Learning Evaluation: Learning Analytics

Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning. (Siemens, 2010) An analytics system includes the information repository itself, as well as a means for data optimization, predictive modeling, forecasting, and statistical analysis.

According to Ferguson (2012), there are 3 factors driving the development of learning analytics in higher education. They are:
1) Big Data
2) Online Learning
3) Political Concerns

When implemeting learning analytics, the following should be considered:
- Methods for visualizing data that are easy to use and understand
- Personalized dashboards
- Standards for the structure of data
- Ethics, privacy, and ownership of data

Data warehouses and the cloud make it possible to collect, manage, and maintain massive amounts of information, and technology platforms can now help us turn a mass of numbers into meaningful patterns. "Data mining uses descriptive and inferential statistics (e.g., moving averages, correlations, and regressions) and complex functions (e.g., graph analysis, market basket analysis, and tokenization) to look inside those patterns for actionable information. Predictive techniques (e.g., neural networks and decision trees) help anticipate behavior and events."(Wagner & Ice, 2012)

Learning Analytics is here to stay. Information gleaned from transactions and interactions in our online lives that can then be summarized in reports in order to provide us with intelligence for making decisions in a shift toward more desirable behavior is extremely valuable!

Resources:
Ferguson, R. (2012). The State Of Learning Analytics in 2012: A Review and Future Challenges. Technical Report KMI-12-01, Knowledge Media Institute, The Open University, UK. http://kmi.open.ac.uk/publications/techreport/kmi-12-01

Siemens, G. (2010). What are learning analytics?. Retrieved from: http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/

Wagner, E., & Ice, P. (2012). Data changes everything: delivering on the promise of learning analytics in higher education. EDUCAUSE Review, July/August. Retrieved from http://www.educause.edu/ero/article/data-changes-everything-delivering-promise-learning-analytics-higher-education.

Learning Research: 2011 Horizon Report

The Horizon Report is an annual publication that outlines six emerging technologies or practices that are likely to enter mainstream use within higher education over a five year period of time.

Near-term Horizon (adoption within one year):

Electronic Books - an easy alternative to printed materials. Most allow for bookmarking, annotation, commentary, and dictionary lookup, and some allow for richly visual and interactive interfaces that include multimedia.

Mobiles - a viable alternative to laptops. Smartphones allow for web browsing, reading, watching videos, navigating, and playing games. They also provide access to the Apple or Android application markets. Mobiles can be used for anytime, anywhere and just-in-time learning.

Mid-term Horizon (adoption within two to three years):

Augmented Reality - A computer-assisted layer of information over the real world. Can be activated by visual metaphors and cues via a camera or location based cues via GPS systems. Allows learners to interact with virtual objects and view or annotate existing spaces with an overlay of information.

Game-based Learning - Can be goal-oriented, social, non-digital, or serious, ranging from paper-and-pencil games to massively, multiplayer online games. They allow for experimentation, exploration, problem-solving, and decision-making within a safe environment.

Far-term Horizon (adoption within four to five years):

Gesture-based Computing - a new era of interface design that foregoes the keyboard and mouse and opts instead for touching, tapping, swiping, and moving to engage with information. Approaches to gesture recognition can include applying pressure, motion, shaking, rotating, or tilting to manipulate interfaces and objects.

Learning Analytics - data collected from student activity in order to assess academic progress, predict future performance, and identify potential issues. Collected data includes assignments, social interactions, and discussions as a means to personalize educational opportunities for students.

Reference:
2011 Horizon Report. Retrieved from: http://wp.nmc.org/horizon2011/