My workshop at the Learning Analytics Summer Institute (LASI) 2016: http://lasi16.snola.es/#!/schedule/113 Educational data continues to grow in volume, velocity and variety. Making sense of the educational data in such conditions requires deployment and usage of appropriate scalable, real-time processing tools supporting a flexible data schema. Elasticsearch is one of the popular open-source tools meeting the enlisted requirements. Initially envisioned as a search engine capable of operating at scale and in real time, Elasticsearch is used by organisations such as Wikimedia and Github, which deal with big data on daily basis. In addition, Elasticsearch is used increasingly often as analytics platform thanks to its scalable architecture and expressive query language. Until recently, the exploitation of Elasticsearch for (learning) analytical purposes by practitioners was hindered by a high entrance barrier due to the complexity of the query language and the query specificities. This is currently changing with the ongoing development of Kibana, an open-source tool that allows to conduct analysis and build visualisations of Elasticsearch data through a graphical user interface. Kibana does not require the user to dive into technical details of the queries (although it is still possible) and hence makes big educational data visualisations accessible to regular users. The additional value of Kibana comes in play whenever several visualisations are combined on a single dashboard, enabling to use multiple coordinated views for an interactive explorative analysis. Both Elasticsearch and Kibana, together with Logstash are part of an analytics stack often referred to as ELK. Logstash supports data acquisition from multiple sources (including twitter, RSS, event logs) thanks to its rich set of available connectors. Custom connectors can be developed for case-specific sources. In addition to the mentioned values, ELK enables building analytics infrastructures decoupled from the learning platform, i.e., it allows to host separately the learning environment (with the analytics functionalities) and the data storage without affecting the end-user experience.

1. Interactive Learning Analytics
with ELK
(Elasticsearch Logstash Kibana)
Andrii Vozniuk, María Jesús Rodríguez-Triana, Denis Gillet
The copyright of images belongs to their authors. I will remove them on demand. Contact me at andrii.vozniuk@epfl.ch
Bilbao, June 2016
Learning Analytics Summer Institute (LASI)
Workshop description: http://lasi16.snola.es/#!/schedule/113

2. REACT - EPFL - Lausanne

3. Goals of the Workshop
1. Get understanding of modern, scalable
analytics tools: Elasticsearch & Kibana
2. Get hands-on experience of interactive
learning analytics with the tools
3. Elaborate on how the tools can be
useful in your specific cases

4. Agenda
My presentation (40 minutes)
Groupwork (30 minutes)
Discussion (20 minutes)

5. Motivation

6. REACT
=
Coordination & Interaction
Systems Group

7. SpeakUp
is a co-located
social media to
improve audience
interaction
speakup.info

8. graasp.eu
Graasp
a social media as a
personal and
collaborative
learning environment

9. graspeo.org
Graspeo
a social media as a
knowledge sharing
platform for
humanitarian orgs

10. How the
tools are
being used
?We (researchers) want to know it
Teachers want to know it
Even students want to know it
Knowledge managers as well
for awareness & reflection

11. Approach

12. Interaction Traces
✓Record
✓Store
✓Visualise
✓Interact

13. Interactive
User-oriented
Analytics
Learning
Dashboards

14. Capturing Interactions
with xAPI

15. What is the purpose of xAPI?
xAPI = Experience API
It is a standard
to capture in a unified way
experiences of the user,
in our case user-tool interactions

16. Source: http://www.slideshare.net/aaronesilvers/going-with-xapi
xAPI

17. How does xAPI Work?
Users interact with tools
These interactions are observed and
recorded by the tools as xAPI statements
The tools [store] and send the statements to a
central system (Learning Record Store or
LRS) for further usage, for instance, Analysis

18. xAPI Format Specification
• When? - timestamp
• Who? - actor
• Did what? - verb
• With what? - object
• With what result? - result
• In which context? - context
Today at 10:15 (time) Andrii (actor) answered (verb) the question five
(object) with the grade four (result), while it was raining outside (context)

19. xAPI Statement Example

20. OK, we track the
interactions with xAPI.
What’s next?

21. ✓Visualisation
✓Interactive Visualisation
✓Visual Analysis
✓Dashboards
✓Recommendations

22. ELK =
Elasticsearch
+ Logstash
+ Kibana
<-This is
an elk
as well

23. The ELK Stack
Elasticsearch - search and analytics
database based on Lucene
Logstash - data ingestion and
transformation
Kibana - Interactive data visualisation

24. Have you used
Elasticsearch or Logstash or
Kibana?

25. Logstash Elasticsearch
Date
Source
Kibana
Reads
Transforms
Sends
Maps
Indexes
Visualises
Interaction
Processing Pipeline

26. elastic.co/products/logstash

27. Logs are not the best format for analysis
Especially, when coming from multiple systems

28. Getting timestamp should
be easy, right?
• Apache [19/Feb/2015:19:00:00 +0000]
• Unix timestamp 1424372400
• log4j [2015-02-19 19:00:00,000]
• postfix.log Feb 19 19:00:00
• ISO 8601 2015-02-19T19:00:00+02:00
• …
Over 40 formats
Not sexy work to do

29. Logstash Solves
the Problem
It collects, transforms and
transmits logs (streams)
So they can be stored and analysed
in a centralised and unified way

30. Logstash Processing Pipeline
Filter OutputInput
file
syslog
jdbc
log4j
couchdb
s3
twitter
…
elasticsearch
jira
hipchat
s3
http
sqs
zeromq
…
anonymise
aggregate
csv
date
clone
geoip
prune
...

31. 31
but there is more than that
elastic.co/products/elasticsearch

32. 32
Elasticsearch Users
Source: http://www.slideshare.net/OleksiyPanchenko/elasticsearch-51730805

33. Source: http://www.slideshare.net/ruslanzavackiy/introduction-to-elasticsearch-45118828

34. Source: http://www.slideshare.net/ruslanzavackiy/introduction-to-elasticsearch-45118828

35. Mapping Analysis QueryData

36. Relational DBs vs Elasticsearch
Database
Table
Row
Column
Schema
Index
Type
Document
Field
Mapping

37. Default mapping is created automatically
Mapping
Differently from relational databases,
where schema is static,
in Elasticsearch mapping is flexible
Core types: string, integer/long, float/double, boolean, and null
Other types: Array, Object, Nested, IP, GeoPoint, GeoShape,
Attachment
Tells Elasticsearch how to treat the data

38. Mapping for xAPI Statements
…

39. Analysis
Analysis is about extracting terms from a given text

40. Query
Elasticsearch provides an expressive
Query Domain Specific Language
to query the data
Can be thought as SQL for non-
relational data.
The query itself is a JSON Object.

41. Sample
Query
Contains
- query
- filter
- highlight

42. We mapped and
indexed the data
in Elasticsearch
What’s next?

43. elastic.co/products/kibana

44. Kibana
is for Interactive Dashboards

45. Another example
with light colors scheme

46. Kibana
• Enables near-real-time analysis and
visualisation of streaming data
• Allows interactive data exploration and
supports cross-filtering
• Multiple chart types: bar charts, line and
scatter plots, histograms, pie charts,
maps
• No need to know programming or query
language in most of the cases
• It’s open-source, there are extensions

47. Logstash Elasticsearch
Date
Source
Kibana
Again, what is ELK?

48. I stop talking!
We start doing real things together!

49. Let’s Form Groups
4-5 per group
Time to try it yourself!

50. Generating
xAPI Stream
My open-source xAPI
Stream Generator
We can have fun
seeing it all
update in real-time
github.com/voz/xapi-generator

51. Sample Generated
xAPI Statement
• timestamp
• actor
• id
• name
• mbox
• verb
• id
• display
• object
• id
• definition
• name
• description
• objectType
• context
• ipAddress
• location
• city
• countryCode
• countryName

52. Interaction Types
• abandoned
• answered
• asked
• attempted
• attended
• commented
• completed
• exited
• experienced
• failed
• imported
• initialized
• interacted
• launched
• mastered
• passed
• preferred
• progressed
• registered
• responded
• resumed
• satisfied
• scored
• shared
• suspended
• terminated
• voided
• waived
• messaged
• invited
• mentioned
…many of them

53. Demo Time

54. go.epfl.ch/lasi2016
login: admin
pass: lasi2016
A running Elasticsearch cluster on
A 14-day free trial cluster
* they don’t pay me :)
www.elastic.co/cloud

55. Group Work
What to do?
1. Formulate a research question
2. Build a visualisation to answer it, save it
3. If you have time left, repeat 1 and 2 :)
Possible research question examples
• Who are the most active users? And per country?
• What are the most popular types of interaction in general?
• What are the content items (objects) used the most per country (per user)?
• What types of interaction are the most common? Per country? Per user?
• With whom the most active users communicate (message or mention)?
• From which countries most of the interactions happen?
go.epfl.ch/lasi2016
login: admin
pass: lasi2016

56. Embedded &
Contextual
Analytics

57. Analytics as a part of the Interaction Context
Try yourself on graasp.net

58. Analytics as a part of the Interaction Context
We call it “User-oriented” analytics

59. Data Ownership

60. Who owns
learning data?
Institution?
Learner?
Parents?
Who can use it
and for what?

61. Source: https://www.flickr.com/photos/gforsythe/8508041553/
It’s complicated

62. Possible Architecture
Platform provides a UI
to interact with the data
without storing the data
Data is stored on premises:
in a LRS of a school
or of a user
Push the traces
Get the analysis

63. Other
Possible Applications

64. Elasticsearch
is about search
Has powerful NLP
toolkit in place
For Content Analytics

65. Learning Resources

66. Online
https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html
Go through it. It is really good.
Elasticsearch Reference
Multiple tutorials
http://www.elasticsearchtutorial.com/
https://www.elastic.co/guide/index.html
ELK Guides
YouTube Videos
https://www.youtube.com/watch?v=Kqs7UcCJquM
https://www.youtube.com/watch?v=wHWb1d_VGp8
https://www.youtube.com/watch?v=1gnpzL9jBqY
https://www.youtube.com/watch?v=SH5hLM2asB8

67. Books

68. Books

69. Questions?
Feedback?
about.me/vozniuk
Andrii@Vozniuk.com