The COVID-19 pandemic has caused educators around the world to access online-learning systems. Applying the online system involves challenges, such as the students’ need to cope with changes in their learning process, where they must develop capabilities to manage their learning more independently. Self-Regulated Learning (SRL) is an approach considered to help us understand students’ ability to manage their learning strategies and achieve improved performance. This paper aims to investigate the SRL of Indonesian students in underdeveloped areas when using a learning management system (LMS), namely SPADA, initiated by the Indonesian government. This study employed the clickstream data (CSD) of SPADA to examine students’ SRL within the first nine months of its implementation. We also analyzed the correlation of certain activities in SPADA with the students’ SRL results. The findings suggest some positive indications of SPADA implementation, particularly in promoting the students’ SRL, either students in general or in the underdeveloped areas. Some improvements indeed still need to be made on the system, including in improving the platform architecture to gain a better measurement method on students’ SRL.
ABSTRACT3D point data is utilized in various industry domains for its high accuracy to the surface information of an object. It is substantially utilized in geography for terrain scanning and analysis. Generally, 3D point data need to be changed by Gridding which produces a regularly spaced array of z values from irregularly spaced xyz data. But it requires long processing time and high resource cost to interpolate grid coordination. Kriging interpolation in Gridding has attracted because Kriging interpolation has more accuracy than other methods. However it haven't been used frequently since a processing is complex and slow. In this paper, we presented a parallel Gridding algorithm which contains Kriging and an application of grid data structure to fit MapReduce paradigm to this algorithm. Experiment was conducted for 1.6 and 4.3 billions of points from Airborne LiDAR files using our proposed MapReduce structure and the results show that the total execution time is decreased more than three times to the convention sequential program on three heterogenous clusters.
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