Analytics Technologies

According to the Horizon Report K-12 Edition, analytics technologies will be emerging as a critical tool in education in the next 2-3 years (2017).

Analytics technologies are data tools or systems that gather, store, interpret, and display large quantities of information.

Key Concepts

• Big Data
• Track/Store/Use
• Visually Display
• Data Mining
• Data Analyzing & Interpreting
• Data Tools
• Data Training

Overview

• Need for gathering, storing, and interpreting large amounts of data is increasing.
• This is specifically useful in education as it helps track student growth, teacher performance, and personalize learning.
• Need for more data tools and the skills to interpret those tools is becoming more and more important.

Related Research

• Why is big data so critical?
• Volume
• Velocity
• Value
• Verification
• Variety
• Veracity
• (Daniel, 2015, p. 908).

What this means in the classroom..?

• We can use data to personalize instruction, provide feedback, engage students, increase achievement, and better our own instruction.
• Furthermore, we can teach students how to utilize data in a relevant and meaningful way.

What this means for others..?

• Administrators can make more informed decisions about curriculum, allocation of resources, and support or professional development needed.
• Counselors can also utilize data in the advisement process to help students and parents make better decisions about college or career pathways.

What this means for students...?

• Students benefit from personalized instruction and data collection and interpretation skills that better prepare them for college or career success.

Challenges

• Cost
• Time Consuming
• Complex
• Unreliable data due to lack of standardized measures
• Lack of personnel to interpret and analyze data successfully
• Lack of teachers with skills to instruct students how to collect and use data.
• (Daniel, 2015).

At our school....

• Adopt a data program school wide (all in learning, google forms, excel surveys, quia, etc..?)
• Train staff on program
• Use data in our weekly PLCs to make informed decisions about instruction, assessments, and achievement.
• Include data interpretation and analysis skills in curriculum

References

• Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904-920. doi:10.1111/bjet.12230

References

• De Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., & ... Arnab, S. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6), 1175-1188. doi:10.1111/bjet.12212

References

• Erwin Jr., R. W. (2015). Data Literacy: Real-World Learning Through Problem-Solving With Data Sets. American Secondary Education, 43(2), 18-26.
• NMC/CoSN Horizon Report Preview. (2017). K-12 Ed. Retrieved from: https://cdn.nmc.org/media/2017-nmc-cosn-horizon-report-k12-preview.pdf