A Study on Supporting Visual Narratives Student Engagement using Big Data Technologies

A novel visual narrative framework that has been exposed to facilitate, support and enhance student commitment in an adaptive Online Learning Environment (OLE). VisEN provides explorable visual narratives modified to students in order to support them to engage with course content. The evaluation of VisEN showed that the explorable visual narratives confident the majority of improving engagement students that completed the Information Management and Data Engineering module as part of their undergraduate degree, to engage with assigned activities , and subsequently these learners enhanced their engagement levels. It might make the power system load varied complex than before which will bring difficulties in short-term load forecasting area. To overcome this issue, this paper proposes a new short-term load forecasting framework based on big data technologies. First, a cluster analysis is performed to classify daily load patterns for individual loads using smart meter data. Next, an association analysis is used to determine critical influential factors. This is followed by the application of a decision tree to establish classification rules. Then, appropriate forecasting models are chosen for different load patterns. Finally, the forecasted total system load is obtained through an aggregation of an individual load's forecasting results. Case studies using real load data show that the proposed new framework can guarantee the accuracy of short-term load forecasting within required limits.