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robertogreco : learninganalytics   3

Data collected about student behaviour doesn't help improve teaching or learning
"Universities and schools around the world face constant pressure to find measures that demonstrate their impact on student learning. Most recently, they are devoting immense amounts of time, money and other resources to a new measurement approach called learning analytics.

Learning analytics captures data about student and teacher behaviour, most frequently from the learning management software systems (LMS) used by schools and universities to design, manage and deliver their programs and courses.

The data includes tweets, file uploads, downloads, logons and participation in LMS blogs and chat rooms. This data is frequently augmented with information from other systems like student records and administration.

The goal of learning analytics is to establish what works to improve learning and teaching in the same way other fields use analytic approaches to predict the behaviour of shoppers, banking fraudsters and stock traders.

The idea of using data to understand and predict better learning and teaching makes complete sense, so what’s the problem?

The problem is that the learning analytics data gathered is not much about learning or teaching.

When analytics systems monitor the behaviour of shoppers, or fraudsters, or stock traders, they are watching what people do when they shop, commit frauds or make trades; the behaviour the analysts are interested in predicting.

In learning analytics, the concept is the same – to predict whether students learn well and teachers teach effectively.

However, the quality of learning and teaching cannot be determined from the behaviours being watched and counted by an LMS or related systems.

Why? Because knowing how many times a student tweeted or used a chat room has little to do with how teachers teach or the ways students learn.

The current learning analytics approach is like deciding whether a medical practice is successful by counting whether people attend their appointments or pick up their prescriptions, instead of focusing on doctors interacting with patients and the quality of what happens when they do.

No data to show it works

Not surprisingly, there is no body of evidence showing that LMS and other system data improve student learning or teaching.

It is not the case that current learning analytic data is irrelevant. It is correlated with student engagement and participation and may offer general indicators of student needs. It just does not inform teachers or learners what they need to do better or differently to make learning happen.

Focusing on things that do not make an important difference to student learning means we are not paying attention to the things that do.

Over 50 years of research on learning and teaching has told us about what makes a lecture active, how students work best in groups, the strategies that help students learn most effectively, and what makes for quality assessment.

These things among many others are well known, while data about them can be gathered from the interaction among teachers and students face-to-face or online.

Most importantly, they are powerful predictors of student learning. The issue is worthy of serious concern because the things we measure focus our attention, shape our priorities and can ultimately determine what we understand and how we behave. This is a big problem if you are not gathering the right data.

Learning analytics data and the systems that gather it have become proxies, surrogates for what we should be measuring to improve student learning.

Three ways to solve the problem

1. Pay attention to the over 50 years of research about learning and teaching that show visible effects on student achievement, including what makes co-operative and teacher-led learning most effective.

2. Build technology tools that help teachers to design, deliver and evaluate what they do in ways that include effective learning and teaching approaches. A growing body of research is showing that technology can be used in a different way to assist teachers design and deliver more effective learning experiences. This approach offers the potential of a new kind of learning analytics data that focuses on what learners and teachers do.

3. Gather data directly from the people involved – the students and teachers. Ask them to give feedback when they are designing, delivering and participating in learning and teaching, instead of surveilling them. This feedback emerges all the time from the day-to-day interaction among students and teachers.

We know that doing something about these things can make a big difference in student learning. Implementing the three solutions means focusing on the evidence we need instead of the data we have.

We can gather data on how well programs and courses are designed, whether effective practices are being employed, whether assignments line up with what is taught, and whether all those efforts are improving student learning outcomes.

The problem with learning analytics is not simply another example of education wastefully impersonating other fields for little benefit.

These types of data and the systems that gather them are defining what learning and teaching mean. At present, quality is being defined by the data that is available instead of the feedback we need about learning and teaching in schools and universities."
students  schools  data  behavior  2016  learninganalytics  teaching  learningoutcomes  howweteach  surveillance 
may 2016 by robertogreco
New Topics in Social Computing: Data and Education by EyebeamNYC
"In this discussion, we will consider how younger generations are growing up with data collection normalized and with increasingly limited opportunities to opt-out. Issues of surveillance, privacy, and consent have particular implications in the context of school systems. As education and technology writer Audrey Watters explains, “many journalists, politicians, entrepreneurs, government officials, researchers, and others … argue that through mining and modeling, we can enhance student learning and predict student success.” Administrators, even working with the best intentions, might exaggerate systemic biases or create other unintended consequences through use of new technologies. We we consider new structural obstacles involving metrics like learning analytics, the labor politics of data, and issues of data privacy and ownership.

Panelists: Sava Saheli Singh, Tressie McMillan Cottom, and Karen Gregory"
savasahelisingh  tressiemcmillancottom  karengregory  education  personalization  race  class  gender  2015  publicschools  testing  privacy  government  audreywatters  politics  policy  surveillance  consent  social  journalism  learning  howwelearn  howweteach  labor  work  citizenship  civics  learninganalytics  technology  edtech  data  society  socialcontract 
july 2015 by robertogreco
Half an Hour: The Robot Teachers
"There is an ongoing and incessant campaign afoot to privatize education. In the United States, education is almost the last bastion of public expenditure. In Canada, both health care and education face the forces of privatization and commercialization.

The results are wholly predictable. In all cases, the result will be a system that favours a small moneyed elite and leaves the rest of the population struggling to obtain whatever health and education they can obtain with their meagre holdings. As more wealth accumulates in the hands of the corporations and the wealthy, the worse health and education outcomes become for the less well-off in society.

(Indeed, from my perspective, one of the greatest scams perpetrated by the wealthy about the education system is that it has a liberal bias. …)"

But here's where the challenge arises for the education and university system: it was designed to support income inequality and designed to favour the wealthy."
via:tealtan  economics  policy  politics  schooling  oligarchy  wealth  wealthy  sorting  tonybates  liberalbias  criticalthinking  higherorderskills  texas  california  corporations  corporatism  bias  corruption  influence  wealthdistribution  poverty  inequity  disparity  capitalism  adaptivelearningsystems  mitx  udemy  coursera  learninganalytics  programmedlearning  universalhealthcare  healthcare  deschooling  publiceducation  onlinelearning  canon  cv  technology  scriptedlearning  robotteachers  democracy  highereducation  highered  moocs  pedagogy  hierarchies  hierarchy  inequality  schools  education  privatization  privilege  us  canad  2012  stephendownes  mooc 
september 2012 by robertogreco

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