BEHAVIORAL

PREDICTIONS

UNIT

Deb DavisScott MigdalskiWilliam Taylor

investigators

A study in Curriculum Minds*

* Note: the play on words from the Television series Criminal Minds is strictly intended to provide educational lightheartedness, leading to a remembrance of the material.

Using Analytics to “Profile” Behaviors for Student Success

• The Crime: Ignorance• The Evidence: • Reduced test scores• Unhappy teachers• Remediation at college level

• The Victims: our students• The Unknown Subject (UnSub):• The teaching method that suits that student!

As we Quest Onward, Remember . . .

•Education is the most powerful weapon you can use to change the world.

- Nelson Mandela

The story line

•How can the teacher know?

• Education + Experience + Instinct

•A vignette of personal reflection

Descriptive?

Data

Diagnostic?

Assignments

Predictive?

Future likelihood

Prescriptive?

Change the future

Learning Analytics Defined

• “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising [sic] learning and the environments in which it occurs”(Scheffel, Drachsler, Stoyanov, & Specht, 2014, p. 117)• In other words, the more we learn about our

students, the better we can aid them in learning.• The cycle of learning analytics allows for the

data to be compiled and analyzed to direct intervention for learners.

Learning Analytics• By funneling in elements of

descriptive data from prior actions and traits, educators can diagnose issues and thus predict the pitfalls students may face and prescriptively redirect those students. • How does it happen? • What does it take? • How does it work? • What will it do?

Big Data

What is Big Data?

• Laymans terms - Big data is just a vastly large amount of data that cannot be analyzed at one time.

• Big Data is “state-of-the-art techniques and technologies to catch, collection, allocate, accomplish and explore petabyte- or larger-sized datasets with high-speed and varied patterns that predictable data management methods are unable to control” (Drigas & Leliopoulos, 2014).

How "Big" is Data?

• Bit (Single Binary Digit) = 1 or 0

• 8 bits = 1 byte

• 1024 byte = 1 Kilobyte

• 1024 Kilobytes = 1 Megabyte

• 1024 Megabytes = 1 Gigabyte

• 1024 Gigabytes = 1 Terabyte

• 1024 Terabytes = 1 Petabyte

To help understand how gigantic Big Data is, look at the infographic on the next slide that explains about Petabytes!

The Data Story

• How did we get to this point?• The buildup of Big Data starts before

the invention of Google in 1998 and before Apple began in 1976 (Barnes, 2013).• Hollerith Tabulating Machine –

allowed the 1890 Census to be complete in about a year (Truesdell, 1965).

The Data Story

• Hollerith’s Tabulation Machine Company merged with Computing-Tabulating-Recording Company in 1911 and became International Business Machines Corporation (IBM) in 1924 (Austrian, 1982).

• Tesla predicts pocket computers in 1926 (Kennedy, 1926)

• Pfleumer creates magnetic tape in 1928 (Weiss, 2000)

The Data Story• In 1944, Rider predicts 2040 Yale library

would need 6000 miles of shelving (Kent, Lancour & Daily, 1980).

• Data storage of tax returns and fingerprints planned in 1965 (Kraus, 2011).

• Codd creates relational database model in 1970 (Gray, 2004)

How Learning Analytics can make us better

Converting Reality to Data (Adapting Traits)

• Students generally know themselves. (Ngidi, 2013).

• “People have different characteristics which affect their life affairs; even the way they learn is influenced by these personal characteristics” (Boroujeni, A., Roohani, A., & Hasanimanesh, A., 2015, p. 212)

• Aptitude tests discover relevant training programs, identify talents, and allow for traits to become data (Barrett, 2012)

(Furnham, Monsen, & Ahmetoglu, 2009, p. 770)

Using the Past to Predict the Future

•Student Data Points

•Student Behavior Data Points

•Next Stop: Lrng Analytics “Funnel”Determine Predictors-Course Success

•Sample Method: Linear Regression to Correlate Student Data/Course Predictors

Adapting Students Traits to Data Points

Selecting the Significant Data Points for Course

Charting the Most Significant Data Points

Selecting Significant “Influential” Data Points

Learning Analytics Dashboards

(Dringus 2012)

LA “Profiles” = Prescriptive Interventions

“Using Student Data/Behavior and Applying Learning Analytics to

“PROFILE” Today’s Learners and Improve Teaching and Learning”

(Curriculum Minds Team 2015)

Learning Analytics Support Valid Prescriptions

Increase Amount of Time Reading, Reviewing, and Responding to

Discussion Board Posts

Proactive Course Predictors of Success

Reactive Student Perf. Improv. Plan

Did we Solve the Case?

Summary

• Our students should not be bound by ignorance of their own learning style.

Conclusion

• We have found our unknown subject: That learning method that allows early detection of academic issues. Using the analytics of learning, we can continue to push onward toward the elimination of ignorance in this field!

• Break the chains of learning challenges

As we Return from this Quest, Remember . . .

•Never doubt that a small group of thoughtful, committed citizens can change the world. Indeed, it is the only thing that ever has.”

- Margaret Mead

Questions?

References• Austrian, G. D. (1982). Herman Hollerith: The forgotten giant of information

processing. Columbia.

• Barrett, J. (2012). Ultimate aptitude tests. [electronic resource] : assess and develop your potential with numerical, verbal and abstract tests. London ; Philadelphia : Kogan Page, 2012.

• Barnes, T. J. (2013). Big data, little history. Dialogues in Human Geography 3: 297–302, doi: 10.1177/2043820613514323

• Boroujeni, A., Roohani, A., & Hasanimanesh, A. (2015). The Impact of Extroversion and Introversion Personality Types on EFL Learners' Writing Ability. Theory & Practice In Language Studies, 5(1), 212-218. doi:10.17507/tpls.0501.29

• Drigas, A. S. & Leliopoulos, P. (2014). The use of big data in education. International Journal of Computer Science Issues, 11(5). 58.

• Dringus, L. P. (2012). Learning Analytics Considered Harmful. Journal Of Asynchronous Learning Networks, 16(3), 87-100.

References• Furnham, A., Monsen, J., & Ahmetoglu, G. (2009). Typical intellectual

engagement, Big Five personality traits, approaches to learning and cognitive ability predictors of academic performance. The British Journal Of Educational Psychology, 79(Pt 4), 769-782. doi:10.1348/978185409X412147

• Kennedy, G., Ioannou, I., Zhou, Y., Bailey, J., & O'Leary, S. (2013). Mining interactions in immersive learning environments for real-time student feedback. Australasian Journal Of Educational Technology, 29(2), 172-183.

• Kent, A., Lancour, H., & Daily, J. E. (1980). Encyclopedia of Library and Information Science, 29. CRC Press.

• Ngidi, D. P. (2013). Students' personality traits and learning approaches. Journal Of Psychology In Africa, 23(1), 149-152.

References• Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014).

Quality Indicators for Learning Analytics. Journal Of Educational Technology & Society, 17(4), 117-132.

• Truedsell, Leon E. (1965). The development of punch card tabulation in the Bureau of the Census 1890-1940. United States Government Printing Office. p. 51.

• Weiss, E.A. (2000). Magnetic recording, the first 100 years. IEEE, Annals of the History of Computing 22(1). doi: 10.1109/MAHC.2000.815472