Big Data. Big Decisions
InformationWeek
Special Coverage Series


Rice University's OpenStax Tutor Tackles Personalized Learning

Machine learning is key to scaling personalized learning, presenters from Rice and Duke argue at SXSWEdu.

Educational 'Technology' Across the Ages
Educational 'Technology' Across the Ages
(click image for larger view and for slideshow)
Can personalized learning technology save university students from their own bad study habits?

One of the great promises of applying technology to education is that it will allow every student to have a personalized learning plan, with a closed-loop link between learning assessments and further instruction on areas of weakness.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

At SXSWEdu, the educational innovation and learning technologies spin-off of the South by Southwest conference, Richard G. Baraniuk of Rice University and Andrew C. Butler of Duke University spoke about some of the barriers to achieving that goal and presented one possible solution, OpenStax Tutor, an open-source resource aimed at improving college study skills.

"This is not the first time technology has promised to revolutionize education," Baraniuk said. In their day, the movie projector and the television were new technologies promoted for their revolutionary educational potential, he noted.

[ Are tablets taking over education? Read Why Tablets Will Kill Smart Boards In Classrooms. ]

"The difference is, for the first time we have technologies that are not just broadcast technologies. We have technologies that can acquire massive amounts of personalized data about students as they work through problems," said Baraniuk. That data can drive feedback to the students, as well as feedback for the improvement of curricula and research into better educational methods, he said.

This is the latest in a series of projects Baraniuk has championed through the Rice Center for Digital Learning and Scholarship. Back in 1999, his team introduced Connexions, a site that encourages educators to create modular learning resources and share them freely. OpenStax College was introduduced in February 2012 to promote the creation and promotion of open-source college textbooks. OpenStax Tutor also is aimed primarily at college-level education, although components of the software also are being used as part of Rice's STEMScopes program supporting science, technology, engineering and math education for the K-12 grades.

For the OpenStax Tutor project, Baraniuk, a professor of electrical and computer engineering, has teamed up with experts in cognitive science including Butler.

One of the problems with current approaches to learning is they tend to be "cognitively uninformed," meaning they fail to take advantage of the latest research on how we really learn and remember what we have learned, Baraniuk said.

Left to their own devices, students also tend to choose ineffective study strategies, Butler said. "They're very focused on the short term, so there's a lot of cramming going on," he said. That's a problem because information learned in a rush tends not to be retained very well, he said. What works much better is to space out learning, with testing and refreshing doses of learning spread out over time.

What's most productive isn't necessarily what's easiest. "We want to present students with desirable difficulties," Butler said. "So we have this tension between what people like and what's good for them."

 1 | 2  | Next Page »


Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.