What are learning analytics and why should they be used?

What are learning analytics and why should they be used?
Analytics
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The use of data in education is not new.  However, advances in technology in terms of data collection, analysis, and visualization have created an environment where the questions that data can answer are more specific, more timely, and more tailored to the needs of individual stakeholders.  By being able to see the key information at the right time, those involved with a student, including the student themselves, can be informed about their status and the steps needed to help them succeed.

Learning Analytics: Definition

“Learning analytics refers to the collection and analysis of data about learners and their environments for the purpose of understanding and improving learning outcomes.”  (Northeastern University, 2020)  Education institutions use learning analytics to understand, share, and even predict, more about student learning, the environment and how to make decisions that can optimize teaching and learning, to promote equity and academic success.

What does that really mean?

Simply stated, it is the process of using analytic tools, in combination with the expertise of teachers, curriculum leaders, data coaches, and administrators to extract the insights about student performance, including social emotive learning (SEL), that are generated daily in classrooms and in online learning.  Those insights should drive the process of examining, and if needed, changing the teaching, learning, and environments.

Is it just reporting?
Quite frequently, learning analytics are lumped in with the simpler education data reporting.  For example, a simple chart showing student performance on a state assessment is a report.  However, a visualization of whether it is a small set of students located in a particular school or classroom, versus a widely spread group of students across the school or district is the type of analysis that can help decision makers understand if an issue with student performance is because of a need for professional development, or because the curriculum does not sufficiently address a portion of the evaluation.

Another example would be providing teachers with high-level notifications of students’ needs at the very beginning of the year.  For example, a detailed notification that shows past learning skills or standards in which the students have not shown mastery, along with the possible impact those might have in the current year’s learning.  On its own, or combined with a schoolwide screen, such as those used in Multi-Tiered Support Systems (MTSS), it can highlight key information about the students’ learning experience and suggest additional interventions such as the use of educational technology tools.

How Does It Work?

Much like teaching, learning analytics is a lot of science, and a bit of “magic.”  A well-designed analysis will consider a variety of factors to uncover the insights about what it is that helps a student succeed.

It begins with the data already created, in student information systems (SIS), learning management systems (LMS), various online learning resources, and assessments, whether national, state, or local.  Those data can be further supplemented with information about SEL and school environments to provide a more holistic view of individual students, or groups.

Using the types of data analytics originally developed in other industries, learning analytics in education use technology to introduce teachers to their incoming students, identify patterns that highlight instructional needs, SEL concerns, monitor student progress, and predict likely outcomes such as student dropout.  Those insights can be provided either as individual feedback to students, parents/guardians and teachers, or in aggregate to teachers, school and district staff.

The benefits of learning analytics in education

All education stakeholders can benefit from the use of learning analytics.  However, different groups benefit from different flavors of analysis, tailored to their role in students’ learning.  The analysis provided to each group must meet their specific information needs.

For district administration

Charged with the success of students across the entire district, administrators have to balance the needs of a variety of students in different age groups, from different socio-economic backgrounds, with different social emotive needs, and different educational backgrounds.  Learning analytics help those administrators by surfacing insights such as:

  • Tracking and predicting changing enrollment, or the composition of their student body
  • Adherence to, and efficacy of online learning education technology usage
  • Evaluating programs
  • Outcome predictions, with suggestions about interventions to ensure student success
  • Supporting staffing/funding decisions, such as additional English Language Learner staff/resources with an expanding ELL student population.

For school administration

Similar to their district counterparts, school administrators face a variety of challenges.  Learning analytics can answer questions about the overall status of the school, as well as student and staff performance.  Some examples include:

  • Clarifying the impact of new policies, programs, or resources on student outcomes
  • Responding to parent/staff/learner surveys about school environments
  • Pinpointing insights that are only apparent by combining different data together, such as transportation information and its impact on student attendance
  • Predictive notifications about student outcomes about achievement, proficiency, and graduation.

For other staff including curriculum leaders, data coaches, and guidance counselors

A variety of school staff also benefit from analytics.  Curriculum coaches can identify areas where their curriculum is meeting needs, as well as pinpoint the source of areas for improvement.  Data coaches can use well-designed analyses to provide both feedback to teachers and administrators on student learning, as well as increase the frequency and that feedback, so that it helps build a culture of data usage. Guidance counselors can have a much more holistic view of the students, so that their guidance is more apt to bring about the desired results.  Examples for these stakeholders include:

  • Identifying if a need is widely spread, and might point to a curriculum need, or confined, which might benefit from professional development
  • Helping data coaches provide teachers with answers to “How are my students doing?” and “How do I help my students succeed?”
  • Providing guidance counselors with SEL data to supplement their understanding of students’ needs.

For teachers

Teachers are in many ways the personification of an analytics tool, and the group that benefit the most from learning analytics.  On a daily basis, they process large amounts of data about their students, from the academic, to the personal, to the behavioral and more.  However, with the explosion of the use of blended learning and online learning, the additional data is harder to access as it sits siloed in various systems, requiring time that would be best used to prepare for student success.  An analytics tool provides several benefits to teachers:

  • Answering the two most basic questions, “How are my students doing?” and “How do I help my students succeed?”
  • Extracting of the most important information about their students from a variety of systems, allowing them to evaluate additional data while saving time
  • Helping teachers cut through the clutter of data, by notifying teachers of high-value suggestions for actions to monitor and promote student progress and achievement
  • Consolidating information about student performance from various tools and platforms in a simple view
  • Analyzing individual student performance on its own, or in context of a group, to identify specific areas of needs in terms of skills, standards, and competencies needed by one, a few, or most students
  • Ability to combine progress data with other factors, such as SEL, to support additional feedback, e.g., providing a “pat on the back” to students who improved in some measure of performance and are motivated by praise.

For parents/guardians

“Parental involvement and engagement in education matters now more than ever because it’s in decline.” (Waterford, 2018)  

While a number of factors, including societal changes, are behind that trend, providing parents/guardians with clear, succinct information about their learners can go a long way towards keeping them engaged with the students’ learning process.  Good analytics tools will extract the most important information and make it readily available, so that parents/guardians do not have to wade through mountains of data to arrive at a clear understanding of their learners’ status and needs.  For parents/guardians, learning analytics should answer the following questions:

  1. How is my student doing?
  2. Where are they headed?
  3. What do they need to do to succeed?

For students

“[A]cademic feedback is more strongly and consistently related to achievement than any other teaching behaviour[sic]…this relationship is consistent regardless of grade, socioeconomic status, race, or school setting.” (Reading, Bellon et al, 1991)

Not unlike their teachers, or parents/guardians, students are awash in data.  From teacher feedback, to online learning resources, to assessments, and from their environments.  Helping guide them towards self-understanding, as well as how to process the feedback they are receiving so that they can identify their needs and successes is critical in helping them prioritize and succeed. Learning analytics for students should succinctly answer the following:

  1. How am I doing?
  2. What do I need to succeed?

AnalyticVue and Learning Analytics 

AnalyticVue responds to the learning analytics needs of all of the above stakeholder types.  It includes and exceeds the traditional education data reporting by providing the types of analyses that generate higher-level questions about how to help students succeed.

Its analysis of academic performance uncovers underlying issues whether pertaining to learning environments, individual student needs, or curricula.  It offers clear feedback to parents/guardians and students themselves.  It builds on the work traditionally done by teachers, administrators, curriculum leads, and data coaches in collating and analyzing data to inform instruction and student interventions.  It creates those analyses automatically, as soon as the underlying data changes so that our partner districts can take advantage of these features to conduct data meetings on a regular basis, and to make data use part of their daily culture.

We are able to do this because of our decades of experience in examining, consolidating, and analyzing education data, and, just as importantly, our decades of listening to and partnering with education entities to understand their needs in terms of data analytics to optimize their students’ learning experience, environment, and outcomes.