As an emerging field of research, learning analytics (LA) offers practitioners and researchers information about educational data that is helpful for supporting decisions in management of teaching and learning. While often combined with educational data mining (EDM), crucial distinctions exist for LA that mandate a separate review. This study aims to conduct a systematic meta-review of LA for mining key information that could assist in describing new and helpful directions to this field of inquiry. Within 901 LA articles analyzed, eight reviews were identified and synthesised to identify and determine consistencies and gaps. Results show that LA is at the stage of early majority and has attracted great research efforts from other fields. The majority of LA publications were focused on proposing LA concepts or frameworks and conducting proof-of-concept analysis rather than conducting actual data analysis. Collecting small datasets for LA research is predominant, especially in K-12 field. Finally, four major LA research topics, including prediction of performance, decision support for teachers and learners, detection of behavioural patterns & learner modelling and dropout prediction, were identified and discussed deeply. The future research of LA is also outlined for purpose of better understanding and optimising learning as well as learning contexts.

This study proposes two innovative approaches, the 1-channel learning image recognition (1-CLIR) and the 3-channel learning image recognition (3-CLIR) to convert student's course involvements into images for early warning predictive analysis. Multiple experiments with 5,235 students and 576 absolute/1728 relative input variables were conducted to verify their effectiveness. The results indicate both methods can significantly capture more at-risk students (the highest average recall rate is equal to 77.26%) than the following machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN) in the middle of the semester. In addition, the innovative approaches allow minor subtypes of at-risk student identification and provide visual insights for personalized interventions. Implications and future directions are also discussed in the article.

The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward.

The viscosity of slag is a key factor affecting metallurgical efficiency and recycling, such as metal-slag reaction and separation, as well as slag wool processing. In order to comprehensively clarify the variation of the slag viscosity, various data mining methods have been employed to predict the viscosity of the slag. In this study, a more advanced dual-stage predictive modeling approach is proposed in order to accurately analyze and predict the viscosity of slag. Compared with the traditional single data mining approach, the proposed method performs better with a higher recall rate and low misclassification rate. The simulation results show that temperature, SiO₂, Al₂O₃, P₂O₅, and CaO have greater influences on the slag’s viscosity. The critical temperature for onset of the important influence of slag composition is 980 °C. Furthermore, it is found that SiO₂ and P₂O₅ have positive correlations with slag’s viscosity, while temperature, Al₂O₃, and CaO have negative correlations. A two-equation model of six-degree polynomial combined with Arrhenius formula is also established for the purpose of providing theoretical guidance for industrial application and reutilization of slag.