Learning Analytics: A Current Trend in Educational Technology by Linette Rasheed
The notion that technology is constantly changing is a well-known fact. For teaching and learning this concept is shaping the landscape in classrooms across the globe. From hardware to software, trends in emerging technology roll out so quickly, oftentimes pedagogy and instructional design lag behind. From people services to technical services, resources to support many emerging and emergent technologies at colleges and universities fall short too. The charge which all learning environments face, from traditional (brick and mortar) to blended and online environments, is implementing a strategic plan to accommodate this shift. Beyond shoring up infrastructure to meet the rapidly changing landscape of technology is learning more about learners’ behaviors through technology in order to improve services.
Current technology shaping higher education
One such trend that has emerged in the last few years shaping productivity in higher education across disciplines is Learning Analytics (LA). Learning Analytics as defined by the LA community “is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” (LAK, 2011). Data is collected and analyzed using a variety of methods, in order for instructors to gain an understanding of students’ academic behavior and performance. As students engage with learning materials, collaborate with their peers, and interact with their instructors, LA software and tools to collect data is used to make changes at the course level as well as institution level. Increasing student retention and grades for all stakeholders—learners, instructors, and the community is the intended outcome.
Challenges associated with this technology
There are, however, challenges associated with Learning Analytics. One such challenge is cost. Resources must be allocated for storing data. There are also costs associated with hiring employees to manage the system. Once staffing is in place, costs for ongoing training can continue. Another challenge to LA once data are collected is deciding what should be analyzed. Associated with this challenge is creating a unified, illustration—a picture that represents the data. Among the most challenging concerns are issues that stem from privacy and ethics. Embedded in this notion are questions such as: How is LA information used? How is LA information safeguarded?
Societal needs and benefits of this technology
On the other hand, Learning Analytics meets the societal needs of students by delivering personalized learning. In education this need oftentimes go unmet and students sometimes fall through the crack. From data gleaned through Learning Analytics, a focused learning path can be tailored for each student. Functioning much like a Center for Student Success, this technology can build a profile for each student, which then can be used to determine if a student needs to be referred to a counselor for help. Another benefit of LA is data can be used as a predictor of when a student is at risk of not completing a course. Assessing students’ strengths and weaknesses based on performance is possible. From this information, LA can be used to determine when a student has grasped the knowledge needed in one content area in order to move to another. More importantly, data compiled from LA can be an indicator of a student’s potential grade. One of the greatest benefits this enables is for intervention strategies to be put in place when needed.
Pitfalls of this technology
With all technology there are unanticipated consequences. For Learning Analytics, these pitfalls can be magnified when decisions based on data fail to produce positive results. One pitfall associated with LA can better be phrased as questions. Does the data collected represent a clear picture of student performance and behaviors when other elements (environmental and personal) used in measuring student success and achievement are not factored in? Another pitfall associated with LA is the potential breech of private information. How is data protected? Institutions must assure students that data collected through LA is safeguarded and used only for its intended purpose—to deliver better instruction. Protecting privacy is no small feat considering even our nation’s top branches of government fail to secure information. Moreover, there are no certainties with even the most advanced firewalls. However, one measure to make LA impenetrable to the pitfall of private information being hacked or leaked is for student information to be house serviced by a clearinghouse rather than the campus network. Pitfalls associated with clear representation of data collection and analyses may best be minimized by utilizing multiple analytical tools and instruments. The pitfalls identified are by no means exhaustive; however, they represent valid concerns integral to learner’s everyday life.
As a futurist, I forsee Learning Analytics restructuring the role students take on in their own learning. Student achievement is linked to student success. Student success, I believe, lies in student empowerment. Learning Analytics has the capacity to empower students just as Constructivism empowers students because they are actively involved in their own learning. They are the designer and meaning maker of knowledge rather than a passive receiver of knowledge. In the same vein, students can use the experiences and information gleaned through Learning Analytics to reflect on their academic progress, assess it, and determine how to move forward. When students are able to reflect on their achievements and patterns of behavior it enables them to navigate their own course. Learning Analytics is merely one vehicle.
For more information on Learning Analytics, visit:
NMC Horizon Report 2016 Higher Education Wiki at http://www.horizon.wiki.nmc.org or
Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics
1st International Conference on Learning Analytics and Knowledge LAK 2011