# A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System
## 5 Results and Discussion
### Societal and Environmental Well-Being  
In line with the prevention of harm on individuals, social influence should be analyzed as well. Workforce impact can deteriorate people's physical and mental well-being; meanwhile, environmental effects should be evaluated to reduce the carbon footprint on AI utilization.  
AI systems are commonly untrusted when they directly impact the workforce [99; 100]. Education can be equally affected if AI-powered LMS can be proved as a replacement for teachers. However, AI tools should enforce teachers' work instead of thinking of substituting [101]. As authors in [102] (p. 1) claimed, AI should never remove \"the role of the teacher because how it works and what it does is so profoundly different from human intelligence and it has the potential to transform education in ways that make education more human, not less.\" AI can leverage teacher's work on repetitive tasks or enhance the personalization. As aforesaid in Section 5.1, the designed EWS can be set up to automate the teacher's recommendation task. The autonomous modecan be configured with basic recommendations provided as templates by the system. However, the semi-autonomous mode is also available, wherein the teacher's expertise is considered. This second option is the recommended one because the teacher knows which resources, exercises, and learning paths can help the learner pass the course with their expertise. Moreover, this option increases trustworthiness with the system because the system empowers the teacher with additional knowledge about the learners' behavior and needs. Therefore, better personalization can be supplied.  
Furthermore, environmental effects are considered. Training models require an increment of energy consumption due to the resource's utilization and operation calculations. The leading research in this topic focuses on evaluating energy consumption in terms of software (i.e., language programming libraries for AI) [103] and hardware levels (i.e., identifying critical circuit paths during AI operations) [104]. In our infrastructure, we focus on the software level in the computational service. The service has been implemented to process only registered courses in the system and learners who consented to participate. Thus, the operations have been optimized and run only for active courses and learners. Three main operations affect energy consumption: read/write operations, training, and predicting outcomes.  
The read/write operations from MongoDB are a critical bottleneck when big data pipelines are implemented [105]. Here, it is critical to design a proper schema and query aggregation pipeline. Aforesaid, the document schema is created on the basis of the query language for the ETL process described in Section 4.1.4, storing each learner as a document and identified by the anonymized identifier. All the information referred to a learner is stored in subdocuments identified by the name of the data mart dataset. The query aggregations can quickly obtain data for a specific learner by querying by document identifier. The critical operation is the dataset creation for training because the collection associated with the view must be parsed slicing by course and activity identifiers using the query language described in Section 4.1.5. In such a case, only MongoDB cache and RAM size can help to get results faster. However, the training task is only performed during developer testing or when setting up the model for a course, and it is only performed once at the beginning of the semester for the registered course. Finally, the prediction is only performed when grade information for a specific activity is detected in the data mart. Thus, it implies only a read operation and a prediction for each course, activity, and learner.