There is a developing focus on measuring learning, which has accelerated the adoption of analytics technologies. Analytics technologies provide various tools and applications used to convert data into useful information (Perkins, 2015). People are developing ways of collecting, connecting, combining, and interpreting student progress and capabilities. Measuring student learning has evolved from using metrics such as semester grades and graduation rates to real-time, interactive metrics that provide information on meeting students’ needs and the suitability of the learning curriculum. Analytics technologies are now used to track student performance, behaviors, interests, and activities to better understand their learning experience. These technologies help institutions migrate from information-poor decision-making to data-driven, research-based, and information-rich decision-making.
An emerging trend
in education is authentic learning, where students are provided with real-world
problems and are required to provide practical solutions. Authentic learning is
being adopted to enable students to gain practical learning skills. Schools are
now connecting academic knowledge to practical applications by establishing
relationships with the community. By partnering with local organizations,
students can experience the real world and develop essential skills valuable
for the real-world environment. Students are now participating in internship
programs, apprenticeships, and community projects (NMC, 2017).
There are various
factors affecting analytics technologies. Ethical and privacy issues are a big
challenge for analytics technologies. Ethics and privacy influence how
applications and data are stored in cloud-based systems. These ethics and
privacy complexities discourage some institutions from implementing learning
analytics. These considerations include privacy considerations such as
maintaining anonymity, obtaining consent, and data sharing. Technical
challenges are also present when assimilating data analysis due to the data
format provided. Erroneous data can compromise the findings, thus generating
inaccurate results of the general population. During manual data analysis,
erroneous information can easily be identified from the population. Thus, using
erroneous data in learning analysis leads to a big margin of error on the
overall results.
Authentic learning
also faces several challenges. Societal challenges are a barrier to authentic
learning; many people have not embraced the idea. It is much easier to compile
student test scores and compare them for successes and failures. Parents like it
when great schools are defined by their test scores. Authentic learning can
sometimes be tedious. Authentic learning takes time and requires professionals,
thus being costly. Cultural challenges also impact the adoption of authentic
learning. Many educators are used to traditional learning and find authentic
learning a difficult shift. Educators face challenges during implementation and
lose control over the learning process and student performance. Authentic
learning involves handling mistakes, revision, and reflection. Authentic
learning is a long-term process that requires patience, tuning, alignment,
observation, and reflection.
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References
Perkins, D. (2015). What Are The Barriers to More Authentic Teaching and Learning? [Website]. TeachThought PD.
https://www.teachthought.com/pedagogy/authentic-learning/
NMC. (2017). NMC/CoSN Horizon Report: 2017 K–12 Edition. https://library.educause.edu/~/media/files/library/2017/11/2017hrk12EN.pdf
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