Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation.

Despite the potential of learning analytics for personalized learning, it is seldom used to support collaborative learning particularly in face-to-face (F2F) learning contexts. This study uses learning analytics to develop a dashboard system that provides adaptive support for F2F collaborative argumentation (FCA). This study developed two dashboards for students and instructors, which enabled students to monitor their FCA process through adaptive feedback and helped the instructor provide adaptive support at the right time.

How women differently felt guilt from men in Korea: Focusing on the influence of demographic factors and leisure motivation.

Although leisure provides numerous advantages, people sometimes feel guilty while engaging in leisure. The study objective was to explore the gender differences in the effects of socio-demographic factors and motivational factors on leisure guilt in Korea. Results showed that socio-demographic factors and leisure motivations influenced leisure guilt and the influence of these factors was different by gender.

The ENERTALK dataset, 15 Hz electricity consumption data from 22 houses in Korea.

AMI has been gradually replacing conventional meters because newer models can acquire more informative energy consumption data. The additional information has enabled significant advances in many fields, including energy disaggregation, energy consumption pattern analysis and prediction, demand response, and user segmentation. However, the quality of AMI data varies significantly across publicly available datasets, and low sampling rates and numbers of houses monitored seriously limit practical analyses.

Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building.

In this paper, we provide findings from an energy saving experiment in a university building, where an IoT platform with 1 Hz sampling sensors was deployed to collect electric power consumption data. The experiment was a reward setup with daily feedback delivered by an energy delegate for one week, and energy saving of 25.4% was achieved during the experiment.