Big Data and Education Education at both the K-12 and university levels is now supported inside and outside the classroom by a range of technologies that help foster and enhance the learning process. Students now access class materials, watch instructional videos, comment on class activities, collaborate with each other, complete homework, and take tests online. Technology-based educational tools and platforms offer important new capabilities for students and teachers. After only a few generations of evolution, these tools provide real-time assessment so that material can be presented based on how quickly a student learns. Education technologies can also be scaled to reach broad audiences, enable continuous improvement of course content, and increase engagement among students.

http://mfeldstein.com/white-house-report-big-data-will-impact-ed-tech/

I think the difference between the two is that any meaningful analysis of big data leads to learning, but every learning may not involve big data. For example, when one learns  language or music or basic principles of, for example, any social science, it does not involve big data.

Big Data and Education Education at both the K-12 and university levels is now supported inside and outside the classroom by a range of technologies that help foster and enhance the learning process. Students now access class materials, watch instructional videos, comment on class activities, collaborate with each other, complete homework, and take tests online. Technology-based educational tools and platforms offer important new capabilities for students and teachers. After only a few generations of evolution, these tools provide real-time assessment so that material can be presented based on how quickly a student learns. Education technologies can also be scaled to reach broad audiences, enable continuous improvement of course content, and increase engagement among students.

http://mfeldstein.com/white-house-report-big-data-will-impact-ed-tech/

The Potential of Learning Analytics and Big Data

Big Data applies to information that can’t be processed or analyzed using traditional processes or tools. Increasingly, organizations today are facing more and more Big Data challenges. They have access to a wealth of information, but they don’t know how to get value out of it because it is sitting in its most raw form or in a semistructured or unstructured format; and as a result, they don’t even know whether it’s worth keeping (or even able to keep it for that matter).’[3, p.3]

There are three characteristics that define Big Data: volume, variety and velocity. Big data is well suited for solving information challenges that do not natively fit within a traditional relational database approach for handling the problem at hand. The major shortcomings of the existing tools today according to the report by Emicien [6]:

• Search helps you discover insights you already know, yet it does not help you discover things of which you are completely unaware.
• Query-based tools are time-consuming because search-based approaches require a virtually infinite number of queries.
• Statistical methods are largely limited to numerical data; over 85% of data are unstructured.

Learning Analytics and Educational Data Mining

We need to use new models that do not treat data as only numerical data, that are time-efficient as the data available have a time dependency relevance and can provide a representation of means of informing us of new potential discoveries.

- See more at: http://www.ariadne.ac.uk/issue71/charlton-et-al#.dpuf

http://www.ariadne.ac.uk/issue71/charlton-et-al#.dpuf

It is not necessary to build own massive data repositories before starting with big data analytics, government and various public organizations have already put large amounts of information into the public domain have made large volumes of data accessible to everyone.

http://www.datasciencecentral.com/m/blogpost?id=6448529:BlogPost:244056

For most of people in the field of education, Big Data analysis will come to us already 'digested' (e.g. articles such as the quoted by Brenda above).

Semantic Web developers are in the front line. If Semantic Web 'really' works (I understand that until now it only works in clusters or in simpler versions such as data mining), most of data in the web that remain unstructured today will be structured by machine-generated semantics (not semantics provided by human mind). On the latter, there are skeptics who say that this is not possible. 

I understand that those who claim to be 'successful', they are only in certain fields and clusters such as business application (or by 'tipping' or 'cheating', that is, instead of leaving it to the machine, humans provide a set of parameters).

Going back to my initial argument, most of educators will only be consumers of 'half-digested' Big Data analysis.

I personally still give more credit to pencil-paper big scale meta-analysis. I am open to new era though.

Big data and analytics add greater value to higher education.

• They can improve decision making and  resource management.
• Success rate of students can be increased by identifying the risk at earlier stage.
• Institutional growth can be enhanced and challenges tackled effectively by the transparent data available.
• Innovative models can be developed for transforming the university or college system.
•  Decision making would be facilitated by “what if” experiments.
•  Usage of social networking and technical and information network could provide assistance in handling complex issues.
• Hard values like patents and soft values like brand building generated by faculty activity can be determined.
• Productivity of the institution can be enhanced through effective responses generated due to real time data availability.

Source(PDF attached):

Tulasi, B. (2013). Significance of Big Data and analytics in higher education. International Journal of Computer Applications, 68(14).

Here are a few articles on the subject:

K., & Muthu, L. (2015). Application of big data in education data mining and learning analytics—A lterature review. ICTACT Journal on Soft Computing, 5(4), 1-035.

Shun-ping, W. E. I. (2013). Learning Analytics: Mining the Value of Education Data under the Big Data Era [J]. Modern Educational Technology, 2, 003.

Dix, A. J., & Leavesley, J. (2015). Learning Analytics for the Academic: An Action Perspective. J. UCS, 21(1), 48-65.

Big Data & Analytics in Education!

"Failing to leverage big data means lost potential. Big data offers little business (or educational) value until it is transformed into knowledge through analytics engines. This involves interconnecting data from disparate systems; using readily available, existing data sources in new ways, to achieve greater outcomes; thinking creatively about how the relationships between sources of data can create new value; reducing manual workload and analysis times; improving communication using technology; and improving decision-making using readily available data...

Through day-to-day activities, students' and staff generate an enormous amount of data that could be used as part of an analytics solution. This data includes academic results, attendance and behaviour records, timetables, printing and photocopying logs, activity on learning management systems (ie. Blackboard), website and authentication logs and financial transactions. The key to using this data effectively is thinking creatively about how it can be used to add value to students' learning experience...

By using analytics solutions to monitor assessment results and identify outliers, students that are underperforming can be flagged automatically on guidance counsellors’ dashboards. Attendance data, teacher feedback reports and LMS activity could be used to predict poor performance. Computer and internet activity logs could be monitored in real-time to ensure students are on-task when they’re in class. If students are off-task, mobile notifications could be sent to the teacher in real-time so they can steer the student back on track. To assess effort grades, school management could identify patterns in data that correlate with high engagement, such as hours spent viewing content in the LMS, and statistically calculate a student’s effort grade...."

https://www.linkedin.com/pulse/big-data-analytics-education-thomas-pagram?trkInfo=VSRPsearchId%3A577811291459234715096%2CVSRPtargetId%3A7627655799031561459%2CVSRPcmpt%3Aprimary&trk=vsrp_influencer_content_res_name

"We launched Sense with the mission of helping data scientists and data engineers focus on what's important -- extracting value rather than managing infrastructure," wrote Sense founders Tristan Zajonc and Anand Patil in a blog post.

"As the quantity of data explodes and machine learning pushes computing to new limits, this mission has only become more important."

http://www.informationweek.com/big-data/big-data-analytics/google-loves-machine-learning-cloudera-acquires-startup-big-data-roundup/d/d-id/1324845

Dear Brenda Jacono, the link you posted is the same as mine (hard to miss, above your response in pervious page!). We should read all answers before adding ours, that is the purpose of Q/A.

Here are more information about leaning analytic:

Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. A related field is educational data mining. For general audience introductions, see:

• The Educause Learning Initiative Briefing 
• The Educause Review on Learning analytics
• And the UNESCO "Learning Analytics Policy Brief" (2012)

https://en.m.wikipedia.org/wiki/Learning_analytics

Learning analytics can help monitor, evaluate, and predict learner performance.

http://www.brightspace.com/solutions/higher-education/advanced-analytics/

Please also see the attached infographic.

Behrouz:

Thanks for sharing your link and pointing out the similarity. I really did not realize it.

Debra

Some more articles about the issue.

What Universities Can Learn from Big Data – Higher Education Analytics

Learning Analytics in Higher Education: A Summary of Tools and Approaches

"Higher education institutions recently have been drawing on methods from learning analytics to make decisions about learners’ academic progress, predictions about future performance and to recognise potential issues. As the use of learning analytics in higher education is a relatively new area of practice and research, the intent of this paper is to provide an overview of learning analytics including a summary of some exemplar tools..."

https://infocus.emc.com/william_schmarzo/what-universities-can-learn-from-big-data-higher-education-analytics/

http://www.ascilite.org/conferences/sydney13/program/papers/Atif.pdf

Dear Debra et all, 

Big data involve relationships from disparate systems and needs a creative thinking in a way to transform it into knowledge through the creation of added value, helping in this way a decision-making or whatever learning process.

Have a nice time

Helena

Learning analytics is more pertinent to MOOCS but is very useful for e learning institutions as well. Yes it can trace students performance both poor and good and can help to guide them accordingly. It can also help in tracing dropouts and contacting them accordingly. This much is commonly used. On an advanced level LA can help us see how students approach certain courses cognitively and what is their learning path. 

Hi Debra

I understood your question in a more simplistic manner, such as :.

"How we could analyze the learning of a student or group by big data analysis, in contrast to the current analysis of learning?"

For example: Learning the formal language usage (in my case Portuguese) could sometimes be evaluated by the posts of the student in social media. However, it comes up against very seriously the privacy boundary of him.

In another way, an analysis of students posts (using only a small sample of the big data universe) could results in the development of new tools for motivation and teaching geared to meet the needs analyzed.

However, few teachers have the means and the time required for this type of analysis, that when it happens is informal. At least in Brazil.

ok.

Big data can be analyzed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis. andThe Context of Learning Analytics;

The rise of online education focussed around Virtual Learning Environments (VLEs), Content Management Systems (CMSs), and Management Information Systems (MIS) for education, which saw an increase in digital data regarding student background (often held in the MIS) and learning log data (from VLEs). This development afforded the opportunity to apply 'business intelligence' techniques to educational data