Despite the potential of learning analytics for personalized learning, it has seldom been used to support collaborative learning particularly in face-to-face (F2F) learning contexts. This study aims to use learning analytics to develop a dashboard system that provides adaptive support for F2F collaborative argumentation (FCA). This study developed two types of dashboards for students and an instructor, which enabled students to monitor their FCA process through adaptive feedback and helped an instructor to provide adaptive support at the right time. The effectiveness of the dashboards was examined in a university class with 88 students (56 females, 32 males) for four weeks. The dashboards significantly improved the FCA process and outcomes. The dashboards encouraged students to actively participate in FCA and create high-quality group arguments. In addition, students had a positive attitude toward the dashboard and perceived it as useful and easy to use. These findings indicate that learning analytics dashboards can be useful in improving collaborative learning through adaptive feedback and support. This study provides suggestions on how to design a dashboard for adaptive support in F2F learning contexts using learning analytics.
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. To address these challenges, we herein present the ENERTALK dataset, which contains both aggregate and per-appliance measurements sampled at 15 Hz from 22 houses. Among the publicly available datasets with both aggregate and per-appliance measurements, 15 Hz was the highest sampling rate. The number of houses (22) was the second-largest where the largest one had a sampling rate of 1 Hz. The ENERTALK dataset is also the first Korean open dataset on residential electricity consumption.
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. Post-experiment sustainability, defined as 10% or more of energy saving, was also accomplished for 44 days without any further intervention efforts. The saving was possible mainly because of the data-driven intervention designs with high-resolution data in terms of sampling frequency and number of sensors, and the high-resolution data turned out to be pivotal for an effective waste behavior investigation. While the quantitative result was encouraging, we also noticed many uncontrollable factors, such as exams, papers due, office allocation shuffling, graduation, and new-comers, that affected the result in the campus environment. To confirm that the quantitative result was due to behavior changes, rather than uncontrollable factors, we developed several data-driven behavior detection measures. With these measures, it was possible to analyze behavioral changes, as opposed to simply analyzing quantitative fluctuations. Overall, we conclude that the space-time resolution of data can be crucial for energy saving, and potentially for many other data-driven energy applications.
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