Tech Trends-Learning Analytics
Learning Analytics is a technology trend that, according to the 2013 NMH Horizon Report will be a part of the mainstream K-12 education community in two-three years. The report is suggesting that this trend will have a 20% penetration point in the community by 2016. I believe it will be at a higher penetration point.
Did you ever see the movie Minority Report? The story goes that sometime in the future, using 3 telepathic people of great potential, that a database can predict violent crimes before they happen. There is an interface between the database and the telepathics. The police are tasked with apprehending the perpetrator before the event happens.
Learning Analytics is a possible forerunner to this storyline. It is the idea of cataloging as much information about a student as possible: grades, behaviors, interactions on computer, interactions with classmates and teachers, family history, medical history, attendance, learning styles, extra-curricular activities, etc. Once this is cataloged in a communal database, the data can be assessed by a teacher or administrator to assist the student with decisions on courses, career opportunities, how to be a better student, and graduating high school or university. The idea of this trend is to have a database that is nationwide, possibly world-wide, so that the information can be extracted by any school system that a student attends. This has many implications that will not be addressed in this short paper. The data can also predict possible outcomes for individual students and groups of students. These prediction can be used with course creation, student retention, and even behavior modification.
Using learning analytics with learning platforms can assist students struggling with skills and concepts in a particular class, as well as support advanced student learning. It will also support the teacher and administration with better opportunities to support the students through regular reports and suggestions. Below is an example of a learning analytic feedback system.
The Components and Data Flow Through a Typical Adaptive Learning System1
Relevance to Elementary Education
Title One schoolchildren tend to have disadvantages. One of these disadvantages is a higher rate of below level reading ability. Once a child is “behind” in reading, without appropriate intervention, that child usually stays behind in reading. This disadvantage is set by the time a student is in 4th grade. Learning platforms that are “fine-grained” can offset this disadvantage. “Fine-grained” means a learning platform that assesses a student’s skillset and adapts the program to help the student be successful. The more fine-grained a platform is the better data can be used to help the student.
Learning analytics is reaching this adaptive stage in math concepts. The Khan Academy is a good example of platform that has some “fine-graining”. This free online program now has a math pre-test to help a learner start their program – after the quick test, the program then makes suggestions which skills should be practiced first. When practicing the skills, it offers a video to help with the skill, hints if requested, and lets a learner know immediately if they have the correct answer for the problem. It has incentives such as scores and badges to keep a learner engaged.
This type of platform can be used in Title One schools to augment live teaching. If used 2-3 times a week it will reinforce the math skills that a teacher introduces. The Khan Academy offers reports to the teacher on each student, so a teacher will also have the chance to reinforce those problematic skills.
Reading is a more complex skill than say 2-digit addition and so it is taking longer for these learning platforms to be “fine-grained” to the point that they are closing the reading gap with financially disadvantaged students. Again, these platforms will be advantageous if used on a consistent weekly basis.
There are also behavior platforms that are showing promise in helping students self-monitor how they act in a classroom. The platforms also help teachers with tracking good and challenging behavior, keeping track of student and parent communications, and helps administration make disciplinary decisions.
Once the different platforms are connected in a central open database that can be assessed by students, parents, teachers, and administrators; this trend will be even more powerful.
This trend has immense potential to create student-centered learning in schools. The analytics and learning platforms can make it easier to find ways to assist struggling students, focus medial students on subjects of interest, and keep high-performing students engaged. It can make a teacher’s job easier with reports that offer suggestions for each student. It also helps students be responsible for their learning.
The learning platforms and analytics often share the information in real-time with the students to help them understand if they are on-task or running the risk of failure. The student then can make a choice with that knowledge.
This trend also has immense potential to be abused. It can be used to place students in categories according to their data. No student is completely defined by the data of grades, attendance, behavior, etc… It also has the potential to be misused. In the movie Minority Report, the main character gets wrongly accused of a crime that is committed by someone else. The database was manipulated. This can happen with analytics- there can be too little data, the data can be manipulated, and it can be misread. So as with any new trend good professional development will be a key component for it to be successful.
Learning Analytics in the Classroom
The school where I taught for ten years began using Class Dojo school-wide this year. It is a behavior platform. If I were still in the classroom, I would also engage Khan Academy or a similar math platform that students could access on a daily basis to augment math lessons. I would also find a really good reading platform that could be used regularly to assist the reading curriculum.
I would love to have an analytical database that offered an overview of each student’s preferred learning style, grades- where they excel and where they struggle for math, language arts, science, and social studies, what their personal interests are, community background, and attendance from previous years at the beginning of school. This would be helpful with small groups, which learning styles should be used when teaching, and where to start in each subject for each student and the class as a whole.
In conclusion, learning analytics will be a catalyst for education. It has the potential to change the way K-12 education is implemented. There will be a greater trend to using online learning platforms, teachers will have the ability to tailor learning, the struggling student will have the opportunity to be successful in a school setting, while the advance student will be allowed to advance at their pace – not the class pace. Administration and school districts will be able to use metadata to create courses that work in real life and help identify challenged students early enough to assist them to be successful. This trend will affect all aspects of our present education system.
 U.S. Department of Education, Office of Technology. (2012). Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief. Washington, D.C.
 VanLehn, Kurt., The Behavior of Tutoring Systems. Abstract retrieved from http://www.public.asu.edu/~kvanlehn/Stringent/PDF/06IJAIED.pdf