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Data pre-processing is carried out to ensure that the available event logs are ready to be processed with the heuristic miner algorithm. In this case data processing is to minimize redundant data log. There are two data processing step has done to lecturer and student data log.
1st Lecture data processing
The first step is filtering the attribute namely CRUD (create-read-update-delete) by selecting records that have C and U value. This step based on LMS behavior.
At the time the lecturer adds and changes learning material like activities or resources LMS will add record to CRUD attribute with C or U value.
R value for Read or Viewed the learning material. D value for Delete learning material. So we can ignore this both value
After filtering CRUD attribute
Then we did the merge subsequent events process assisted by Pro M tools using the merge subsequent events plugin to ensure that there are no redundant records.
2nd Student data processing
We develop table mapping to mapping event name attribute to activity attribute in student data log.
This table can filter activity value with their event name value that chosen from description each sub activity attribute.
This process also to minimize redundant student log data. |
IEEE/ICACT20220197 Slide.07
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