No abstract

No abstract

No abstract

This paper reports on one aspect of a nationally funded research project on contract cheating in Australian higher education. The project explored students' and educators' experiences of contract cheating, and the contextual factors that may influence it. This paper reports the key findings from non-university higher education providers (NUHEPs). It compares survey responses from 961 students and 91 educators at four NUHEPs with previously reported findings from eight universities (14,086 students and 1,147 staff). NUHEP and university students report engaging in contract cheating in similar ways. However, while NUHEP educators spend more time teaching academic literacies and discussing contract cheating, NUHEP students are 12 times more likely than university students to report use of a professional academic writing service. Both NUHEP and university educators require systematic professional development regarding the relationship between the teaching and learning environment and students' contract cheating behaviour. NUHEPs need to be cognisant of students' vulnerability to commercial contract cheating services, and ensure they have access to timely academic and social support.

Time-series forecasting is a significant discipline of data modeling where past observations of the same variable are analyzed to predict the future values of the time series. Its prominence lies in different use cases where it is required, including economic, weather, stock price, business development, and other use cases. In this work, a review was conducted on the methods of analyzing time series starting from the traditional linear modeling techniques until the automated machine learning (AutoML) frameworks, including deep learning models. The objective of this review article is to support identifying the time-series forecasting challenge and the different techniques to meet the challenge. This work can be additionally an assist and a reference for researchers and industries demanding to use AutoML to solve the problem of forecasting. It identifies the gaps of the previous works and techniques used to solve the problem of forecasting time series.

No abstract

No abstract

No abstract