Global learning: Definition, assessment, and approaches

Global learning has become a fundamental aspect of international education. Yet, a clear understanding of global learning and how to develop it remain unclear. Using the dynamic systems approach, this paper analyzed the reasons, methods, and knowledge, skills, and attitudes(KSA) of global learning in higher education. Global learning is the higher education institutions’ critical response to globalization. It is the essential learning outcome of comprehensive internationalization of curriculum requiring students to develop KSA about the external world and their internal selves in their daily lives across local and global communities. With survey results from 142 undergraduate students in one U.S. university and a global learning rubric and publication, this paper demonstrated how global learning is interpreted and approached differently at various levels and further proposed pedagogical approaches to enhance global learning in higher education.

Integrating liberal education, business, and high-impact practices – the case of George Mason university’s school of business

Purpose In College Learning for the New Global Century, the National Leadership Council of Liberal Education and America’s Promise (LEAP) argue for a liberal education for all students because “(i)n an economy fueled by innovation, the capabilities developed through a liberal education have become America’s most valuable economic asset.” (LEAP, 2007). The Business for a Better World Center and the School of Business at George Mason University endorse this view and have applied the liberal education approach to the study of business. This paper aims to explore the current environment of business education, the role of liberal education and the school’s programs and their benefits. Design/methodology/approach This paper relies on a case-study approach. Findings In this paper, the authors explore how George Mason University’s School of Business brings a liberal education approach to business education and draws on a combination of high impact practices, such as first-year seminars, common intellectual experiences, learning communities, collaborative assignments, undergraduate research, community-based learning, internships, capstone courses and projects and diversity and global learning (Kuh, 2008). Mason’s experience demonstrates the feasibility and benefits of this integration. Originality/value This case study provides unique insight into how business schools can integrate a liberal education approach into business education with successful results. As such, the paper contributes to the growing body of research on the benefits of liberal arts and business education models as a means of addressing global goals and provides a valuable case study to understand better the necessity of integrative, interdisciplinary learning.

You Learn When It Hurts: Evidence in the Mutual Fund Industry

This paper aims to contribute to the lack of research on the learning process of mutual fund markets. The empirical design is focused on the ability of the Spanish equity mutual fund industry to learn from its important errors. The choice of this industry is justified by both its relevance in the European mutual fund markets and some specific characteristics, such as the concentration and the banking control of the industry, which may affect the learning process. Our main objectives are to identify important trading errors in mutual fund management by applying three independent filters based on the relative importance of each decision, and then testing the evolution of these errors both at the industry level and at the fund family level. We apply the dynamic model of generalized method of moments (GMM), and we find an overall significant decrease in the percentage of important trading errors over time, thereby providing evidence of the global learning process of the industry. In addition, we find that a large number of fund families drive this evidence. Finally, we obtain that the family size and its dependence on financial groups do not seem to play significant roles in explaining the learning process. Therefore, we conclude that fund managers have incentives to learn from their important trading errors, in order to avoid them in future decisions, due to their serious negative consequences on fund performance, regardless of the characteristics of the families to which they belong.

Brain-inspired global-local learning incorporated with neuromorphic computing

There are two principle approaches for learning in artificial intelligence: error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide complementary learning capabilities for versatile learning scenarios. At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs to fully exploit its advantages. Here, we present a neuromorphic global-local synergic learning model by introducing a brain-inspired meta-learning paradigm and a differentiable spiking model incorporating neuronal dynamics and synaptic plasticity. It can meta-learn local plasticity and receive top-down supervision information for multiscale learning. We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors. It achieves significantly higher performance than single-learning methods. We further implement the model in the Tianjic neuromorphic platform by exploiting algorithm-hardware co-designs and prove that the model can fully utilize neuromorphic many-core architecture to develop hybrid computation paradigm.

A Hybrid Machine Learning Model Based on Global and Local Learner Algorithms for Diabetes Mellitus Prediction

Health is a critical condition for living things, even before the technology exists. Nowadays the healthcare domain provides a lot of scope for research as it has extremely evolved. The most researched areas of health sectors include diabetes mellitus (DM), breast cancer, brain tumor, etc. DM is a severe chronic disease that affects human health and has a high rate throughout the world. Early prediction of DM is important to reduce its risk and even avoid it. In this study, we propose a DM prediction model based on global and local learner algorithms. The proposed global and local learners stacking (GLLS) model; combines the prediction algorithms from two largely different but complementary machine learning paradigms, specifically XGBoost and NB from global learning whereas kNN and SVM (with RBF kernel) from local learning and aggregates them by stacking ensemble technique using LR as meta-learner. The effectiveness of the GLLS model was proved by comparing several performance measures and the results of different contrast experiments. The evaluation results on UCI Pima Indian diabetes data-set (PIDD) indicates the model has achieved the better prediction performance of 99.5%, 99.5%, 99.5%, 99.1%, and 100% in terms of accuracy, AUC, F1 score, sensitivity, and specificity respectively, compared to other research results mentioned in the literature. Moreover, to better validate the GLLS model performance, three additional medical data sets; Messidor, WBC, ILPD, are considered and the model also achieved an accuracy of 82.1%, 98.6%, and 89.3% respectively. Experimental results proved the effectiveness and superiority of our proposed GLLS model.

From Crisis to Vision

Higher education now faces the critical role of partnerships, linkage, and strategic joint ventures to achieve shared goals in a transformed external environment. This environment is itself shaped not only by the pressures of neo-liberal competition, but by a set of crises emerging from the contradictions that is producing greater levels of inequity and social division. It is in this context that the chapter evaluates the importance of global learning as a critical tool to understand, engage with, and potentially transform a globalized socio-economic environment and engage proactively with existing multiple crises. Academics and educators are now intimately connected to the need to articulate and demonstrate globalized learning models and reflective practice founded on explicitly international perspectives. Given the urgency, internationalization alone is insufficient to achieve transformation. A re-appropriation of purpose and values is also required within an emancipatory and social justice model that asserts human needs, not corporate efficiency.

Students’ Perception of General Education Courses at the Faculty of Engineering and Technology, University of Botswana

To adequately prepare graduates for the ever-changing and complex work environment, students should be equipped with technical and professional skills. This can be achieved by a curriculum that incorporates General Education Courses (GEC) that teach diverse essential skills that every graduate must possess e.g. academic and professional competencies, ethics, global learning, and active citizenship. Such courses help will produce a well-rounded learning experience and well-versed graduate. A descriptive cross-sectional survey was conducted with undergraduate engineering and industrial design students at the University of Botswana. The study assessed the students’ current perceptions of the GEC. The results show that the skills outlined in the Learning and Teaching Policy of the University of Botswana are poorly attained. Furthermore, the skills outlined in the policy are not aligned with the skills that will be needed by graduates in the 4th Industrial Revolution. The GEC curriculum at the University of Botswana needs to be reviewed.

RALR: Random Amplify Learning Rates for Training Neural Networks

It has been shown that the learning rate is one of the most critical hyper-parameters for the overall performance of deep neural networks. In this paper, we propose a new method for setting the global learning rate, named random amplify learning rates (RALR), to improve the performance of any optimizer in training deep neural networks. Instead of monotonically decreasing the learning rate, we expect to escape saddle points or local minima by amplifying the learning rate between reasonable boundary values based on a given probability. Training with RALR rather than conventionally decreasing the learning rate achieves further improvement on networks’ performance without extra consumption. Remarkably, the RALR is complementary with state-of-the-art data augmentation and regularization methods. Besides, we empirically study its performance on image classification tasks, fine-grained classification tasks, object detection tasks, and machine translation tasks. Experiments demonstrate that RALR can bring a notable improvement while preventing overfitting when training deep neural networks. For example, the classification accuracy of ResNet-110 trained on the CIFAR-100 dataset using RALR achieves a 1.34% gain compared with ResNet-110 trained traditionally.

COVID-19 impacts on virtual exchange around the world

The COVID-19 pandemic has simultaneously created both opportunities and challenges for the emerging field of virtual exchange: On one hand, institutional administrators and funding organisations saw virtual exchange as the solution to global learning needs while physical travel was restricted and traditional mobility programmes were suspended. On the other hand, instructors felt overwhelmed by transitioning all of their teaching online, and without physical access to their educational institutions, many students and instructors lacked reliable internet connections or safe places to engage in learning, not to mention the financial burdens of the pandemic. This moderated panel discussion which took place during the IVEC 2020 conference invited diverse perspectives to explore the impacts of the pandemic on virtual exchange in various contexts around the world. Central to the discussion were issues of equity, inclusion and justice: Is virtual exchange truly a more accessible and equitable form of global learning, as it is often promoted to be? In this video contribution, Eva Haug moderates the conversation between Maha Bali, Paulo Goes, and Anita Patankar around the following questions. * How is virtual exchange a solution to global learning during COVID-19? * What have been the two to three most relevant impacts of the pandemic on virtual exchange activity at your institution, in your country, or region of the world? * How can we as a field of practitioners maintain and sustain the current momentum and interest in VE in a post-COVID-19 world? * Can intercultural exchange be apolitical? * If an institution is in a position of power or privilege, how can they create space in virtual exchange for institutions that are less represented? The video recording is accessible on: https://vimeo.com/459415071 (CC BY-NC-NC)