How Business Data Analytics Empowers the Ed-tech Industry
3 Minutes Read
According to a 2021 global CIO survey conducted by Gartner for higher education, 86% of CIOs worldwide anticipate continued demand for new digital products and services. The Covid-19 situation has, of course, made digital learning mandatory. Business data analytics plays a vital role in imparting successful digital or eLearning experiences. Reducing student dropout rates, driving enrolments, customizing course curricula, conducting secure exams online, and enhancing the overall eLearning experience are a few of the ways business data analytics has been powering the ed-tech industry. Incorporating analytics with eLearning leads to a more comprehensive evaluation of learners.
We shall now discuss and learn how data analytics solutions help boost the eLearning experience, thus powering the ed-tech industry.
Predictive modeling refers to a statistical technique used to ascertain the probability of a student performing a desired enrolment behavior. Based on student demographic details, online interactions, and assessment results, virtual profiles are uniquely created. Predictive learning models are developed out of these virtual profiles to generate insights on students in the future. A predictive model may determine students’ probable learning outcomes, depending on their experience with similar profiles from their earlier academic years. Unique data of students may also yield significant effects right away. Through business data analytics, you can identify students struggling or at risk to bring them back on track. Besides, schools and universities can monitor the long-term efficiency of such initiatives to support students and teachers better.
Ed-Tech Analytics Gauges Student Engagement
Ed-tech platforms make it easy and accessible to gather, aggregate, and analyze activities. When card swipes into university buildings and library use no longer remain appropriate markers of student engagement, data analytics services will make it simpler to track how students perform online. The best part is that administrators can monitor their student “touchpoints” basis the time they spend in the virtual learning environment or submit their coursework. Through the power of e-tech analytics, instructors can explore the behaviors of their students, identifying areas of student needs. They can thus help students missing or at risk of falling behind academic benchmarks. As we advance, instructors can employ ed-tech analytics for student engagement to revise content, course curriculum, and course material in the future.
Data Encryption Secures Online Exams
Data Encryption can be defined as the method of securing the data by preventing it from being hacked. It is done through the encryption or addition of binary codes to the current data that only the receiver can decode at his end. Exam data and question banks are stored securely and encrypted, where data encryption restricts unauthorized access to question banks. The entire communication between the exam client and the server is encrypted with a secure mode of communication too. You can avoid result manipulation without valid credentials. Data analytics companies use high-level encryption algorithms to encode data that you can decode quickly, thus guaranteeing the confidentiality of the question papers exchanged between the client and the server. The decoding can be done by authorized personnel only, ensuring maximum security of the examination data.
Adaptive Systems Automate eLearning Content Delivery
Adaptive learning systems dynamically adjust to the kind or level of course content basis the skills or abilities of a student. These systems are created to collect data, analyze, and make decisions to optimize a specific criterion. The aim is to enhance a learner’s performance with instructor as well as automated interventions. Based on context and results, these systems can change their response to inputs. Best data analytics companiesmeasure the efficiency of an adaptive learning system by its ability to improve results by effecting such changes. In customized recommendation through machine learning (ML), a branch of artificial intelligence and computer science, adaptive learning systems use neural network algorithms to achieve these learning objectives:
Create dynamic course structures by gathering data from peer learners.
Make recommendations on course materials and time that should be spent on completing a specific course.
Decide on awards and badges that should be automatically given to the learner when he jumps to the next level.
Offer interventions to instructors based on individual student’s learning progress.
Social Learning Analytics Explores Behavioral Patterns of Educational Social Actors
Social learning analytics use data gathered by the online activities of learners to identify patterns and behaviors within the learning environment signifying effective processes. Online knowledge in discussion forums, wikis, videos, and blogs shared by contributors has increasingly become a key learning resource for corporate learners. You should equip modern learning systems with social analytics tools to execute a powerful learning format. As you map interactions of these learners with social knowledge, you should undergo the phases of search, study, rating, recommendations, and contribution.
Through search, learning may be personalized and targeted to the learners’ curriculum.
The study can be tracked and then automatically tested to monitor learners’ performance, understanding, and retention rate.
Rating and recommendations help users find the right content at the right time to ensure a successful training program.
The contribution may be tracked and rewarded as you complete the entire social learning cycle.
The Future of the Ed-tech Industry
The significance of ed-tech and eLearning has gained pace due to the ongoing wave of the pandemic. Data has played the most prominent role, right from online algorithms to assessment, to measuring results. Simply put, you cannot ignore the relevance of business data analytics in the education sector today. To drive swift innovation in the ed-tech industry, an integrated learning data strategy implemented by data analytics companies will be central to targeting the right change, unlocking institutional efficiency, and delivering student success.