reports their perspective on their institution’s recent adoption of eAdvising technologies with prescriptive and predictive advising affordances. The findings detail why advisors rejected the tools due to usability concerns, moral discomfort, and a belief that using predictive measures violated a professional ethical principle to develop a comprehensive understanding of their advisees. The discussion of these findings contributes to an emerging branch of educational data mining and learning analytics research focused on social and ethical implications. Specifically, it highlights the consequential effects on higher education professional communities (or “micro contexts”) due to the ascendancy of learning analytics and data-driven ideologies

Mechanistic target of rapamycin (mTOR) signaling genes in decapod crustaceans: cloning and tissue expression of mTOR, Akt, Rheb, and S6 kinase in the green crab, Carcinus maenas, and blackback land crab, Gecarcinus lateralis

Mechanistic target of rapamycin (mTOR) controls global translation of mRNA into protein by phosphorylating p70 S6 kinase (S6K) and eIF4E-binding protein-1. Akt and Rheb, a GTP-binding protein, regulate mTOR protein kinase activity. Molting in crustaceans is regulated by ecdysteroids synthesized by a pair of molting glands, or Y-organs (YOs), located in the cephalothorax. During premolt, the YOs hypertrophy and increase production of ecdysteroids. Rapamycin (1 μM) inhibited ecdysteroid secretion in Carcinus maenas and Gecarcinus lateralis YOs in vitro, indicating that ecdysteroidogenesis requires mTOR-dependent protein synthesis. The effects of molting on the expression of four key mTOR signaling genes (mTOR, Akt, Rheb, and S6K) in the YO was investigated. Partial cDNAs encoding green crab (C. maenas) mTOR (4031 bp), Akt (855 bp), and S6K (918 bp) were obtained from expressed sequence tags. Identity/similarity of the deduced amino acid sequence of the C. maenas cDNAs to human orthologs were 72%/81% for Cm-mTOR, 58%/73% for Cm-Akt, and 77%/88% for Cm-S6K. mTOR, Akt, S6K, and elongation factor 2 (EF2) in C. maenas and blackback land crab (G. lateralis) were expressed in all tissues examined. The two species differed in the effects of molting on gene expression in the YO. In G. lateralis, Gl-mTOR, Gl-Akt, and Gl-EF2 mRNA levels were increased during premolt. By contrast, molting had no effect on the expression of Cm-mTOR, Cm-Akt, Cm-S6K, Cm-Rheb, and Cm-EF2. These data suggest that YO activation during premolt involves up regulation of mTOR signaling genes in G. lateralis, but is not required in C. maenas