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Big data in education promotes access to the analysis of learning behavior, yielding many valuable analysis results. However, with obscure and insufficient guidelines commonly followed when applying the analysis results, it is difficult to translate information knowledge into actionable strategies for educational practices. This study aimed to solve this problem by utilizing the learning analytics (LA) framework. We proposed a learning analytics framework based on human-centered Artificial Intelligence (AI) and emphasized its analysis result application step, highlighting the function of this step to transform the analysis results into the most suitable application strategy. To this end, we first integrated evidence-driven education for precise AI analytics and application, which is one of the core ideas of human-centered AI (HAI), into the framework design for its analysis result application step. In addition, a cognitive load test was included in the design. Second, to verify the effectiveness of the proposed framework and application strategy, two independent experiments were carried out, while machine learning and statistical data analysis tools were used to analyze the emerging data. Finally, the results of the first experiment revealed a learning strategy that best matched the analysis results through the application step in the framework. Further, we conclude that students who applied the learning strategy achieved better learning results in the second experiment. Specifically, the second experimental results also show that there was no burden on cognitive load for the students who applied the learning strategy, in comparison with those who did not.

Educational data mining and learning analytics have become a very important topic in the field of education technology. Many frameworks have been proposed for learning analytics which make it possible to identify learning behavior patterns or strategies. However, it is difficult to understand the reason why behavior patterns occur and why certain strategies are used. In other words, all of the existing frameworks lack an important step, that is, result confirmation. In this paper, we propose a Result Confirmation-based Learning Behavior Analysis (ReCoLBA) framework, which adds a result confirmation step for exploring the hidden reasons underlying the learning patterns and strategies. Using this ReCoLBA framework, a case study was conducted which analyzed e-book reading data. In the case study, we found that the students had a tendency to delete markers after adding them. Through an investigation, we found that the students did this because they could not grasp the learning emphasis. To apply this finding, we proposed a learning strategy whereby the teacher highlights the learning emphasis before students read the learning materials. An experiment was conducted to examine the effectiveness of this strategy, and we found that it could indeed help students achieve better results, reduce repetitive behaviors and save time. The framework was therefore shown to be effective.

Online learning has become a part of daily life. However, it has fostered a tendency to shallow reading. Notably, little attention has been paid to precisely identifying shallow reading behaviors using quantified education big data in favor of methods such as conceptual descriptions, opinions, and questionnaires, resulting in an imprecise analysis of its impact on learning and a lack of strategies to mitigate its causative factors. This study addresses the issue by introducing a behavior-identifying model grounded in measurable data to precisely identify shallow reading behaviors. Initially, three key dimensions of shallow reading behaviors were proposed, along with corresponding mathematical indicators. Subsequently, the k-means algorithm and Pearson correlation coefficient were employed to evaluate the relationship between shallow reading and learning performance, underscoring its significance. The analysis showed that shallow reading behaviors are highly associated with poor learning performance, thus hindering critical reading. These findings further validate the predominant influence of reading speed on learning performance. Consequently, a strategy was developed to intentionally decrease reading speed. Following that, an experiment was conducted to assess the effectiveness of this strategy in mitigating shallow reading. The results revealed improved learning performance and a significant reduction in shallow reading behaviors without increasing cognitive load. [ABSTRACT FROM AUTHOR]

This study investigated the occurrence and abundance of class 1 integrons and related antibiotic resistance genes (ARGs) in a sewage treatment plant (STP) of China. Totally, 189 bacterial strains were isolated from influent, activated sludge and effluent, and 40 isolates contained the integons with a complete structure. The intl1-carrying isolates were found to harbor two types of gene cassettes: dfr17-aadA5 and aadA2, conferring resistances to trimethoprim and streptomycin, which were further confirmed by antimicrobial susceptibility analysis. Many other gene cassettes were carried on integron, including qnrVC1, catB-8-blaoxa-10-aadA1-aac(6'), aadB-aacA29b, aadA2, aac(6')-1b, aadA6 and aadA12, which were detected using DNA cloning. Quantitative real time PCR showed that over 99% of the integrons was eliminated in activated sludge process, but average copy number of integrons in given bacterial cells was increased by 56% in treated sewage. Besides integrons, other mobile gene elements (MGEs) were present in the STP with high abundance. MGEs and the associated ARGs may be wide-spread in STPs, which constitute a potential hot spot for selection of antibiotic resistant bacteria and horizontal transfer of ARGs.