It has been on my ‘to read’ list for a long time, but I’m now just getting around to the 124 page 2013 Moodle Research Conference published papers. This is the full program from the 2013 MRC and includes lots of papers only found in the book (download it at http://research.moodle.net/course/view.php?id=4)
Most recently I’ve read “Using Excel Macros to Analyse Moodle Logs” by Dr. Andreas Konstantinidis and Dr. Cat Grafton of King’s College London. According to the abstract,
Learning analytics enables tutors to gain useful insights on the behaviour of students in an online learning environment. This information can then be utilised to customize the educational space, optimize the learning resources and activities, and personalize the student experience. This paper presents our approach to analysing the data of users’ behaviours that are recorded in the Moodle logs. Currently, the Moodle logs manager suffers from functional limitations and uninspiring visualizations. Our method utilises the possibility of downloading the logs in Microsoft Excel format and provides a simple and effective offline solution. The method we have developed is based on Excel macros and visual basic. Tutors can experiment with different combinations of metrics such as total page views, unique users, unique actions, IP addresses, unique pages, average session length and bounce rate. Furthermore, the software allows the definition of date ranges and the selection of individual or groups of students. The complicated processes of analysing and combining data are carried out in the background, enabling tutors to focus on the pedagogic implications and invest in practical, realistic scenarios through informed decision-making. Future work includes transferring the offline functionality to an online Moodle plugin and increasing system intelligence to allow the production of meaningful and actionable suggestions with regards to set target goals.
The seven page paper is an interesting look at how and why the logs were analyzed and provides some insight to the value of data just waiting in Moodle logs. From the logs the researchers were able to calculate
- Total page views
- Total unique users
- Total unique actions
- Total unique pages
- Total IP addresses
- Approximate mean session length
After importing the log data a nice visualization of aggregated user information was available to the tutors (below). This was helpful in setting a benchmark or average for students and for gaining insight about days of the week, spikes, drilling down to certain pages or users, etc.
In conclusion the writers suggest that the next steps are most important: the data can be used to better prepare students through feedback and additional assessment, and recommend action or behavior. Pretty cool stuff. Hope to see their continued research this year at the MRC2014 in CA.