Learning analytics

How to use data to PREDICT student Progress and performance?

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Learning Analytics

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ducators struggle all the time when it What is Learning Analytics? comes to evaluation, Learning Analytics (LA) is like it is not easy to really software that improves understand what works understanding of teaching and and what does not. Having learning, and links education to students with different individual students more effectively. needs and different learning styles makes it hard for educators to “…more precise and come up with accurate information EDUCAUSE’s Next curricula that should facilitate greater Generation learning suit every use of information in initiative defines learner. decision making and Learning Analytics as Educators need therefore lead to higher “…the use of data and feedback; they firm performance” models to predict Brynjolfsson (2011) need data to student progress and rely upon when performance, and the making decisions. ability to act on that information.”

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Learning Analytics

How Does Learning Analytics Works?

Students produce a vast amount of data in their day-today 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 most common use of LA is to identify studentsat-risk by analyzing their digital activities and compare results to the rest of the class

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Learning Analytics

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.

Three Major Benefits Come with Learning Analytics Applications

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.

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Learning Analytics

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.

The Process

Gathering

Analyzing

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.

Predictions

Adaptation

Modification

Personalization

Intervention

“… 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

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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.

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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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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/erm0 742.pdf Gestwicki, P.Learning analytics: visualizing collaborative knowledge work. Retrieved March 20, 2012, from http://emergingmediainitiative.com/project/l earning-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+Revie w/EDUCAUSEReviewMagazineVolume43/Acti onAnalyticsMeasuringandImp/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

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Learning Analytics

Done BY...

Taghreed Alhaddab For the Course EDST6210 Real World Technology Spring 2012