As education technology continues to increase in prominence, effective data analytics has begun to garner attention in the global academic sector. Within education, data analytics is categorized into academic and learning analysis, both of which rely on data collected through education technology.
What is the Difference Between Academic and Learning Analysis?
Academic analysis focuses on larger scope data interpretation, such as differing educational standards across subjects, teachers, or school districts. This type of analysis is beneficial to administrative or governmental bodies seeking to identify points of strength or weakness across a wider audience, such as school districts with notably high or low test scores. Additionally, academic analysis can indicate the likelihood of student dropouts by recognizing trends across a variety of factors such as assignment submission, attendance rates, or library usage. As a result, schools are more capable of providing academic support to at-risk students and increase retention rates.
Conversely, learning analysis pertains to a much smaller spectrum, such as an individual student’s conceptual comprehension of a subject, or a teacher’s success in presenting a new lesson. Learning analytics can measure the length of time required for a student to answer a question or trace patterns of incorrect answers across an exam. Data analytics vendors offer a variety of functions that can gauge how students learn more precisely than relying solely on the observations of a teacher.
Data analytics is currently in its early stages of development across the globe, but the market is expected to grow exponentially within the coming years. Growth is anticipated to concentrate especially within nations with large student demographics and increased educational funding, such as China, India, and Brazil. For example, in 2013, Latin American nations only used 88 LMS but are projected to house 138 by 2016. Similarly, in 2013 Asia only held 446 LMS, but is likely to reach up to 722 within the next two years.
The current benefits and challenges facing data analytics vary greatly across different educational sectors. Due to decreasing budgets paired with increased demand for higher retention rates, institutes of higher education have been prompt in adopting data analytics. Additionally, data platforms allowing for data analysts have grown rapidly as universities have embraced education technology as an integral part of the academic curriculum. However, higher education institutes report a hesitation on the part of many teachers to utilize data analytics. Unwillingness to adopt data analytics typically arises out of mistrust of privacy or reluctance to learn how to operate the new technologies. Furthermore, though a vast majority of higher education institutes in developed nations such as the United States utilize LMS, many universities are displeased with limitations of early LMS providers. Higher education institutes are currently transitioning to LMS 2.0s, which include LMS capable of tracking student engagement as well as learning analytics.
Within the K12 sector, increased pressure to establish assessment standards across school districts has increased the importance of data analytics. Governments, administrations, and NGOs frequently utilize data analytics to locate disparities between standardized test scores of students from different regions. As a result of analysis, educational funding is distributed more appropriately to districts with poorer retention rates or quality of education. That said, the K12 market still remains extremely fragmented, with wealthier districts capable of funding education technology still unobtainable to regions of lower socioeconomics. Furthermore, when it comes to the privacy of data analytics, K12 educators indicate a similar mistrust as their higher education counterparts.
As mentioned above, one of the major obstacles facing data analytics in the education sector is a lack of trust in its privacy and necessity. Teachers, the majority of whom were educated without a heavy reliance on technology, are among the most hesitant demographic to accept education technology and data analytics.
Additionally, in order to rely on data analysis, proper technology must first be established to organize data received. Educational institutes lacking proper funds are likely unable to afford data analysts, even if students, teachers, and administrators support its implementation.
For schools which do possess the capacity to establish data analytics, a new challenge arises. Approximately 90% of patterns observed by analytics are derived solely from data collected. Data analysts can often locate patterns in data that lead to falsely correlated conclusions, and schools must keep this weakness in mind when relying on analyzed patterns.
Data analytics is a relatively new market and therefore still has much room for investment and improvement. Early warning programs to identify and support struggling students have recently become a primary focus for many school districts. While higher education institutes in the United States currently use data analytics to prepare education planning 73% of the time, data analytics is only used to establish early alert systems for at-risk students 46% of the time.
Higher education institutes have requested the production of more comprehensive data analytics. While many education technologies such as Moodle of Blackboard provide data analytics from information collected within their own sources, there remains a lack of communication across multiple technologies. Separately purchased plug-ins are capable of analyzing specific data sets, but within the market there still remains a demand for data analysis of specific data sets across various technologies.
As educational institutes across the globe increasingly depend upon education technology, proper data analytics grows in importance. Educational sectors will likely adopt data analytics as an integral aspect of academics in order to identify trends previously unobservable to teachers and students, support teacher and administrative design of curriculum, and provide individualized education to students.
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