LONDON--(BUSINESS WIRE)--Technavio analysts forecast the higher education learning analytics market in the US to grow at a CAGR of almost 29% during the forecast period, according to their latest report.
The research study covers the present scenario and growth prospects of the higher education learning analytics market in the US for 2017-2021. The market is segmented based on application (learner efficiency, student retention, institutional management, instructional design) and deployment method (on-premises and cloud).
Teachers are increasingly using online instructional technologies, such as teleconferencing, interactive videos, simulations, artificial intelligence, and computer-based learning, with or without traditional methods such as printed textbooks. These technologies have compelled institutions to adopt solutions that further facilitate an online learning environment. Considering the online evaluation system, the formative assessments in that are increasingly becoming an inherent part of the online education system in the US.
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Technavio education research analysts highlight the following three factors that are contributing to the growth of the higher education learning analytics market in the US:
- Increased need to develop competitive strategies
- Emphasis on personalized learning
- Emergence of M-learning
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Increased need to develop competitive strategies
Besides providing advantages to teachers, students, and administrators, learning analytics has also proved its importance to other education stakeholders that include curriculum designers, advisors, education policy makers, and school districts. They are entrusted with responsibilities of taking strategic decisions that will impact institutions in short-term and long-term duration. As such, they possess a higher need of such tools that can assist them with actionable intelligence.
Jhansi Mary, a lead education technology research analyst at Technavio, says, “With respect to the increasing competition levels in the education industry, institutions are expected to achieve global education quality standards by investing in adequate tools and technologies. Institutions have to undertake these steps within their constrained budgets. As such, analytical support that can aid in critical areas of student recruitment, investment strategies, and education marketing will be of crucial help. These insights are helping educators to design competitive strategies while logically supporting their decisions.
Emphasis on personalized learning
In a bid to improve the learning outcomes of students and eventually the quality of education, institutions are looking for better ways to analyze student performance. Individual learners follow different learning methods at a different pace. For instance, some of them are visual learners while others can be engaged by having hands-on experience on a particular concept.
“If teachers can ascertain ways through which students are grasping concepts, it will be possible for them to assist students on an individual level and this is not attainable under the traditional education system. This is how learning analytics is helping educators and observing penetration at a rapid pace. These tools are used to capture data on varied parameters of student learning and performance which is then consolidated to extract meaningful observations and trends,” adds Jhansi.
Emergence of M-learning
Mobile devices have become a pervasive form of imparting education to students in the US. Concerning BYOD policies, students are either offered institutional iPads, e-readers, and laptops or are asked to bring their own devices. Apart from students, the faculty, administrative staff, and senior management have also automated their work with the help of these devices.
Observing the surge in the fundamental role played by these devices in accessing education services, providers of learning analytics have introduced analytical tools that can capture data from mobile devices as well. The learners interact via games, videos, and apps and produce a huge amount of data trails that can be monitored and analyzed to interpret the student's behavior and learning quotient.
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