What can museums do to get more immigrants through their doors?

Reaching out to newcomers: the National Gallery of Denmark. Image: SMK Statens Museum for Kunst (officiel)/Flickr/creative commons.

As the bastions of a local and national culture, museums can often feel steeped in tradition and history rather than the problems of today. Yet in recent years, this has been changing, with some museums embracing a social justice agenda, aware that they need to become more relevant for 21st century society.

As part of my recent work on how museums can address the pressing social and economic needs of immigrants, I carried out research in five museums and art galleries in Copenhagen, Manchester and Paris.

I found that museums have a unique role to play in providing opportunities for immigrants to learn the language of their host country and to gain employment skills. But despite ongoing programmes to provide these skills, they still struggle to attract less-privileged immigrants – and actually reinforce the view of museums as elitist places.

Part of my research focused on Manchester Museum, which offered two volunteering programmes for a diverse range of participants, including immigrants. The In Touch Volunteer Programme ran between 2007 and 2010, followed by the Inspiring Futures: Volunteering for Wellbeing programme between 2013 and 2016. In addition, Manchester Museum and Manchester Art Gallery provide regular free English conversation classes through an ongoing programme called English Corner.

Good for skills development

Museums play a key role in developing the communication skills essential for learning a foreign language. Their objects are particularly powerful in helping learners to bring their everyday experiences and life stories into the class – a strategy called “bringing the outside in”. A 2007 study of English language lessons found such strategies were essential, helping students to construct more complex sentences as well as to speak more fluently.

This process occurred most clearly at an English Corner session organised for a group of refugee women at Manchester Museum. One woman from Somalia, who I interviewed a week after the museum session, spoke almost solely about a bowl from her community used as handling object during the session. She obviously felt very proud that such an object was at the museum and that she was able to touch it and explain its different functions to others.

Butterflies welcome visitors at Manchester Museum. Image: tom_t.photography/Flickr/creative commons.

Museums can also provide immigrants with essential employment skills that can boost their self-esteem and self-confidence. Volunteers on Mancheter Museum’s Inspiring Future programme took part in ten sessions on developing heritage knowledge. They then worked for 60 hours in the museum galleries on handling tables where they were responsible for interacting with the public around a specific artefact that they could touch. The volunteers I interviewed felt privileged to be able to touch these historically loaded objects – it gave them social prestige and turned them into experts.

An independent evaluation of the programme, conducted by the Envoy Partnership, found these volunteering activities boosted the self-confidence of participants. While they all reported feeling low self-confidence at the start of the project, a year after the volunteering experience, most reported that they often felt self-confident, and even more so after two years. They said it was a direct result of the programme.


Some immigrants left out

But despite the importance of these programmes to help develop language and employment skills, most of them actually marginalised or excluded less-privileged immigrants or those who were not fluent in the language of the host country.

Even when programmes were organised with less privileged immigrants, such as refugees and asylum seekers in mind, they often did not come to the museum without being brought in. The official evaluations of the volunteering programmes at Manchester Museum confirm these trends. While recent migrants and asylum seekers were a target group of the In Touch programme, only 13 out of a total 203 participants had refugee or asylum seeker status, and 28 participants were of black or minority ethnic background, which could have included immigrants.

The successor programme, Inspiring Futures, no longer specifically targeted recent migrants and asylum seekers. Its first-year evaluation indicated that 85.7 per cent of participants were white – and those I interviewed were all from the US. The second-year evaluation indicated that the programme had become even more ethnically homogeneous, with under 10 per cent of participants from ethnic minority backgrounds.

The trends were slightly different in Copenhagen, as the programmes I studied – language learning and employment projects at the National Gallery of Denmark and Thorvaldsens Museum – solely recruited immigrants. Yet, those immigrants they did recruit were usually from relatively comfortable backgrounds. When immigrants from less privileged backgrounds were recruited, they showed weaker outcomes and less progression than other participants.

The ConversationMuseums must better understand the issues faced by less-privileged immigrants and the reasons why they do not attend programmes that target them. By developing innovative programmes to provide language and employment skills that tackle multiple forms of exclusion, and expanding their activities beyond their walls, they could start to reach out to these immigrants and asylum seekers.

Sophia Labadi, Senior Lecturer in Heritage and Archaeology, University of Kent.

This article was originally published on The Conversation. Read the original article.

 
 
 
 

Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

This article was originally published on The Conversation. Read the original article.