مرجان کائدی

Online learning programs have gained significant popularity in recent years. However, despite their widespread adoption, completion and success rates for online courses are notably lower than those for traditional in-person education. If students' final academic performance could be predicted early by analyzing their behavior within the virtual learning environment, timely alerts could be issued, and targeted interventions could be recommended to prevent underperformance and course abandonment. Previous studies have predicted academic performance using various features, such as demographic data, academic history, in-term exam results, and assignment assessments. However, many online learning platforms do not provide access to such data, rendering these methods ineffective. This study focuses on the early prediction of students' academic performance by extracting novel behavioral features based on their interactions with the online learning platform. To develop robust predictive models, we utilize an integrated approach combining multiple feature selection methods to extract the most informative interaction patterns, followed by application of advanced machine learning algorithms including ensemble learning techniques and artificial neural networks (ANNs). The evaluation results demonstrate that our proposed approach can predict students' final academic performance with an accuracy of 90.62%, using only data collected during the first third of the online course.

Nowadays, the tourism industry has become one of the most important sectors in the world economy. Due to the perishability of this industry, accurate forecasting of the demand is very important for tourism planning and resource allocation. Studies show that due to the diversity and complexity of the factors affecting tourism demand, the combination of different approaches may increase the forecasting accuracy. The aim of this paper is to forecast the tourism demand of Alisadr cave. For this purpose, a method based on artificial neural networks is presented, in which the results of linear and non-linear methods and short-term and long-term forecasts are combined. This method is applied to a dataset of Alisadr cave tourists. The evaluation results show that in most cases, the proposed combined method can predict the tourism demand with higher accuracy than the monthly and seasonal methods based on neural networks and random forest models. The predictive models obtained from this study can enhance customer service and improve the interaction between users and tourist ticketing web applications and online reservation programs.