Personalized Learning
For a long time, we have been learning the same way. Students have been pushed into a rigid structure since the beginning of formal education. While technologies have advanced greatly giving education great potential, they haven’t been effectively integrated into education until very recently. Personalized learning can transform how we learn and provide a robust alternative to the rigid structure we have today. The forced shift to virtual learning due to COVID 19. has provided an impetus to students, schools, and teachers to embrace technology that can support personalized learning.
What does Personalized learning look like?
Personalized learning could be enabled in multiple ways for students. First, it could allow for different starting levels and create personalized pathways. For example, a student may have a knowledge gap in a certain basic topic so it could identify the gap and work from there to build up. The second is through the type of learning and resources that a student prefers. Some prefer synchronized while others prefer asynchronous learning. Some students prefer live lectures while others learn better from watching a video or using animations. The third is the pace of learning. A certain course could be finished in two months or in four months depending on the student. Personalized learning services would ideally combine the three to offer the best individual experience.
In terms of methodology, schools could partner with technology companies to offer personalized learning. Students initially would have to take a test to determine their proficiency and starting level. Companies such and MindSpark and DreamBox offer lessons that they have created in the form of animation and games in addition to regular practice. These services are adaptive to different starting levels and learning speeds and provide support for personalized pathways. There are some services like Gooru that offer different types of resources as well by recommending content from open-source platforms, thereby allowing for more flexibility in the type of learning. These services collect and analyze lots of data and can be used by teachers to learn more about their students.
What are the benefits of Personalized learning?
In a much-needed change for the “ one-size-fits-all” method of education, Personalized learning allows for individuality. Personalized learning allows students to learn at different speeds and tailors lessons based on the student's proficiency in the subject and also allows students to take more advanced courses based on their interests. In schools where students have been seen to be below their grade level, it has the potential to help the catch-up
Personalized learning programs have been shown to greatly increase students learning. Mindspark uses AI to create a personalized plan for each student based on their proficiency which is assessed using a test. The Abdul Latif Jameel Poverty Action Lab (J-PAL) conducted a third-party study of MindSpark in 40 different schools in Rajasthan, India, using Randomised Control tests. Before the use of MindSpark, the students were several grade levels behind. After two years of using MindSpark most had advanced many grade levels and caught up. Students who used the service also had twice as much learning value as the control group who didn’t. Karthik Muralidharan, Co-chair of JPAl stated “The reason why this is so effective is that you are getting complete customization in a setting where the vast majority of children are so far behind the textbook and the syllabus that is taught in their class”.
The role of technology in personalized learning
Personalized learning will not be possible at scale without the use of technology. Technology has two main uses in personalized learning. One is in collecting the data about each individual student and cross-referencing it to provide suitable resources. The second is in the creation of the content itself and for giving a large number of users access to the content. The idea behind most Personalized learning services is to use AI and Machine Learning to collect real-time data to constantly tailor resources to meet the needs of the students. For example, DreamBox uses an Intelligent Adaptive Learning technology to collect data as students solve problems and immediately make changes in the difficulty and style of the questions. Gooru uses a model similar to the one used by search engines like Google and Netflix to give course recommendations. They use a search algorithm called Navigator to collect metadata about learning habits to then predict the best type of resource. They then help provide teachers with data to create online playlists for their students. Most Personalised Learning services use technology to approximately place students on the map with their preferred learning style and starting level. This concept has drawn parallels to Google maps locating you exactly on the map, but with a lot more parameters than latitude and longitude. The availability of AI and Machine learning has and will greatly change the possibilities of education.
Bureau, BW. “Mindspark In Rajasthan: Personalized Adaptive Learning Tools To Improve Learning Outcomes”. BW Businessworld, 2020, http://www.businessworld.in/article/Mindspark-In-Rajasthan-Personalized-Adaptive-Learning-Tools-To-Improve-Learning-Outcomes/01-11-2019-178372/
“Dreambox Learning — Online Math Learning For Students, K-8”. Dreambox Learning, 2020, https://www.dreambox.com/.
“Mindspark”. Mindspark.In, 2020,
https://mindspark.in/#about_us.
Shapiro, Jordan. “This Guy Left Google To Put The Power Of Big Data Into Small Classrooms”. Forbes, 2020, https://www.forbes.com/sites/jordanshapiro/2013/08/13/this-guy-left-google-to-put-the-power-of-big-data-into-small-classrooms/#66fb27e12595.
“Welcome”. Gooru.Org, 2020,
