Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction of students’ academic achievement) based on predictive models. However, the class imbalance problem in educational datasets could hamper the accuracy of predictive models as many of these models are designed on the assumption that the predicted class is balanced. Although previous studies proposed several methods to deal with the imbalanced class problem, most of them focused on the technical details of how to improve each technique, while only a few focused on the application aspect, especially for the application of data with different imbalance ratios. In this study, we compared several sampling techniques to handle the different ratios of the class imbalance problem (i.e., moderately or extremely imbalanced classifications) using the High School Longitudinal Study of 2009 dataset. For our comparison, we used random oversampling (ROS), random undersampling (RUS), and the combination of the synthetic minority oversampling technique for nominal and continuous (SMOTE-NC) and RUS as a hybrid resampling technique. We used the Random Forest as our classification algorithm to evaluate the results of each sampling technique. Our results show that random oversampling for moderately imbalanced data and hybrid resampling for extremely imbalanced data seem to work best. The implications for educational data mining applications and suggestions for future research are discussed.

In this study, we present three types of unsupervised anomaly detection to identify anomalous test-takers based on their action sequences in problem-solving tasks. The first method relies on the use of the Isolation Forest algorithm to detect anomalous test-takers based on raw action sequences extracted from process data. The second method transforms raw action sequences into contextual embeddings using the Bidirectional Encoder Representations from Transformers (BERT) model and then applies the Isolation Forest algorithm to detect anomalous test-takers. The third method follows the same procedure as the second method, but it includes an intermediary step of dimensionality reduction for the contextual embeddings before applying the Isolation Forest algorithm for detecting anomalous cases. To compare the outcomes of the three methods, we analyze the log files from test-takers in the US sample ( n = 2,021) who completed the problem-solving in technology-rich environments (PSTRE) section of the Programme for the International Assessment of Adult Competencies (PIAAC) 2012 assessment. The results indicated that different groups of test-takers were flagged as anomalous depending on the representation (raw action sequences vs. contextual embeddings) and dimensionality of action sequences. Also, when the contextual embeddings were used, a larger number of test-takers were flagged by the Isolation Forest algorithm, indicating the sensitivity of this algorithm to the dimensionality of input data.

Despite its proven success in various fields such as engineering, business, and healthcare, human–machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human–machine collaboration for improving the efficiency and accuracy of decision-making processes in educational settings. High school dropout prediction serves as a case study for examining human–machine collaboration’s efficacy. Unlike previous research prioritizing high accuracy with immutable predictors, this study seeks to bridge gaps by identifying actionable factors for dropout prediction through a framework of human–machine collaboration. Utilizing a large dataset from the High School Longitudinal Study of 2009 (HSLS:09), two machine learning models were developed to predict 9th-grade students’ high school dropout history. Results indicated that the Random Forest algorithm outperformed the deep learning algorithm. Model explainability revealed the significance of actionable variables such as students’ GPA in the 9th grade, sense of school belonging, self-efficacy in mathematics and science, and immutable variables like socioeconomic status in predicting high school dropout history. The study concludes with discussions on the practical implications of human–machine partnerships for enhancing student success.

Parental self-efficacy beliefs develop over time. Most research, however, has focused mainly on the trajectories and predictors of trajectories of maternal self-efficacy, while little is known about those of paternal self-efficacy. This study examined the change in paternal self-efficacy for educational involvement during children's elementary school period, analyzing whether the change is influenced by fathers' long working hours (i.e., work hours on workdays and work hours on nonworkdays) and fathers' perceptions of time and energy. Data from 1,684 Chinese fathers of fourth grade children were collected every half year for two-and-a-half consecutive years. The results of a latent growth curve analysis revealed that paternal self-efficacy for educational involvement increased over the elementary school period. Fathers' working hours on nonworkdays were negatively associated with the initial level of paternal self-efficacy for educational involvement, but this negative effect was nonsignificant after fathers' perceived time and energy were added to the final model. Fathers' perceived time and energy were positively associated with the initial level of paternal self-efficacy for educational involvement and negatively associated with the growth rate of paternal self-efficacy for educational involvement. The findings advance the theory of parental self-efficacy, underlining the need to consider fathers' perceived time and energy to understand changes in paternal self-efficacy for educational involvement.