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Learning Analytics

How to use data to

PREDICT student

Progress and

performance?

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Learning Analytics

ducators struggle all

the time when it

comes to evaluation,

it is not easy to really

understand what works

and what does not. Having

students with different

needs and different

learning styles makes it

hard for

educators to

come up with

curricula that

suit every

learner.

Educators need

feedback; they

need data to

rely upon when

making decisions.

EDUCAUSE’s Next

Generation learning

initiative defines

Learning Analytics as

“…the use of data and

models to predict

student progress and

performance, and the

ability to act on that

information.”

E

“…more precise and

accurate information

should facilitate greater

use of information in

decision making and

therefore lead to higher

firm performance”

Brynjolfsson (2011)

What is Learning Analytics?

Learning Analytics (LA) is like

software that improves

understanding of teaching and

learning, and links education to

individual students more

effectively.

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Learning Analytics

How Does Learning

Analytics Works?

Students produce a vast

amount of data in their day-to-

day academic activities, and

what LA does is collect and

analyze this data in order to

help and assess academic

progress, spot potential

issues, and predict future

performance. LA

simply finds links

between

students’ digital

activities and learning

outcomes, and then transfers

these links into patterns that

can be used to improve

students’ learning.

The kind of

students’ digital

activities that are

usually used by

LA are: the

frequency of

accessing online

materials, exams

and exercises

results, grades,

finishing

assignments,

time spend on

online

interactions like

posting on

discussion

forums…etc. The most common use of

LA is to identify students-

at-risk by analyzing their

digital activities and

compare results to the

rest of the class

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Learning Analytics

ONE: After identifying

students-at-risk, educators

can then find better ways to

help such students by adapting

different ways of assessments

and interventions that help to

achieve much better academic

outcomes.

TWO: Educators can also use

LA to predict what really

works and improve students’

outcomes very early in the

academic year. By using LA,

educators can pick up on

signals that indicate

difficulties with learner

performance.

THREE: Learning Analytics

can be used by students

themselves as an assurance

that they are doing fine and on

the right track of academic

success.

Three Major Benefits Come with Learning Analytics

Applications

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Learning Analytics

In a simplified definition for the

whole process conducted by LA,

data is gathered from different

sources, and then analyzed.

After that, predictions are

made, educators then based on

these predictions make some

adaptation, modification

personalization, and

intervention.

The Process

Gathering

Analyzing

Predictions

“… it’s sufficient to state that our data trails and profile, in

relation to existing curriculum, can be analyzed and then

used as a basis for prediction, intervention, personalization,

and adaptation.” ELEARNSPACE Website

Adaptation Modification Personalization Intervention

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Learning Analytics

Higher education faces

numerous challenges in

improving teaching and

learning, completion

rates and increasing

accountability. In order

to face these challenges

and make effective

decisions, collecting

students’ data alone is

not enough. This data

needs to go through a

specific system, a system

that collects, measures,

analyzes, and reports

data about learners for

the purposes of

understanding learning

and the environments in

which it occurs.

Learning Analytics benefits in higher education can be divided into five categories according to Campbell et al. (2007) in the article Academic Analytics: A new tool for a new era: …into five categories that would make the analysis manageable but not too broad-brush: (1) academic services (academic advising and tutoring); (2) recreational resources (recreation center usage, fitness program, intramural sports, and wellness education); (3) social resources (student organization membership, after-hours events, and social activities); (4) academic referrals (by centralized advising center staff to academic departments for curricular and degree program assistance); and (5) advising/career sessions (with centralized advising center staff and resources)

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Learning Analytics

WHAT ARE THE

DOWNSIDES?

With all the potentials and hopes that come with LA, some concerns also

show up:

- The data that is collected and analyzed may be protected by privacy

regulations, which raises questions like if the institution needs approval before

data is used, or who has access to the data.

- Also, the notion that a person can track the actions of students’ daily activities

within a software application may raise the specter of “Big Brother”. This

might be okay with some students but at the same time it might be threatening

to others. This raise questions like: does a student need to provide formal

consent before data can be collected? Does a student have an option to reject an

analytics process?

- Another major concern is that LA predictions may sometimes be unable to

get the real picture. Although LA predictions are based on the data available,

no prediction can take into account all the possible aspects of success, like, for

example, financial problems or problems at home.

- Furthermore, some faculties may feel that LA prediction, and the new

adaptations that come along with it, minimize their authority or they may

feel obligated by these predictions that are only based on numbers.

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Learning Analytics

Done BY...

Taghreed Alhaddab

For the Course EDST6210

Real World Technology

Spring 2012

References Campbell, J., DOblinger, D. & eBlois, P. Academic analytics: A new tool for a new era. Retrieved March 19, 2012, from http://net.educause.edu/ir/library/pdf/erm0742.pdf Gestwicki, P.Learning analytics: visualizing collaborative knowledge work. Retrieved March 20, 2012, from http://emergingmediainitiative.com/project/learning-analytics/ Gsiemens. (2010). What are learning analytics?. Retrieved March 19, 2012, from http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/ Johnson, L., Smith, R., Willis, H., Levine, A., and Haywood, K., (2011). The 2011 horizon report. Austin, Texas: The New Media Consortium. Norris, D., & Baer, L. (2008). Action analytics: Measuring and improving performance that matters in higher education. Retrieved March 20, 2012, from http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume43/ActionAnalyticsMeasuringandImp/162422 Norris, D., Baer, L., Leonard, J., Pugliese, L. and

Lefrere, P. (2008). Framing action analytics

and putting them to work, EDUCAUSE Review

43(1). Retrieved March 19, 2012 from

http://www.educause.edu/EDUCAUSE+Revie

w/EDUCAUSEReviewMagazineVolume43/Fra

mingActionAnalyticsandPutti/162423