Tag Archive | Educational Data Mining

RSS feed for this tag

Biosensors to monitor students’ attentiveness


DENVER (Reuters) – The Bill & Melinda Gates Foundation, which has poured more than $4 billion into efforts to transform public education in the U.S., is pushing to develop an “engagement pedometer.” Biometric devices wrapped around the wrists of students would identify which classroom moments excite and interest them — and which fall flat.

The foundation has given $1.4 million in grants to several university researchers to begin testing the devices in middle-school classrooms this fall.

The biometric bracelets, produced by a Massachusetts startup company, Affectiva Inc, send a small current across the skin and then measure subtle changes in electrical charges as the sympathetic nervous system responds to stimuli. The wireless devices have been used in pilot tests to gauge consumers’ emotional response to advertising.

Gates officials hope the devices, known as Q Sensors, can become a common classroom tool, enabling teachers to see, in real time, which kids are tuned in and which are zoned out.

Existing measures of student engagement, such as videotaping classes for expert review or simply asking kids what they liked in a lesson, “only get us so far,” said Debbie Robinson, a spokeswoman for the Gates Foundation. To truly improve teaching and learning, she said, “we need universal, valid, reliable and practical instruments” such as the biosensors.

Gates Foundation Responds To GSR Bracelets Controversy


A relatively tiny donation from the Bill & Melinda Gates Foundation has created quite a stir over the past several days. News broke that Clemson U. had late last year obtained a nearly half million dollar grant from the foundation to conduct a pilot study with Galvanic Skin Response (GSR) bracelets, wireless sensors that track physiological reactions, in schools. The idea supposedly was that children would wear these biometric bracelets in classrooms to measure their engagement. What made this grant even more polarizing was the notion that the bracelets were in fact tools that would evaluate teachers’ effectiveness.

Then came the discovery of another grant, this one for $621,265, awarded to the National Center on Time & Learning Inc. to “measure engagement physiologically with Functional Magnetic Resonance Imaging and Galvanic Skin Response,” also to be used to gauge degrees or levels   of engagement.”

Online Education Run Amok? Private Companies want to Scoop Up your Child’s Data


But when middle and high school students participate in classes with names like “Mars: The Next Frontier” or “The Road to Selective College Admissions,” they may be unwittingly transmitting into private hands a torrent of data about their academic strengths and weaknesses, their learning styles and thought processes — even the way they approach challenges. They may also be handing over birth dates, addresses and even drivers license information. Their IP addresses, attendance and participation in public forums are all logged as well by the providers of the courses, commonly called MOOCs.

With little guidance from federal privacy law, key decisions on how to handle students’ data — including how widely to share it and whether to mine it for commercial gain — are left up to the company hosting the MOOC or its business partners. In fact, student data is even less protected by federal law since the Education Department updated regulations in 2012 to allow for even greater disclosure of students’ personal identifying information.

The Ethics of Big Data in Higher Education


Data mining and predictive analytics—collectively referred to as “big data”—are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge con- siders the extent to which data mining’s outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.

The Asilomar Convention for Learning Research in Higher Education – Six Principles


Six principles should inform the collection, storage, distribution and analysis of data derived from human engagement with learning resources. The principles are stated here at a level of generality to assist learners, scientists, and interested citizens in understanding the ethical issues associated with research on human learning.

  • Respect for the rights and dignity of learners. Data collection, retention, use, and sharing practices must be made transparent to learners, and findings made publicly available, with essential protections for the privacy of individuals. Respect for the rights and dignity of learners requires responsible governance by institutional repositories and users of learner data to ensure security, integrity, and accountability. Researchers and institutions should be especially vigilant with regard to the collection and use of identifiable learner data, including considerations of the appropriate form and degree of consent.
  • Beneficence. Individuals and organizations conducting learning research have an obligation to maximize possible benefits while minimizing possible harms. In every research endeavor, investigators must consider potential unintended consequences of their inquiry and misuse of research findings. Additionally, the results of research should be made publicly available in the interest of building general knowledge.
  • Justice. Research practices and policies should enable the use of learning data in the service of providing benefit for all learners. More specifically, research practices and policies should enable the use of learning data in the service of reducing inequalities in learning opportunity and educational attainment.
  • Openness. Learning and scientific inquiry are public goods essential for well-functioning democracies. Learning and scientific inquiry are sustained through transparent, participatory processes for the scrutiny of claims. Whenever possible, individuals and organizations conducting learning research have an obligation to provide access to data, analytic techniques, and research results in the service of learning improvement and scientific progress.
  • The humanity of learning. Insight, judgment, and discretion are essential to learning. Digital technologies can enhance, do not replace, and should never be allowed to erode the relationships that make learning a humane enterprise.
  • Continuous consideration. In a rapidly evolving field there can be no last word on ethical practice. Ethically responsible learner research requires ongoing and broadly inclusive discussion of best practices and comparable standards among researchers, learners, and educational institutions.

Murky Federal Privacy Law Puts MOOC Student Data in Questionable Territory


At a Dec. 2 symposium on student privacy, The Chronicle of Higher Education reports Styles said, “Data in the higher-education context for MOOCs is seldom FERPA-protected.” U.S. Department of Education website says FERPA applies to “all schools that receive funds under an applicable program of the U.S. Department of Education.” MOOCs are rarely funded with Title IV, government-funded dollars, Styles said.

However, two of the largest MOOC providers disagree on whether federal law applies to their student data.

The standard agreement used by edX, a MOOC platform founded by Harvard University and the Massachusetts Institute of Technology, says that it is subject to and complies with FERPA requirements, according to The Chronicle.

Coursera, a MOOC platform founded by Standard University professors, follows the “principles” of FERPA but doesn’t think it applies to MOOCs, its chief academic strategist Vivek Goel told The Chronicle.

Netflix-Like Algorithm Drives New College-Finding Tool


PossibilityU’s data-driven approach to college matching isn’t new, but Mr. Jarratt’s recommendation algorithm is unique. Rather than starting with a list of questions about what students are looking for, PossibilityU asks users to enter up to three colleges that they are interested in. It then spits out a list of 10 other, similar colleges to consider. A premium paid subscription allows students to compare an unlimited number of colleges and provides application deadlines and other advice.

It’s kind of like Netflix’s movie suggestions, says Mr. Jarratt, who studies recommender systems like those used by the movie service and by Amazon.