Plant diversity and niche complementarity had progressively stronger effects on ecosystem functioning during a 7-year experiment, with 16-species plots attaining 2.7 times greater biomass than monocultures. Diversity effects were neither transients nor explained solely by a few productive or unviable species. Rather, many higher-diversity plots outperformed the best monoculture. These results help resolve debate over biodiversity and ecosystem functioning, show effects at higher than expected diversity levels, and demonstrate, for these ecosystems, that even the best-chosen monocultures cannot achieve greater productivity or carbon stores than higher-diversity sites.

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Diversity and productivity in a long-term grassland experiment

In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1 , 2 ). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3 , 4 ), what lessons can we draw from this error?

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The Parable of Google Flu: Traps in Big Data Analysis

Recommender systems have the effect of guiding users in a personal- ized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recom- mender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of content- based recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describ- ing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.

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Content-based Recommender Systems: State of the Art and Trends

Social Networking Sites (SNSs) such as Facebook are one of the latest examples of communications technologies that have been widely-adopted by students and, consequently, have the potential to become a valuable resource to support their educational communications and collaborations with faculty. However, faculty members have a track record of prohibiting classroom uses of technologies that are frequently used by students. To determine how likely higher education faculty are to use Facebook for either personal or educational purposes, higher education faculty (n = 62) and students (n = 120) at a mid-sized southern university were surveyed on their use of Facebook and email technologies. A comparison of faculty and student responses indicate that students are much more likely than faculty to use Facebook and are significantly more open to the possibility of using Facebook and similar technologies to support classroom work. Faculty members are more likely to use more “traditional” technologies such as email.

Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites

Governments are becoming increasingly aware of the important contribution that high performance, world-class universities make to global competitiveness and economic growth. There is growing recognition, in both industrial and developing countries, of the need to establish one or more world-class universities that can compete effectively with the best of the best around the world. Contextualising the drive for world-class higher education institutions and the power of international and domestic university rankings, this book outlines possible strategies and pathways for establishing globally competitive universities and explores the challenges, costs, and risks involved. Its findings will be of particular interest to policy makers, university leaders, researchers, and development practitioners.

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Gail A. Herndon

Papers

The chronicle of higher education