Technology integration in higher education and student privacy beyond learning environments—A comparison of the UK and US perspective

Technology integration in higher education (HE) has brought immense innovation. While research is investigating the benefits of leveraging, through learning analytics, the data created by the greater presence of technology in HE, it is also analysing the privacy implications of vast universes of data now at the fingertips of HE administrators. This paper argues that student privacy challenges linked to technology integration occur not only within but also beyond learning environments, namely at the enterprise level. By analysing the UK and US legal frameworks surrounding how HE institutions respond to parents demanding disclosure of their adult children's personal data in the event of mental health crises, this paper offers an example of real and complex privacy issues, often overlooked by interdisciplinary inquiry, that exist in the ‘interstitial space’ between HE technology and privacy law. The purpose of conducting a comparative analysis was to demonstrate that countries with different privacy regimes are similarly ill-equipped to address certain student privacy issues at the HE enterprise level, leaving HEIs exposed to potential litigation/regulatory risks. The contribution of this work is to invite greater interdisciplinary awareness of, and inquiry into, student privacy beyond learning environments.     

A ‘power law’ based method to reduce size-related bias in indicators of knowledge performance: An application to university research assessment

The knowledge production provided by universities is essential to sustaining a country’s long-term economic growth and international competitiveness. Many nations are thus driving to create sustainable and effective funding environments. The evaluation of university knowledge, productivity and research quality becomes critical, with ever increasing share of public funding allocated on the basis of research assessment exercises. Nevertheless, the existing methods to assess the universities’ knowledge production are often affected by limits and biases, extensively discussed in the scientific literature.In this paper we study how to reduce the effect of size-related bias due to university size on the indicators of knowledge performance used in evaluation exercises. We propose an innovative utilization of the scale-free property of the power laws as a scaling relationship, to normalize research productivity indicators, and provide results independent by the university size. Our method has evident policy implications and gives a contribution for the future design of assessment exercises.We apply our findings in a recent Italian research assessment exercise.     

Analyzing learners’ engagement and behavior in MOOCs on programming with the Codeboard IDE

Educational technology research and development - Massive Open Online Courses (MOOCs) can be enhanced with the so-called learning-by-doing, designing the courses in a way that the learners are...     

Are all citations worth the same? Valuing citations by the value of the citing items

Bibliometricians have long recurred to citation counts to measure the impact of publications on the advancement of science. However, since the earliest days of the field, some scholars have questioned whether all citations should be worth the same, and have gone on to weight them by a variety of factors. However sophisticated the operationalization of the measures, the methodologies used in weighting citations still present limits in their underlying assumptions. This work takes an alternative approach to resolving the underlying problem: the proposal is to value citations by the impact of the citing articles, regardless of the length of their reference list. As well as conceptualizing a new indicator of impact, the work illustrates its application to the 2004–2012 Italian scientific production indexed in the WoS. The proposed impact indicator is highly correlated to the traditional citation count, however the shifts observed between the two measures are frequent and the number of outliers not negligible. Moreover, the new indicator shows greater “sensitivity” when used to identify the highly-cited papers.