applying analytics for a learning portal (lak 2011)

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Study on Google Analytics set up for a Learning Portal on Organic Agriculture & Agroecology

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Applying Analytics for a Learning Portal: the Organic.Edunet Case Study

Vassilis Protonotarios, Nikos Palavitsinis, Nikos Manouselis

Greek Research & Technology NetworkGreek Research & Technology Network

1st International Conference on Learning Analytics and Knowledge 2011

pre-intro

• Overwhelmed by LAK 2011

• We used Google Analytics for this paper

• Originally deployed for reporting purposes

• METADATA: This presentation has 18 more slides (9 of them with pictures/figures)

about Organic.Edunet

• Completed eContentPlus project (10/2007 – 10/2010) funded with 2.5M €

• Main objective: Make learning content on Organic Agriculture and Agro-ecology available online, for a range of stakeholders.

• And: Develop educational scenarios to be used within schools & universities

Organic.Edunet Portal

• Went online on January 2010 (13 months)– www.organic-edunet.eu

• 30.700 visits• 146.100 page views• 21.400 unique visitors• 150 countries• 10.900 resources • 1.800 registered users

22.100 120.00014.300130same1.0003 months ago

open days

• Validation events organized in all countries of the project partners, where users from institutions outside the project worked with the portal

• Predefined program for each “Open Day” & structured portal exercises which also allowed for unstructured interaction with the portal

research question

• How do the usage statistics both on the portal level but also on the individual user level, change, after a series of training events on the portal is organized?

• Do the users take full advantage of the portal functionalities we developed?

methodology

• Statistics that concern the Portal as a whole– Visits per day, page views per day– Visitors per day, origin of visits– Bounces per day & most popular pages

• Statistics that concern each individual user– Average time on portal, average time on page– Pages per visit, depth of visit, user loyalty

methodology

• Study these statistics in three distinct periods– Pre-Open Day, During & Post-Open Day

• Two groups of Open Days where organized one during April-May 2010 and another during August-September 2010

• 160 participants in 13 Open Days

March 2010

May 2010 August 2010

September 2010

January 2010 November 2010

During-OD

Post-OD

timeline

portal statistics

0

10

20

30

40

50

60

PRE-OD OD1 POST-OD1 OD2 POST-OD2

Visits per day Visitors per day Bounces per day

visit sources

0%

20%

40%

60%

80%

100%

PRE-OD OD1 POST-OD1 OD2 POST-OD2

Direct Search Engines Refer.

popular pages

Visits per day Pre-OD OD1 Post-OD1 OD2 Post-OD2

Homepage 102 138 118 136 81

Text-based Search 51 61 49 73 59

How to Search 30 30 18 27 3

Educational Scenarios 15 5 7 5 6

Browse 12 14 7 9 5

Semantic Search 6 9 14 14 17

Tag search 4 4 2 2 2

depth of visit

A deep visitor would open more pages in one visit than the average pages per visit of the portal for the same period of reference

25,00%

26,00%

27,00%

28,00%

PRE-OD OD1 POST-OD1 OD2 POST-OD2

visitor loyalty

A loyal visitor is defined as the one coming back to the Organic.Edunet Portal more than once per week

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

PRE-OD OD1 POST-OD1 OD2 POST-OD2

some conclusions

• Traditional text-based search prevails over other means of searching,

• More users keep coming to the portal, spending less time on it, viewing less pages

• More users keep coming to the portal but also bouncing off of it

let’s take one step back

TAKEN FROM: http://www.adventuresoutwest.com/climber-cartoon-web-style.jpg

What? Go back

down?

lots of variables to consider

• Language of the resources available?

• Seasonality (schools & universities)

• “Black box” of each Open Day

• Cultural differences?

• Affiliation strategy of the project & press releases

open issues

• Deployment of a different approach on analytics that will allow in-depth statistics to be recorded?

• Sophisticated analysis of existing statistics?

• Also examine user ratings, comments, bookmarks in relation to these statistics

• How far can we go?

Thank you!

1st International Conference on Learning Analytics and Knowledge 2011

palavitsinis@grnet.gr

30/5 – 3/6

www.jtelsummerschool.eu

PhD students: 18/3/2011

PhD advisors: 31/3/2011

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