Purpose

The purpose of Learning Analytics, simply put, is to improve the quality of education. By analyzing students' preferences, behavior, and assessment results, teachers will have an easier time differentiating instruction to determine what their students need. 

Effective teachers will provide feedback to their students through formative assessments, but this process can be time-consuming. Teachers can speak to students individually, but depending on class size and student needs, this approach may not be practical. Another common option is for teachers to provide written feedback, but writing detailed commentary to each student can take hours, meaning students may not receive the feedback in a timely manner. On a larger scale, administrators often rely on data from previous school years to analyze their graduation rates or rates of student promotion by grade. This data does not allow administrators to adjust programs or policies based on the students currently in their buildings.

Learning analytics is a way for teachers and administrators to give and receive instant feedback, using technology as a tool.

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In the following video, Steve Schoettler, the founder and CEO of a learning analytics company, describes what learning analytics is and how it can improve education in the United States.


One important point he mentions is that an issue in the American education system is that data is not used efficiently. Most of the time, the only data that is collected comes from standardized tests, and that data paints a limited picture of learners. Not all students learn the same way, so limiting data about student achievement to a single test prevents teachers from giving effective feedback. Learning Analytics can provide immediate feedback to students as they complete work. According to Andrianes Pinantoan from Edudemic.com, Learning Analytics programs can track students online behavior and use that behavior to provide immediate help. He explains, "if a student spends significantly less time attempting to solve a problem compared to other students, the system can display prompts and clues to keep him/her going – in real time." The struggling student does not need to wait for someone to provide help, and therefore, is less likely to become frustrated and quit.

Using online programs, students can be provided with what they specifically need, meaning struggling students can receive support while advanced students can be provided with more in-depth activities. Teachers can also decide if other information is relevant - for example, if they discover that certain students are taking little time to complete online quizzes and subsequently failing them, they can determine how to proceed.

In their report "Penetrating the Fog: Analytics in Learning and Education," Professors Siemens and Long provide a list of ways that Learning Analytics can help in higher education. Though they specifically discuss the benefits for colleges and universities, some of the items can potentially apply to primary or secondary schools as well. Their list reads as follows:


How do big data and analytics generate value for higher education?
  1. They can improve administrative decision-making and organizational resource allocation.

  2. They can identify at-risk learners and provide intervention to assist learners in achieving success. By analyzing discussion messages posted, assignments completed, and messages read in LMSs such as Moodle and Desire2Learn, educators can identify students who are at risk of dropping out.

  3. They can create, through transparent data and analysis, a shared understanding of the institution’s successes and challenges.

  4. They can innovate and transform the college/university system, as well as academic models and pedagogical approaches.

  5. They can assist in making sense of complex topics through the combination of social networks and technical and information networks: that is, algorithms can recognize and provide insight into data and at-risk challenges.

  6. They can help leaders transition to holistic decision-making through analyses of what-if scenarios and experimentation to explore how various elements within a complex discipline (e.g., retaining students, reducing costs) connect and to explore the impact of changing core elements.

  7. They can increase organizational productivity and effectiveness by providing up- to-date information and allowing rapid response to challenges.

  8. They can help institutional leaders determine the hard (e.g., patents, research) and soft (e.g., reputation, profile, quality of teaching) value generated by faculty activity.

  9. They can provide learners with insight into their own learning habits and can give recommendations for improvement. Learning-facing analytics, such as the University of Maryland, Baltimore County (UMBC) Check My Activity tool, allows learners to “compare their own activity . . . against an anonymous summary of their course peers.”
To read the report in its entirety, click here.

Because Learning Analytics - and Big Data in general - are fairly new concepts, there are many questions that have arisen about their use in education and other fields. The following infographic discusses the definition of Learning Analyitcs and addresses a number of questions.


One of the major concerns about Learning Analytics involves privacy. Companies like Google and Facebook have already come under fire for tracking and using user data for profit; Learning Analytics may experience similar pushback. Learners may feel intimidated knowing that their online behavior is being tracked. On another note, it may be challenging to incorporate Learning Analytics into elementary and secondary classrooms if students lack the necessary technology. Still, if the overall goal of learning analytics is to help both individual instructors and institutions as a whole better serve their students, answers to these questions to be found.

To learn more about the history of learning analytics, click here.