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Register a new userThe Scalability, Efficiency and Complexity of Universities and Colleges: A New Lens for Assessing the Higher Educational System
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The growing need for affordable and accessible higher education is a major global challenge for the 21st century. Consequently, there is a need to develop a deeper understanding of the functionality and taxonomy of universities and colleges and, in particular, how their various characteristics change with size. Scaling has been a powerful tool for revealing systematic regularities in systems across a range of topics from physics and biology to cities, and for understanding the underlying principles of their organization and growth. Here, we apply this framework to institutions of higher learning in the United States and show that, like organisms, ecosystems and cities, they scale in a surprisingly systematic fashion following simple power law behavior. We analyze the entire spectrum encompassing 5,802 institutions ranging from large research universities to small professional schools, organized in seven commonly used sectors, which reveal distinct regimes of institutional scaling behavior. Metrics include variation in expenditures, revenues, graduation rates and estimated economic added value, expressed as functions of total enrollment, our fundamental measure of size. Our results quantify how each regime of institution leverages specific economies of scale to address distinct priorities. Taken together, the scaling of features within a sector and shifts in scaling across sectors implies that there are generic mechanisms and constraints shared by all sectors which lead to tradeoffs between their different societal functions and roles. We particularly highlight the strong complementarity between public and private research universities, and community and state colleges, four sectors that display superlinear returns to scale.
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The modern astrophysics is moving towards the enlarging of experiments and combining the channels for detecting the highest energy processes in the Universe. To obtain reliable data, the experiments should operate within several decades, which means that the data will be obtained and analyzed by several generations of physicists. Thus, for the stability of the experiments, it is necessary to properly maintain not only the data life cycle, but also the human aspects, for example, attracting, learning and continuity. To this end, an educational and outreach resource has been deployed in the framework of German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI).
As internet related challenges increase such as cyber-attacks, the need for safe practises among users to maintain computer systems health and online security have become imperative, and this is known as cyber-hygiene. Poor cyber-hygiene among internet users is a very critical issue undermining the general acceptance and adoption of internet technology. It has become a global issue and concern in this digital era when virtually all business transactions, learning, communication and many other activities are performed online. Virus attack, poor authentication technique, improper file backups and the use of different social engineering approaches by cyber-attackers to deceive internet users into divulging their confidential information with the intention to attack them have serious negative implications on the industries and organisations, including educational institutions. Moreover, risks associated with these ugly phenomena are likely to be more in developing countries such as Nigeria. Thus, authors of this paper undertook an online pilot study among students and employees of University of Nigeria, Nsukka and a total of 145 responses were received and used for the study. The survey seeks to find out the effect of age and level of education on the cyber hygiene knowledge and behaviour of the respondents, and in addition, the type of devices used and activities they engage in while on the internet. Our findings show wide adoption of internet in institution of higher learning, whereas, significant number of the internet users do not have good cyber hygiene knowledge and behaviour. Hence, our findings can instigate an organised training for students and employees of higher institutions in Nigeria.
The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.
We describe an ecosystem for teaching data science (DS) to engineers which blends theory, methods, and applications, developed at the Faculty of Physical and Mathematical Sciences, Universidad de Chile, over the last three years. This initiative has been motivated by the increasing demand for DS qualifications both from academic and professional environments. The ecosystem is distributed in a collaborative fashion across three departments in the above Faculty and includes postgraduate programmes, courses, professional diplomas, data repositories, laboratories, trainee programmes, and internships. By sharing our teaching principles and the innovative components of our approach to teaching DS, we hope our experience can be useful to those developing their own DS programmes and ecosystems. The open challenges and future plans for our ecosystem are also discussed at the end of the article.
In recent times, as a result of COVID-19 pandemic, higher institutions in Nigeria have been shutdown and the leadership of Academic Staff Union of University (ASUU) said that Nigerian universities cannot afford to mount Online learning platforms let alone conduct such learning system in Nigeria due to lack of infrastructure, capacity and skill sets in the face of COVID-19 pandemic. In the light of this, this research undertook an online survey using University of Nigeria, Nsukka (UNN) as a case study to know which type of online learning system ASUU leadership is talking about - Asynchronous or Synchronous? How did ASUU come about their facts? Did ASUU base their assertion on facts, if YES, what are the benchmarks? Therefore, this research project is focused on providing benchmarks to assess if a Nigerian University has what it takes to run a synchronous Online Learning. It includes Infrastructure needed (Hardware, Software, Network connectivity), Skill sets from staff (Computer literacy level). In a bid to do this, an online survey was administered to the staff of Centre for Distance and E-learning of UNN and out of the 40 members of that section of the University, we had 32 respondents. The survey seeks to find whether UNN has the requisite infrastructure and the skill sets to mount synchronous online learning. The available results of the study reveal that UNN is deficit in both the requisite infrastructure and Skills sets to mount synchronous online learning.
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