Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student's Tweets during COVID-19

As the COVID-19 pandemic swept the globe, social media plat-forms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the pro-posed aspect2class annotation algorithm, we labeled bulk unlabeled tweets according to their contained aspect terms. However, we also recognized the challenges of reducing data's high dimensionality and sparsity to improve performance and annotation on unlabeled datasets. To address this issue, we proposed the Volatile Stopwords Filtering (VSF) technique to reduce sparsity and enhance classifier performance. The resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21% when using the random forest as a meta-classifier. Through testing on three benchmark datasets, we found that the SABAC ensemble framework performed exceptionally well. Our findings showed that international students during the pandemic faced various issues, including stress, uncertainty, health concerns, financial stress, and difficulties with online classes and returning to school. By analyzing and summarizing these annotated tweets, decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.

Engineering Education in Intelligent Manufacturing

The age of digitization is changing the way universities need to educate their engineering graduates. Thus, universities must train their graduates with the skills required in the era of digitization. In this regard universities face significant pressure to overhaul their established sometimes traditional curriculums and expand them by focusing on methodologies and technologies required as skills in the 21st century. In this regard technology enhanced learning methods are essential while flexible to provide engineering students with the respective skills in complex system design in the manufacturing domain. This technology enhanced learning approach also helps to overcome education lags caused by the COVID 19 pandemic. The digital transformative impact on intelligent manufacturing also requires dealing with risk management with regard to vulnerability through cyber threat attacks. Thus, this paper describes an educational approach which refers to the essential skills required in engineering education in intelligent manufacturing- © 2020 IEEE.