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The research aimed to explore the issues in the implementation of online education practice in elementary school, to study teachers' coping strategy to the online education issues and to evaluate teachers' problem-solving skill in online learning practice during the Covid-19 pandemic.

An exploratory research focused on identifying the obstacles in teaching practice faced by elementary school teachers as well as their coping strategy with eight convenience sampled schools.

Online education practice faced unpreparedness and competency issues. Unpreparedness was found in terms of social, technical and cultural factors, while competency issue was related to online education competency and digital competency. Teachers’ struggle to cope with the issue in online education practice was focused on the performing conventional education in the online manner, suggesting teachers' lack of competency in encouraging learning success. Teachers neglected the development of students' readiness and competencies to engage in online learning. Moreover, teachers’ struggle had the least impact on the development of their online teaching competency and digital competency that are required for carrying out online teaching. In general, teachers' problem-solving skill was below the expected level. These findings suggested that improvement of teachers' competencies is important in order to cope with the issues such as in online education practice during Covid-19 pandemic and to face future challenges in education.

This study evaluated the gap between actual action and expected action of elementary school teachers in coping with the issues regarding online education practice.

Hate speech is an expression of intense hatred. Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors. Hate speech detection with social media data has witnessed special research attention in recent studies, hence, the need to design a generic metadata architecture and efficient feature extraction technique to enhance hate speech detection.

This study proposes a hybrid embeddings enhanced with a topic inference method and an improved cuckoo search neural network for hate speech detection in Twitter data. The proposed method uses a hybrid embeddings technique that includes Term Frequency-Inverse Document Frequency (TF-IDF) for word-level feature extraction and Long Short Term Memory (LSTM) which is a variant of recurrent neural networks architecture for sentence-level feature extraction. The extracted features from the hybrid embeddings then serve as input into the improved cuckoo search neural network for the prediction of a tweet as hate speech, offensive language or neither.

The proposed method showed better results when tested on the collected Twitter datasets compared to other related methods. In order to validate the performances of the proposed method, t-test and post hoc multiple comparisons were used to compare the significance and means of the proposed method with other related methods for hate speech detection. Furthermore, Paired Sample t-Test was also conducted to validate the performances of the proposed method with other related methods.

Finally, the evaluation results showed that the proposed method outperforms other related methods with mean F1-score of 91.3.

The main novelty of this study is the use of an automatic topic spotting measure based on naïve Bayes model to improve features representation.