Context: Learning analytics is considered as the third wave in educational technology and it is a new and promising field of study. This study was conducted to clarify benefits and challenges of learning analytics in education. Methods: Cooper's systematic literature review was used as the research method. This method has five steps as follows: a) formulation of the problem, b) collection of data, c) evaluation of the appropriateness of the data, d) analysis and interpretation of relevant data, and e) organization and presentation of the results. Based on the study selection process, 36 articles were finally selected to be analyzed. Results: The results showed that ethics and privacy were one of the most important challenges of learning analytics in education along with the lack of attention to theoretical foundations and scope and quality of data. The results also showed that learning analytics could bring remarkable benefits for education, such as increased engagement of students, improvement of learning outcomes, identification of students at risk, providing real-time feedback, and personalization of learning. Conclusions: Based on the results, it can be concluded that learning analytics offer new insights in education, however, there are ethical, educational, and technical issues in the use of learning analytics in education.
Clustering is a technique used in network routing
Mobile agent data aggregation routing forwards mobile agents in wireless sensor network to collect and aggregate data. The key objective of data aggregation routing is to maximise the number of collected data samples at the same time as minimising network resource consumption and data collection delay. This paper proposes a mobile agent routing protocol, called Zone-based Mobile agent Aggregation. This protocol utilises a bottom-up mobile agent migration scheme in which the mobile agents start their journeys from the centre of the event regions to the sink aiming to reduce the MA itinerary cost and delay and increase data aggregation routing accuracy. In addition, the proposed protocol reduces the impact of network architecture, event source distribution model and/or data heterogeneity on the performance of data aggregation routing.
Time synchronization plays an important role in the performance of wireless sensor networks. It can enhance the throughput and the lifetime of the network by improving the energy-efficiency, the freshness of collected data and reducing the network traffic and message conflicts. Due to the constraints on sensor nodes' energy resources and the vulnerability of the distributed infrastructure of wireless network, an efficient, scalable and accurate time synchronization protocol is desirable. This paper presents an accurate and efficient reactive protocol, named HRTS, that synchronizes the sensor nodes' clock based on the node's demand. It minimizes the synchronization region dynamically to the set of nodes which request synchronization. HRTS improves the accuracy of the synchronization procedure by measuring time parameters accurately and removing delays accordingly. Compared with the conventional time synchronization protocols like PCTS and TPSN, HRTS also reduces energy consumption by decreasing the traffic overheads.
The COVID-19 pandemic has impacted electricity consumption patterns and such an impact cannot be analyzed by simple data analytics. In China, specifically, city lock-down policies lasted for only a few weeks and the spread of COVID-19 was quickly under control. This has made it challenging to analyze the hidden impact of COVID-19 on electricity consumption. This paper targets the electricity consumption of a group of regions in China and proposes a new clustering-based method to quantitatively investigate the impact of COVID-19 on the industrial-driven electricity consumption pattern. This method performs K-means clustering on time-series electricity consumption data of multiple regions and uses quantitative metrics, including clustering evaluation metrics and dynamic time warping, to quantify the impact and pattern changes. The proposed method is applied to the two-year daily electricity consumption data of 87 regions of Zhejiang province, China, and quantitively confirms COVID-19 has changed the electricity consumption pattern of Zhejiang in both the short-term and long-term. The time evolution of the pattern change is also revealed by the method, so the impact start and end time can be inferred. Results also show the short-term impact of COVID-19 is similar across different regions, while the long-term impact is not. In some regions, the pandemic only caused a time-shift in electricity consumption; but in others, the electricity consumption pattern has been permanently changed. The data-driven analysis of this paper can be the first step to fully interpret the COVID-19 impact by considering economic and social parameters in future studies.
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