USING LEARNING ANALYTICS TO IMPROVE STUDENT SUCCESS Melisa Lacroix - - PowerPoint PPT Presentation

USING LEARNING ANALYTICS TO IMPROVE
 STUDENT SUCCESS


Melisa Lacroix Northeastern University • Education as an Advanced Field of Study • Fall 2015

B I G D ATA’ S PA R A D I G M S H I F T

“The ability of society to harness information in novel ways to produce useful insights.” (Mayer-Schönberger & Cukier, K. ,2013)

W H AT I S L E A R N I N G A N A LY T I C S ( L A ) ?

• Measurement
• Collection
• Analysis
• Reporting
• Produces “actionable intelligence”

T H E C A S E F O R L E A R N I N G A N A LY T I C S

• Pressure on K-12 to make sure students

are career or college ready

• No Child Left Behind mandates
• Colleges and universities challenged to

increase retention and graduation rates

• The economy is dependent upon a well-

educated population

• The U.S. is falling behind other countries

in education (Schleicher, 2012)

Click on link at right to view video

P O T E N T I A L B E N E F I T S O F L E A R N I N G A N A LY T I C S

• More of the right kinds of

information can lead to better decisions

• Improve teaching, learning and

student success

• Early warning system to help identify

students needing interventions

H I S T O R Y O F L E A R N I N G A N A LY T I C S

“Analytics have arrived later to education than to government, healthcare, and business. While the education field has deep roots in data and analysis (research, after all, is primarily an exercise in making sense of data), the systemic use of analytics for improving teaching and learning is still emerging.” (Siemens, 2014)

C O N C E R N S

“Despite the great enthusiasm that is currently surrounding LA, it also raises substantial questions for research… Among these are questions of data

• wnership and openness, ethical use

and dangers of abuse, and the demand for new key competencies to interpret and act on LA results” (Greller & Drachsler, 2012).

C O N C E R N S

• Schools must be ready. They need:
• 1. “Leaders who are committed to

evidence-based decision-making

• 2. Administrative staff who are

skilled at data analysis

• 3. A flexible technology platform

that is available to collect, mine, and analyze data” (Goldstein, 2005)

• Merging data
• Whole student

E T H I C S

From Code of Practice for learning analytics, Jisc

T Y P E S O F D ATA

• Student information systems
• Learning Management Systems
• E-Portfolio systems
• Social media
• Campus interactions
• Help systems
• Assessment data

W H O ’ S U S I N G L E A R N I N G A N A LY T I C S ?

• Higher Education
• Purdue (Signals Program)
• Arizona State University
• Baylor University
• K-12
• Data consultants

F U T U R E D I R E C T I O N S F O R L E A R N I N G A N A LY T I C S

“One way to prompt broader thinking about potential data is to ask, ‘What decisions could we make if we had all the information we need?’” (Barton & Court, 2012) “It is still too early to base education fully

• n LA approaches alone, and we expect it

never will be possible to do so. However, at the very least, opportunities this new discipline has to offer are to provide new support for learning activities and stimuli for reflection” (Greller & Drachsler, 2012).

R E F E R E N C E S

Anderson, C. (2008, June 23). The end of theory: The data deluge makes the scientific method obsolete. (2008, June 23). Retrieved December 6, 2015, from http://www.wired.com/2008/06/pb-theory/ Arnold, K. (March 3, 2010). Signals: Applying academic analytics. EDUCAUSE Review. Retrieved from: http:er.educause.edu/articles/2010/3/signals-applying-academic-analytics Barton D. & Court D. (2012, October). Making advanced analytics work for you: A practical guide for capitalizing

• n big data. Harvard Business Review, 90(10), 79-83.

Campbell, J.P ., DeBlouis, P .B. & Oblinger, D.G. (2007). Academic analytics: A new tool for a new era. Educause Review Online, 42(4), 40-57. Charlton, P ., Mavrikis, M., & Katsifli, D. (2013). The potential of learning analytics and big data. Ariadne, (71). Retrieved from: http://www.ariadne.ac.uk/issue71/charlton-et-al#sthash.oCSRCeGW.dpuf Goldstein, P . (2005). Academic analytics: The uses of management information and technology in education. Educause Center for Analysis and Research (ECAR). Retrieved from: http://www.educause.edu/library/ resources/academic-analytics-uses-management-information-and-technology-higher-education Greller, W. & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning

• analytics. Educational Technology & Society, 15(3), 42-57.

Macfadyen L. & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education 54(2010), 588-599. Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work and

• think. London: Murray.

Newman, A. (2013, August 19). Orwellian nightmare: Data-mining your kids. The New American, 29(16), 20-23.

R E F E R E N C E S

Schleicher, A. (2012). Use data to build better schools [Video file]. Retrieved from https://www.ted.com/talks/ andreas_schleicher_use_data_to_build_better_schools?language=en#t-606707 Siemens, G. (2013). The structure and logical of the learning analytics field [PowerPoint slides]. Retrieved from http://www.learninganalytics.net Siemens, G. (2014). The Journal of Learning Analytics: Supporting and promoting learning analytics research. Journal of Learning Analytics, 1(1), 3-4. Tableau (n.d.). Education Reporting. Retrieved from http://www.tableau.com/solutions/education-analytics Will, M. (2014, September 16). U.S. trails in college graduation in global study. Retrieved December 8, 2015, from http://www.edweek.org/ew/articles/2014/09/17/04oecd.h34.html