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NSDL Webmetrics: An Introduction Mick Khoo Evaluation coordinator NSDL Core Integration

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Presentation on theme: "NSDL Webmetrics: An Introduction Mick Khoo Evaluation coordinator NSDL Core Integration"— Presentation transcript:

1 NSDL Webmetrics: An Introduction Mick Khoo Evaluation coordinator NSDL Core Integration mjkhoo@ucar.edu

2 What Are Webmetrics?  Measurements of users’ interactions with a web site  Support understanding, management and improvement of web sites and online presence  There are no standard webmetrics  Different webmetrics tools measure user interaction in different ways

3 Some Basic Webmetrics Concepts  Visitor  A person visiting your site from an Internet computer  Page view  Record of that person’s viewing of a web page (html file)  Hit  Web page plus all files in that page (html + images, etc.)  Visit  Viewing of one or more linked web pages  Ends after period of inactivity (e.g. 30 mins)  Unique visit  Aggregation of visits by same visitor in a specified time  E.g. 5 visits in one day = 1 unique visit

4 5 Webmetrics Caveats  Visits ≠ page views ≠ hits  Visitor ≠ person  Not all visitors count  Traffic varies over time  Different webmetrics tools measure use on the same site in different ways

5 Caveat 1: Visits ≠ Page Views ≠ Hits Unique visits Visits Page Views Hits Day 1 Day 2 # 2 5 12 36 I I I I I I I I I I I I I I I I I I  The same visitor will produce different numbers, depending on the chosen stat  E.g. visitor Y, using site X over two days:

6 Caveat 1: Visits ≠ Page Views ≠ Hits Unique visits Visits Page Views Hits Day 1 Day 2 # 2 5 12 36 I I I I I I I I I I I I I I I I I I  The same visitor will produce different numbers, depending on the chosen stat  E.g. visitor Y, using site X over two days:

7 Caveat 2: A Visitor ≠ A Person  Multiple students on the same computer (e.g. computer lab, collaborative assignment) can count as one visitor  A teacher visiting nsdl.org from her office and later from her home computer will be counted as two separate visitors  A visitor could be a non-human bot/crawler indexing your site

8 Caveat 3: Not All Visitors Count  Visitors you want to count  Human beings who do things on your site  Visitors you want to exclude  Human beings who build your site (developers, testers, etc.)  Non-human beings - bots, crawlers, etc. who are indexing your site  Different methods for excluding visitors  IP address blocking, cookies, etc.

9 Caveat 4: Traffic Varies Over Time  Regular (daily, weekly, monthly, annual) and irregular fluctuations  Long-term trends require reliable longitudinal baseline statistics that smooth out these fluctuations  Minimum of 1 year’s stats required  NSDL not there yet - still building inter- project agreement on baseline measurements

10 Example 1: Daily Fluctuations

11 Morning

12 Example 1: Daily Fluctuations Evening

13 Example 1: Daily Fluctuations

14 East coastWest coast

15 Example 2: Monthly Fluctuations

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17 Weekend

18 Example 2: Monthly Fluctuations

19 Thanksgiving

20 Caveat 5: Different Tools Count Traffic in Different Ways  Differences (and their criteria) are obscure  Incorrect analogy for metrics: utilitie bill  2 people + same use = same bill  More correct analogy: cell phone bill  2 people + same use + different plans = different bills  Difficult to map/standardize across tools

21 NSDL Webmetrics  212 NSDL projects funded since 2000  Projects use different webmetrics tools  Lack of standardized NSDL webmetrics  Makes cross-site comparisons difficult  Makes strategic NSDL planning difficult  Solution: third party metrics contracted from Omniture (omniture.com)  nsdl.org, Pathways projects, and DLESE  ~$20,000 p.a.

22 Omniture  Omniture measures site traffic remotely, using javascript and cookies  Omniture standardizes metrics across all monitored sites  Data and analyses available in a browser  Each site sees only their own metrics  All metrics are viewable in central CI account  Sites can still implement their own server metrics

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29 Current Omniture Status  Omniture stats are lower (more rigorous) than projects’ own server-based web metrics  Bots/crawlers excluded  March 06: nsdl.org  Omniture: ~14k/month, ~170k/year  c.f. AWStats:~28k/month, ~350k/year  March 06: nsdl.org + Pathways + DLESE  Omniture:~110k/month, ~1.3m/year

30 Future Directions  Refine our Omniture ‘menu’  Implement cross-project tracking  E.g. track visitors from nsdl.org to Pathways  Develop NSDL tools to overlay Omniture data  Compare use across different projects  Develop a ‘task-centric’ model of webmetrics  Redefine unit of analysis from ‘visit’ to ‘task’  Can we identify typical educator task profiles from webmetrics?


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