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Educating the smart city: Schooling smart citizens through computational urbanism


From the abstract:

Coupled with the ‘smart city’, the idea of the ‘smart school’ is emerging in imaginings of the future of education. Various commercial, governmental and civil society organizations now envisage education as a highly coded, software-mediated and data-driven social institution. Such spaces are to be governed through computational processes written in computer code and tracked through big data. In an original analysis of developments from commercial, governmental and civil society sectors, the article examines two interrelated dimensions of an emerging smart schools imaginary: (1) the constant flows of digital data that smart schools depend on and the mobilization of analytics that enable student data to be used to anticipate and shape their behaviours; and (2) the ways that young people are educated to become ‘computational operatives’ who must ‘learn to code’ in order to become ‘smart citizens’ in the governance of the smart city. These developments constitute an emerging educational space fabricated from intersecting standards, technologies, discourses and social actors, all infused with the aspirations of technical experts to govern the city at a distance through both monitoring young people as ‘data objects’ and schooling them as active ‘computational citizens’ with the responsibility to compute the future of the city.

Users or Students? Privacy in University MOOCS


From the abstract:

Two terms, student privacy and Massive Open Online Courses, have received a significant amount of attention recently. Both represent interesting sites of change in entrenched structures, one educational and one legal. MOOCs represent something college courses have never been able to provide: universal access. Universities not wanting to miss the MOOC wave have started to build MOOC courses and integrate them into the university system in various ways. However, the design and scale of university MOOCs create tension for privacy laws intended to regulate information practices exercised by educational institutions. Are MOOCs part of the educational institutions these laws and policies aim to regulate? Are MOOC users students whose data are protected by aforementioned laws and policies? Many university researchers and faculty members are asked to participate as designers and instructors in MOOCs but may not know how to approach the issues proposed. While recent scholarship has addressed the disruptive nature of MOOCs, student privacy generally, and data privacy in the K-12 system, we provide an in-depth description and analysis of the MOOC phenomenon and the privacy laws and policies that guide and regulate educational institutions today. We offer privacy case studies of three major MOOC providers active in the market today to reveal inconsistencies among MOOC platform and the level and type of legal uncertainty surrounding them. Finally, we provide a list of organizational questions to pose internally to navigate the uncertainty presented to university MOOC teams.

The Ethics of Big Data in Higher Education


Data mining and predictive analytics—collectively referred to as “big data”—are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge con- siders the extent to which data mining’s outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.

Mining Educational Data to Support Students’ Major Selection


This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well.

Educational Data Sciences: Framing Emergent Practices for Analytics of Learning, Organizations, and Systems


In this paper, we develop a conceptual framework for organizing emerging analytic activities involving educational data that can fall under broad and often loosely defined categories, including Academic/Institutional Analytics, Learning Analytics/Educational Data Mining, Learner Analytics/Personalization, and Systemic Instructional Improvement. While our approach is substantially informed by both higher education and K-12 settings, this framework is developed to apply across all educational contexts where digital data are used to inform learners and the management of learning. Although we can identify movements that are relatively independent of each other today, we believe they will in all cases expand from their current margins to encompass larger domains and increasingly overlap. The growth in these analytic activities leads to the need to find ways to synthesize understandings, find common language, and develop frames of reference to help these movements develop into a field.

Contemporary Privacy Theory Contributions to Learning Analytics


With the continued adoption of learning analytics in higher education institutions, vast volumes of data are generated and “big data” related issues, including privacy, emerge. Privacy is an ill-defined concept and subject to various interpretations and perspectives, including those of philosophers, lawyers, and information systems specialists. This paper provides an overview of privacy and considers the potential contribution contemporary privacy theories can make to learning analytics. Conclusions reflect on the suitability of these theories towards the advancement of learning analytics and future research considers the importance of hearing the student voice in this space.

Information Fiduciaries in the Digital Age


A fiduciary duty would limit the rights the company would otherwise enjoy to collect, collate, use and sell personal information about the end user. In particular, there would be no general First Amendment right to disclose sensitive data or use sensitive data to the disadvantage of the end user. (To be sure, such a right might exist in certain circumstances depending on how strong the fiduciary duty was and whether the duty allows waiver or consent to disclose in certain circumstances.) The online service provider would also have to consider whether its information practices created a conflict of interest and act accordingly. Moreover, the online service provider’s duties of loyalty and care might require it to disclose how it was using the customer’s personal information.

An Exercise in Institutional Reflection: The Learning Analytics Readiness Instrument (LARI)


While the landscape of learning analytics is relatively well defined, the extent to which institutions are ready to embark on an analytics implementation is less known. Further, while work has been done on measuring the maturity of an institution’s implementation, this work fails to investigate how an institution that has not implemented analytics to date might become mature over time. To that end, the authors developed and piloted a survey, the Learning Analytics Readiness Instrument (LARI), in an attempt to help institutions successfully prepare themselves for a successfully analytics implementation. The LARI is comprised of 90 items encompassing five factors related to a learning analytics implementation: (1) Ability, (2) Data, (3) Culture and Process, (4) Governance and Infrastructure, and, (5) Overall Readiness Perception. Each of the five factors has a high internal consistency, as does the overall tool. This paper discusses the need for a survey such as the LARI, the tool’s psychometric properties, the authors’ broad interpretations of the findings, and next steps for the LARI and the research in this field.

A Data Mining Tool for Prediction of Suicides among Students


The inability to handle pressure and meet parent’s expectations is giving rise to suicides among students in high schools and colleges. This is leading to the loss of talent which can be useful in nation building. There is increasing need for providing valid means of determining which students are at risk for suicide. The ability to predict suicidal behaviour is still relatively poor, but identification of personality traits and behaviours in individuals can be helpful to minimize the number of students committing suicides. In this paper we study on implementation of a counselling system to predict suicidal tendencies and depression among the students. We analyze the different warning signs (observable and non observable) such as interpersonal communications, interpersonal relations, classroom behaviour etc. and apply the data mining algorithms to generate the results. We collect data about different students and then design a gradation system on the basis of collected results. Using this gradation system we determine the students at a high risk.