On Mon, Jan 6, 2014 at 12:28 AM, Martin Dluhos <mar...@gnu.org> wrote: > On 3.1.2014 04:09, Sameer Verma wrote: >> Happy new year! May 2014 bring good deeds and cheer :-) >> >> Here's a blog post on the different approaches (that I know of) to data >> gathering across different projects. Do let me know if I missed anything. >> >> cheers, >> Sameer >> >> http://www.olpcsf.org/node/204 > > Thanks for putting together the summary, Sameer. Here is more information > about > my xo-stats project: > > The project's objective is to determine how XOs are used in Nepalese > classrooms, but I am intending for the implementation to be general enough, so > that it can be reused by other deployments as well. Similarly to other > projects > you've mentioned, I separated the project into four stages: > > 1) collecting data from the XO Journal backups on the schoolserver > 2) extracting the data from the backups and storing it in an appropriate > format > for analysis and visualization > 3) statistically analyzing and visualizing the captured data > 4) formulating recommendations for improving the program based on the > analysis. > > Stage 1 is already implemented on both the server side as well as the client > side, so I first focused on the next step of extracting the data. Initially, I > wanted to reuse an existing script, but I eventually found that none of them > were general enough to meet my criteria. One of my goals is to make the script > work on any version of Sugar. > > Thus, I have been working on process_journal_stats.py, which takes a '/users' > directory with XO Journal backups as input, pulls out the Journal metadata and > outputs them in a CSV or JSON file as output. > > Journal backups can be in a variety of formats depending on the version > of Sugar. The script currently supports backup format present in Sugar > versions > 0.82 - 0.88 since the laptops distributed in Nepal are XO-1s running Sugar > 0.82. I am planning to add support for later versions of Sugar in the next > version of the script. > > The script currently supports two ways to output statistical data. To produce > all statistical data from the Journal, one row per Journal record: > > process_journal_stats.py all > > To extract statistical data about the use of activities on the system, use: > > process_journal_stats.py activity > > The full documentation with all the options are described in README at: > > https://github.com/martasd/xo-stats > > One challenge of the project has been determining how much data processing to > do > in the python script and what to leave for the data analysis and visualization > tools later in the workflow. For now, I stopped adding features to the script > and I am evaluating the most appropriate tools to use for visualizing the > data. > > Here are some of the questions I am intending to answer with the > visualizations > and analysis: > > * How many times do installed activities get used? How does the activity use > differ over time? > * Which activities are children using to create files? What kind of files are > being created? > * Which activities are being launched in share-mode and how often? > * Which part of the day do children play with the activities? > * How does the set of activities used evolve as children age? > > I am also going to be looking how answers to these questions vary from class > to > class, school to school, and region to region. > > As Martin Abente and Sameer mentioned above, our work needs to be informed by > discussions with the stakeholders- children, educators, parents, school > administrators etc. We do have educational experts among the staff at OLE, who > have worked with more than 50 schools altogether, and I will be talking to > them > as I look beyond answering the obvious questions. >
We should start a list on the wiki to collate this information. I'll get someone from Jamaica to provide some feedback as well. > For visualization, I have explored using LibreOffice and SOFA, but neither of > those were flexible to allow for customization of the output beyond some a few > rudimentary options, so I started looking at various Javascript libraries, > which > are much more powerful. Currently, I am experimenting with Google Charts, > which > I found the easiest to get started with. If I run into limitations with Google > Charts in the future, others on my list are InfoVIS Toolkit > (http://philogb.github.io/jit) and HighCharts (http://highcharts.com). Then, > there is also D3.js, but that's a bigger animal. Keep in mind that if you want to visualize at the school's local XS[CE] you may have to rely on a local js method instead of an online library. > > Alternatively or perhaps in parallel, I am also willing to join efforts to > improve the OLPC Dashboard, which is trying to answer very similar questions > to > mine. I'll ping Leotis (cc'd) to push his dashboard code to github, so we don't reinvent. cheers, Sameer > > I am looking forward to collaborating with everyone who is interested in > exploring ways to analyze and visualize OLPC/Sugar data in a interesting and > meaningful way. > > Cheers, > Martin _______________________________________________ Devel mailing list Devel@lists.laptop.org http://lists.laptop.org/listinfo/devel