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Web analytics involves the collection and analysis of data generated by interaction with a website or a Web-based application. Web analytics can be used to enhance the performance of the site or application and to inform an organization of use and impact.

Web analytics have been used to investigate users’ characteristics and interaction patterns. Web analytics can be used to predict which products might be of interest to specific customers. The analysis of page visits and site usage can help determine needs and focus. Web analytics can also be used in digital forensics to detect security threats and gather evidence for law enforcement. In the academic world, Web analytics can support organizational planning. For example, as more and more library resources and services are moving online, visits to an online catalogue system can aid in determining subscriptions and technical support for library services. The ubiquity of distributed information invites the integration of Web analytics in many sectors. Educational institutions can track traffic flow on websites, calibrate displays, and manage Web-based learning systems.

This entry begins with a discussion of the techniques and tools used for Web analytics. Next, it examines how Web analytics are used in online education. The final section of this entry focuses on the implications of Web analytics.

Techniques and Tools for Web Analytics

Clicks and page requests are the essential components of Web analytics. According to Bernard J. Jansen, Web analytics started in the 1960s with the analysis of Web logs. These Web logs can be categorized as transaction or search logs. Transaction log analysis (TLA) documents and measures direct interactions (e.g., clicks, page views, length of sessions); search log analysis (SLA) tracks searching behaviors (e.g., keywords used and recommendations followed).

Web analytics depends on quantitative data provided by host or third-party servers. Small packets of data called cookies are sent to a user. These cookies gather and send relevant information to a server system. This is known as server-side data collection. However, accurate data collection through this method becomes challenging. Internet service providers assign dynamic IP addresses to computers. User security options may block some or all cookies. Client-side data collection is an alternative. With this approach, the uniqueness, accuracy, and frequency of visits can be captured with snippets or tags written with various programming languages. These tags are usually embedded in the web-page requested by clients and can send relevant data such as length of page load to a server. A hybrid model of using both methods is often recommended, according to Brian Clifton.

As human-computer interactions on the Web have become predominantly omnipresent in daily life, Web analytics also uses qualitative techniques like surveys, interviews, and lab experiments to make more meaningful interpretation and reports. To determine quantitative or qualitative methods for Web analytics, key performance indicators (KPIs) need to be established. For example, a university that adopts a new learning management system (LMS) may experience unusually high demand for chat-based technical support. The objectives of an analysis might include evaluating the Web-based resources and services and assessing the need to adjust the personnel assigned to technical support. The KPIs can include paths used to reach the support page, clicks within the LMS, session length, readability of support materials, visibility of icons, and frequency of chat sessions. Each of these KPIs will then determine how the segmentation of Web data will be configured, collected, analyzed, and reported.

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