22 May 2018

Research Picks 22 May 2018

Looking for dropouts
Predicting poor performance in students is important for educational establishments looking to improve their “outcomes” for students as well as reduce the dropout rates. The bottom line amounts to a less well-educated public and increased costs for the establishment. Researchers in Greece have now demonstrated an algorithm based on modern machine-learning techniques that can spot those students that are less likely to perform well in examinations and in assessments and so draw those candidates to the attention of educators for extra tuition and guidance at critical points in their education. Proof of principle has been shown with a distance learning, web-based course at an “open university”. However, the approach could equally be applied to any kind of educational system with appropriate modifications.
Kostopoulos, G., Kotsiantis, S., Pierrakeas, C., Koutsonikos, G. and Gravvanis, G.A. (2018) ‘Forecasting students’ success in an open university’, Int. J. Learning Technology, Vol. 13, No. 1, pp.26–43

Pay what you want
Bargaining has been an essential part of trade since the first humans swapped commodities or services back in prehistoric times whether it was food being traded for a stone axe, baubles for an animal skin, or sexual favours for shelter and protection. Today, digital commodities are commonplace, ephemeral, virtual entities that suggest a different kind of value and so a different form of bargaining. Indeed, where such commodities are often freely available, a “pay what you want” approach to marketing and bargaining has emerged for commodities such as music downloads and the like. Researchers in Germany have now demonstrated how the PWYW approach to bargaining plays out with real commodities, in their case study “sweet snacks”. They have found that on average PWYW buyers spend 15% more than the suggested price, while 56% of them pay the marked price. This flies in the face of conventional wisdom that would suggest that a buyer would pay as little as possible, perhaps even nothing at all, if that is an option, to obtain the goods they desire. It seems that PWYW buyers actually see treating the seller fairly as more important than obtaining a product for free or as low a price as possible.
Gerpott, T.J. (2018) ‘Explaining payment amounts among self-selected pay-what-you-want-buyers: results from a field experiment in Germany’, Global Business and Economics Review, Vol. 20, No. 3, pp.263–285.

The European Union (EU) has led the way in privacy and data protection since the 1990s through its Data Protection Directive 95/46/EC. Indeed, it has revolutionised data protection safety across its member states. Now, with the introduction of directive GDPR 2016/679, the General Data Protection Regulation it strengthens the existing legal framework and adds new and important guarantees regarding data protection safety. Moreover, GDPR does not apply only to member states but to anyone who handles and uses the data of any EU citizen wherever the citizen or handler is in the world. The directive was approved by the European Parliament in April 2016 and will be enforced on 25 May 2018. “The GDPR aims in the empowerment of the individual in an era where [their] data are more and more endangered by the augmented use of technology,” explains Christina Akrivopoulou of the Greek Refugee Appeals Authority, in Athens, Greece. “The practical enforcement of the GDPR will surely re-open the discussion on its strengths and weaknesses which remain to be seen.”
The editorial is free to download as a PDF from the journal IJHRC:
Akrivopoulou, C.M. (2018) ‘Editorial: A new era for privacy and data protection in the EU: general data protection regulation‘, Int. J. Human Rights and Constitutional Studies, Vol. 6, No. 1.

SMURF cleans up RFID
RFID (radio frequency identification) technology is widely used in commerce and increasingly in the environment we refer to as the Internet of Things (IoT) wherein all kinds of electronic devices, computers, industrial and domestic machines, and vehicles have internet connectivity. RFID allows objects to be tracked. In order to streamline the vast quantities of data emerging from the IoT and in particular RFID tracking, there is a need to clean the data. The “traditional” Statistical Smoothing for Unreliable RFID (SMURF) data algorithm has been limited to constant speed data flow during the process of data cleaning. Now, a team from China has demonstrated how SMURF might be used to clean dynamic tags and remove data redundancy. The team shows that their approach allows a broader definition of RFID data to be cleaned as well as improving accuracy.
Xu, H., Ding, J., Li, P., Sgandurra, D. and Wang, R. (2018) ‘An improved SMURF scheme for cleaning RFID data’, Int. J. Grid and Utility Computing, Vol. 9, No. 2, pp.170–178.

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