Despite the increase in the adoption of online educational platforms, student retention is still a challenging task with a number of students having low performance margins during these courses. This chapter intends to predict student performance based on their learning behavior on the basis of their logging data history, using the publicly available Open University Learning Analytics Dataset. To model this problem, logistic regression (LR) is used as a baseline technique. Additionally, random forest (RF), multiple layered perceptron with multiple activation functions, and Gaussian Naïve Bayes are also deployed. The results demonstrate that RF outperforms the baseline LR and other models with 89% accuracy, 89% precision, 88% recall, and 88% F1-score. Finally, the authors conclude that using the above-mentioned models, students “at-risk” can be identified which can be managed by an alert mechanism to improve student success rate by making timely interventions.

Student retention is a widely recognized challenge in the educational community to assist the institutes in the formation of appropriate and effective pedagogical interventions. This study intends to predict the students at-risk of low performances during an on-going course, those at-risk of graduating late than the tentative timeline and predicting the capacity of students in a campus. The data constitutes of demographics, learning, academic and educational related attributes which are suitable to deploy various machine learning algorithms for the prediction of at-risk students. For class balancing, Synthetic Minority Over Sampling Technique, is also applied to eliminate the imbalance in the academic award-gap performances and late/timely graduates. Results reveal the effectiveness of the deployed techniques with Long short-term Memory (LSTM) outperforming other models for early prediction of at-risk students. The main contribution of this work is a machine learning approach capable of enhancing the academic decision making related to student performance.

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Ethereum, the second-largest cryptocurrency after Bitcoin, has attracted wide attention in the last few years and accumulated significant transaction records. However, the underlying Ethereum network structure is still relatively unexplored. Also, very few attempts have been made to perform link predictability on the Ethereum transactions network. This paper presents a Detailed Analysis of the Ethereum Network on Transaction Behavior, Community Structure, and Link Prediction (DANET) framework to investigate various valuable aspects of the Ethereum network. Specifically, we explore the change in wealth distribution and accumulation on Ethereum Featured Transactional Network (EFTN) and further study its community structure. We further hunt for a suitable link predictability model on EFTN by employing state-of-the-art Variational Graph Auto-Encoders. The link prediction experimental results demonstrate the superiority of outstanding prediction accuracy on Ethereum networks. Moreover, the statistic usages of the Ethereum network are visualized and summarized through the experiments allowing us to formulate conjectures on the current use of this technology and future development.

This chapter presents a novel scientific research landscape of the Gulf Cooperation Council (GCC) in order to access the research productivity, scholarly impact, and international collaborations across all GCC countries over the time period of 2008–2018, using the Scopus database. While we observe a significant increase in investing the resources in GCC world in order to build research infrastructure to make shift from only oil producing countries to knowledge-based economies, not enough efforts have been done to measure the outcomes of these investments. The chapter explores different thematic areas that have evolved over a decade related to TDI, top scientists, and top institutions in GCC. Further, the study investigates international collaboration networks within or outside GCC to better understand the venues of knowledge exchange with GCC countries. It is expected that the findings of this case study would provide an insight to the research landscape of the GCC and useful information to the scientific community as well as to the technology and innovation policymakers.

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This article aims to exploit social exchanges on scientific literature, specifically tweets, to analyse social media users’ sentiments towards publications within a research field. First, we employ the SentiStrength tool, extended with newly created lexicon terms, to classify the sentiments of 6,482,260 tweets associated with 1,083,535 publications provided by Altmetric.com. Then, we propose harmonic means-based statistical measures to generate a specialised lexicon, using positive and negative sentiment scores and frequency metrics. Next, we adopt a novel article-level summarisation approach to domain-level sentiment analysis to gauge the opinion of social media users on Twitter about the scientific literature. Last, we propose and employ an aspect-based analytical approach to mine users’ expressions relating to various aspects of the article, such as tweets on its title, abstract, methodology, conclusion or results section. We show that research communities exhibit dissimilar sentiments towards their respective fields. The analysis of the field-wise distribution of article aspects shows that in Medicine, Economics, Business and Decision Sciences, tweet aspects are focused on the results section. In contrast, in Physics and Astronomy, Materials Sciences and Computer Science, these aspects are focused on the methodology section. Overall, the study helps us to understand the sentiments of online social exchanges of the scientific community on scientific literature. Specifically, such a fine-grained analysis may help research communities in improving their social media exchanges about the scientific articles to disseminate their scientific findings effectively and to further increase their societal impact.