Ensuring learning products are intelligently developed.
GUEST COLUMN | by Anand Subramanian
New models of learning exist today thanks to technology, and more-and-more educators are willing to spend to take advantage of technology-enabled teaching methods that facilitate better student outcomes. Validation of this is Gartner’s prediction that technology spending for higher education alone will exceed $38.2 billion in the U.S. this year. When designing digital products and solutions for any industry, it’s critical to always keep the end-user experience in mind. In addition to understanding the user’s needs, wants, and pains via traditional research and due-diligence, data collection and analysis are
Various data crunching and analytics techniques will become more prevalent to make learning products even more adaptive to learner preferences, while working in cohesion with teacher/student interactions.
key components, particularly in the education sector. During the digital learning experience, data from students, teachers and even administrators can be captured. This data can then be used to support decisions both on the fly and long-term by tracking changes, exploring new patterns in learner behavior, and digging deeper into problem areas.
Below is an in-depth look at personalization and simulation, two developing learning models, and how technology and big data are driving their success.
With personalization, learning is tailored based on individual strengths, needs and interests. Typical tools utilized include supplemental content (video, audio, etc.), assessments with real-time or periodic feedback, dynamic learning tools (e.g., gamification), and efficient information-sharing across practices and schools which frees up time for more one-on-one student/teacher interaction.
With each interaction that happens on a learning platform comes a data point that can be used to personalize further experiences. Data provides visibility into a student’s engagement with material and enables real-time, dynamic adjustment in the way content is served to the student based on his/her responses, preferences, pauses, behaviors, etc. Data also offers insight into learning styles – what’s working and what’s not. This knowledge is valuable to teachers and helps them optimize their methods for engaging learners in the most effective ways.
To articulate the power of personalization further, consider the example of a history class that covers a vast set of subjects. When a learner chooses specific areas of interest, and chooses how he/she wants to “experience” the content, data is collected. As the class progresses throughout the school year, a successful solution can feed that learner more information in a way that meets his/her preferences; for example, serving audios if the learner has typically chosen to consume content in audio format versus written material. The preferences can also be translated to other subject areas, so, for example, the science department might get insights into what’s working for a given student in another class and apply some of those same tools to personalize the science experience.
As this model and technology continues to mature, we’ll likely see that in addition to students receiving personalized information throughout the learning experience, that information will eventually be pushed to the learner outside the physical or virtual classroom, including notifications about new, relevant material via email or mobile device—encouraging additional consumption of paid and non-paid content.
Simulations provide environments that a student might encounter in a future educational or professional setting – with accuracy. They amplify real experiences and are “immersive.” The model typically includes engagement via 3D/virtual reality, gaming and like technologies.
Consistent with personalization, with each decision that occurs during a simulation comes a data point that is used to enhance the learning method. Simulations are usually built around very specific learning situations. An example from a college business course could focus on the role of a Chief Operations Officer and the need to find an answer to a transportation/delivery problem. In this scenario, the educator would build a simulation to teach how to be the “fastest and the first” to deliver to the customer, which might be the company’s mission statement. The route to reach the customer should be the fastest during the point in time when the deliveries need to happen.
The next phase of the simulation includes a “shock.” Maybe there’s construction happening or roads are closed because of an accident and usual routes aren’t possible. The learner needs to figure out a solution – how can I make the deliveries the fastest without costing the company more money? Each decision becomes a data point and can suggest subsequent “shocks” along the chosen path that the learner must navigate.
This augmented reality, coupled with an involving user experience, can be a very powerful learning experience for educators and students. And as technology matures, it will likely become more prevalent in all educational settings, while also thriving as a training method for many industries.
An Essential Component
It’s an exciting time in education with technology enabling so many new ways of learning. Various data crunching and analytics techniques will become more prevalent to make learning products even more adaptive to learner preferences, while working in cohesion with teacher/student interactions. Ensuring that the right analysis tools and capabilities are built into products and solutions is essential to advancing teaching methods and student results.
Anand Subramanian is Technology Innovation Center Head at Ness Software Engineering Services (SES). With over 20 years of product engineering experience, including specialization in education, publishing, and media, he helps clients conceptualize, develop, and deliver large, complex, and commercially viable products.
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