The advent of the COVID-19 pandemic has precipitated a paradigm shift in education, marked by an increasing reliance on technology and virtual platforms. This study delves into the post-pandemic landscape of Islamic higher education at the State Islamic Institute of Palangka Raya, Central Kalimantan, Indonesia, focusing on students’ readiness, attitudes, and interests toward sustained engagement with e-learning. A cohort of 300 students across all semesters of Islamic Education partook in the investigation. Utilising Structural Equation Modelling, the study gauged students’ preparedness, perceptions, and inclinations toward online learning. Results indicate a general readiness among students for online learning, with a pivotal role attributed to technological devices and internet connectivity. Positive attitudes toward online learning were prevalent, with flexibility and accessibility emerging as significant advantages. Moreover, students showed keen interest in online learning, valuing its technological advancements, affordability, and intellectually challenging nature. These findings highlight the digital transformation of traditional teaching methods among Islamic higher education students, who are typically known for their emphasis on direct interaction in teaching and learning. Their receptivity to innovative learning modalities and adaptability to the digital era’s difficulties highlight the need for educational institutions to leverage this enthusiasm. Comprehensive online learning platforms, robust technological support, and a conducive learning environment are advocated to empower Islamic higher education students in navigating the digital landscape and perpetuating their pursuit of knowledge and enlightenment.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

Higher education (HE) consists of both conventional and non-conventional methods of learning. Open and Distance Learning (ODL) is a non-conventional system where teachers (often referred to as facilitators) are physically not present. The conduct of practical in engineering and science education using ODL remains a challenge due to inadequate technology and the dispersion of the students, which results in a graduate skills gap in ODL programs. There is a possibility of using a cloud computing set-up, as well as platforms for the creation of simulated virtual practical settings (virtual laboratories-VLs), which could be accessible by ODL engineering and science and education-based students notwithstanding their locations. This paper adds to existing knowledge on VLs and discusses these inadequacies in engineering and science education with emphasis on the enhancement of online and collaborative learning, as well as the possible laboratory (lab) requirements. In addition, the paper highlights contemporary trends and some issues in VLs and remote labs.

Recovery and resilience plan (RRP) approved by the European Commission fosters the development of lifelong learning programs to upgrade employees’ skills and knowledge for digital and green transitions. Within higher education, the field of information and communication technology (ICT) is also a priority area, so we compared the demographic variables of students enrolled in formal first-cycle higher education programs in ICT with those enrolled in lifelong ICT programs within the framework of the Advanced Computer Skills project funded by the RRP in Slovenia. The results show that formal first-cycle higher education in the field of ICT remains strongly male-dominated, whereas, among participants in lifelong learning, the percentage of females stands out. Bachelor programs in ICT are primarily enrolled by young people aged up to 24 years, while shorter university-based lifelong learning programs attract mostly older participants with higher completed formal education and from a broader range of prior educational backgrounds. Finally, when all three variables (gender, age and level of prior formal education) are considered, participants in lifelong learning are much more similar to part-time students than full-time bachelor ICT students, although the percentage of men in formal education is still predominant even in part-time studies. The research findings highlight the need for further efforts to offer lifelong learning in ICT to enable individuals to improve their employment prospects, progress in the workplace or even change their field of work.

Gamification is an active methodology of great value that, in a quality educational environment, provides students with the necessary motivation to participate in their teaching-learning process. An emerging active methodology, which is based on the use of information and communication technologies (ICT) and requires an educational space that guarantees greater flexibility in the pedagogical dynamics in favor of academic achievement. This increase in interest in active methodologies, and specifically in gamification, has raised doubts about whether current educational spaces are prepared to host a renewal in methodology or if, on the contrary, they could undermine the attitude of change. For this reason, this research seeks to analyze whether current educational spaces are facilitating elements for the incorporation of gamification in the classroom. The methodological cut of the research is quantitative, specifically in two phases. On the one hand, a descriptive analysis of the results is carried out, obtaining information on the trend of each item. On the other hand, an inferential analysis is carried out around different variables to verify their possible influence on the evaluations of the participants. The results obtained, in the sample made up of 210 teachers distributed in the different centers and who carry out their educational activity from 3rd to 6th grade of primary school, indicate that teachers believe it is relevant to take into account the educational space when incorporating active methodologies in class.