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This study will add to the body of knowledge by creating a conceptual framework around fundamental concepts related to examining the factors that influence the efficiency of online teaching. The framework is based on an assessment of the online teaching literature found using Emerald, Institute of Electrical and Electronics Engineers (IEEE) Explore, ProQuest, and Google Scholar searches. A conceptual matrix is used to combine the findings into a framework of online instruction and effectiveness. A questionnaire was utilized and 81 respondents participated in the survey. We found that Google Meet is the most used platform for online teaching. In terms of stress level, teachers between the ages of 36 and 50 are the most stressed. As per time spent on online teaching, most teachers spent 3-5 hours a day and we also found that the more hours spent on online teaching, the more benefit the teachers and students will get. The study's findings can help the school and Malaysia's Ministry of Education understand the factors that affect how well teachers can conduct online teaching.

Among the sources of legal considerations are judges’ previous decisions regarding similar cases that are archived in court decision documents. However, due to the increasing number of court decision documents, it is difficult to find relevant information, such as the category and the length of punishment for similar legal cases. This study presents predictions of first-level judicial decisions by utilizing a collection of Indonesian court decision documents. We propose using multi-level learning, namely, CNN+attention, using decision document sections as features to predict the category and the length of punishment in Indonesian courts. Our results demonstrate that the decision document sections that strongly affected the accuracy of the prediction model were prosecution history, facts, legal facts, and legal considerations. The prediction of the punishment category shows that the CNN+attention model achieved better accuracy than other deep learning models, such as CNN, LSTM, BiLSTM, LSTM+attention, and BiLSTM+attention, by up to 28.18%. The superiority of the CNN+attention model is also shown to predict the punishment length, with the best result being achieved using the ‘year’ time unit.

Water is essential for life. Frequent water disruption in Malaysia caused turbulence in daily lives and   livelihood of thousands Malaysian. The water operators in Malaysia are facing serious challenges to ensure consumers have continuous access to clean water and to ensure a sustainable water future. River pollution in Malaysia had been identified to be one of the causes of water crisis in Malaysia. Hence, a continuous monitoring system utilizing the concept of Internet of Things had been proposed in this paper. Agile model is used due to its simplicity. The water’s pH measurement, turbidity, temperature and flow can be measured and the reading will be sent to end-user. The sensors that detects the pH value, turbidity, temperature and flow measurement of a water sample will pass through the information to the Arduino, and the result will be shown on the mobile devices via an app called Blynk. This portable and comprehensive prototype is suitable to be used in Smart Cities where WiFi signals is available as the transmission medium.

Purpose The purpose of the study is to use web serer logs in analyzing the changes of user behavior in reading online news, in terms of desktop and mobile users. Advances in mobile technology and social media have paved the way for online news consumption to evolve. There is an absence of research into the changes of user behavior in terms of desktop versus mobile users, particularly by analyzing the server logs. Design/methodology/approach In this paper, the authors investigate the evolution of user behavior using logs from the Malaysian newspaper Berita Harian Online in April 2012 and April 2017. Web usage mining techniques were used for pre-processing the logs and identifying user sessions. A Markov model is used to analyze navigation flows, and association rule mining is used to analyze user behavior within sessions. Findings It was found that page accesses have increased tremendously, particularly from Android phones, and about half of the requests in 2017 are referred from Facebook. Navigation flow between the main page, articles and section pages has changed from 2012 to 2017; while most users started navigation with the main page in 2012, readers often started with an article in 2017. Based on association rules, National and Sports are the most frequent section pages in 2012 and 2017 for desktop and mobile. However, based on the lift and conviction, these two sections are not read together in the same session as frequently as might be expected. Other less popular items have higher probability of being read together in a session. Research limitations/implications The localized data set is from Berita Harian Online; although unique to this particular newspaper, the findings and the methodology for investigating user behavior can be applied to other online news. On another note, the data set could be extended to be more than a month. Although initially data for the year 2012 was collected, unfortunately only the data for April 2012 is complete. Other months have missing days. Therefore, to make an impartial comparison for the evolution of user behavior in five years, the Web server logs for April 2017 were used. Originality/value The user behavior in 2012 and 2017 was compared using association rules and Markov flow. Different from existing studies analyzing online newspaper Web server logs, this paper uniquely investigates changes in user behavior as a result of mobile phones becoming a mainstream technology for accessing the Web.