Dataset Type: a CSV file containing messages from Twitter
Step 1: Download the CCK11 dataset to your computer
LASI_CCK_11_Twitter – sample.csv
Sample dataset
Notes:
- This dataset contains a sample of public tweets posted by participants in a Massive Open Online Course called CCK11.
Step 2: Login to  with your Google or Yahoo ID
Step 3: Import the sample dataset from Step 1 as âText Fileâ. First click add new dataset, select the Text file tab and then upload the file to Netlyic. Don’t forget to give your dataset a name.
The next screen will show you whether the dataset has been imported properly.
Step 4: Click âNext Stepâ and go to the âText Analysisâ tab, then click âAnalyzeâ under âKeyword Extractorâ
Step 5. Click on â# Remaining Postsâ to analyze the data. Once all records have been processed, click on the “Words Cloud” button. A separate pop-up window will show a tag cloud of frequently used words.
NOTE: For more details on how to use this feature, see the online help page at
Step 6. Click the red color X-button to the right of the âcck11â tag to remove it (since it is a common hashtag and would be consider a noise word for the purpose of this analysis).
Step 7. Click on any of the remaining words in the word cloud to examine the exact instance(s) where these words appeared in your dataset. Â (To get more context as to why a particular word was used so frequently, click and examine words that might be surprising or unfamiliar to you.)
Step 8. Scroll down to the âDictionaries/ Manual Categoriesâ section and click the green â# Remaining Postsâ button to analyze the dataset.Â
Step 9. Once the analysis is complete, click on the “Visualize” button under the Categories section to explore the resulting clusters.
Step 10. Close the pop-up window.  Next, in order to create your own categories designed specifically for studying collaborative learning, go back to the â3. Text Analysisâ tab and click on the âCreate/Edit Dictionariesâ button in the âDictionaties/Manual Categoriesâ section
NOTE: For more details on how to create your own categories, Â see the online help page at /?page_id=11101Â
Step 11. In the pop-up screen, disable (delete) the demo categories and create the following categories in Netlytic. Discuss what keywords/phases did you include in each category and why.
- Learning
- Connecting
- Innovation
Step 12. Go to the â4. Network Analysisâ tab, find the âName Networkâ section and select the dataset type as âTwitterâ from the drop down menu, then click the â# Remaining Postsâ button to analyze the dataset.
Step 13. Once the network is built, click on the âVisualizeâ button
Step 14. Review some of the most connected members of the class, as indicated by the larger node size, and then read some of the messages exchanged among the class participants to understand the formation of connections among the students.
To access individual tweets, click on the node/person in question and then click on any of the connecting nodes/names listed in the left pane.Â
Step 15. Annotate 3-5 areas and/or clusters in the network by using the Notes feature.
To label and annotate information about the various cluster and individual in the network visualization, use of the yellow âSticky Notesâ feature. To activate this feature, click the yellow box containing a plus sign located in the bottom right hand corner of the network visualization window. This feature allows you, the researcher, to label and annotate information about the network.
As you add sticky notes to the network visualization, they will appear at the bottom lower right hand corner of the network visualization screen along with information about the zoom level associated with each note.(e.g. 0%, 25%, 50%, etc.)
To capture a snapshot of your network and any sticky notes about your network, click on the âSave Imageâ button in the left pane.  You can only save and publicly share up to three snapshots at a time in the system. If you want to take and save additional snapshots of your network, you will need to save them to your computer first and delete them from the system to make room for new snapshots of your network. For example you might want to take additional snapshots because you want to show/document something interesting about the interactions of a particular clusters of students from your class.