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För att kunna leverera olika prototyper och veta hur jag ska jobba vidare med konceptet har Double Diamond varit en bra referenspunkt för att komma ihåg att fokusera på det iterativa arbetet.
Främst ger det en påminnelse kring att först arbeta divergent och kunna bredda idéerna och se hur jag kan få ut så mycket som möjligt innan jag går vidare med att arbeta med divergens. I och med att konceptet gjordes i tidigare delprov med skisser, så har konvergens påbörjats redan, och kunde därmed börja använda mig av konvergens igen för att se över just AR-möjligheten till exempel och testa den direkt, därefter gå vidare med det större arbetet och då komma tillbaka till divergens i designen.

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Örnsköldsvik ligger placerat bland berg och med Bottenhavet som mynnar in i stadens kant.

E4an passerar genom stadskärnan vilket påverkas av utsläppen från all trafik.

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Örnsköldsvik är omringat av berg och skog, skulle vi påverkas av skogsbränder skulle stora områden brinna ner och det skulle spridas relativt fort på grund av havsvindarna. Alla berg som finns gör också att det finns många dalar där bostadsområden är byggda och de blir väldigt utsatta i ett sådant läge.

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Om Bottenhavet skulle påverkas av ökade havsnivåer skulle detta påverka stora delar av Örnsköldsvik då det ligger mycket produktion längs med hamnområden

Skulle nivån öka skulle dessutom invånarna bli tvingade att förflyttas då många är bosatta längs med kusten.

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Analys av risker med ökade havsnivåer i Örnsköldsvik

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Analys av risker med ökade havsnivåer i Örnsköldsvik

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Kartvideor

Området runt Örnsköldsvik medan alla istäcken smälter och höjer havsnivån med mellan 0,0 och 65,0 m. Kartorna är centrerade runt 7023100, 686050 (SWEREF 99 TM). Havsnivåhöjning över nuvarande havsnivå.

Klicka på miniatyrkartorna nedan för att titta på videorna. Förklaring finns här.

Kartor

Området runt Örnsköldsvik vid olika havsnivåhöjningar. Kartorna, med och utan etiketter, är centrerade runt 6581050, 674350 (SWEREF 99 TM). Hav och landhöjd är enligt nuvarande havsnivå, översvämmade områden är lila. Havsnivåhöjning (HNH) över nuvarande havsnivå.

Klicka på miniatyrkartorna nedan för att titta på kartor i full storlek. Förklaring finns här.

Konsekvenser

Uppskattning av konsekvenserna för kommunen Örnsköldsvik vad gäller landförluster och fördrivna invånare, p.g.a. översvämningar vid olika havsnivåhöjningar (HNH). Förklaring finns här.

Landyta

Invånare

(km²)

(%)

(n)

(%)

HNH

Landförlust

Fördrivna

(m)

(km²)

(%)

(n)

(%)

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Området runt Örnsköldsvik medan alla istäcken smälter och höjer havsnivån med mellan 0,0 och 65,0 m. Kartorna är centrerade runt 7023100, 686050 (SWEREF 99 TM). Havsnivåhöjning över nuvarande havsnivå. Klicka på miniatyrkartorna nedan för att titta på videorna. Förklaring finns här. Området runt Örnsköldsvik vid olika havsnivåhöjningar.

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I dagsläget passerar E4an genom stadskärnan vilket orsakar konsekvenser av utsläpp i staden. Möjligheter att dra om E4an genom berget istället för att minska utsläppen i centrala Örnsköldsvik har diskuterats sedan 1979 men ingenting sker, om det fortsätter passera så mycket trafik genom staden riskerar

PM 2,5 är en stor risk och påverkar hälsan negativt på både andningsorganen samt hjärtkärlsystemet

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Flytt av E4

Då fordonstrafiken är den klart dominerande luftföroreningskällan i centrala Örnsköldsvik är det viktigt att försöka minska på trafikflödet och därmed sänka föroreningshalterna. Den lösning som beräknas ge störst effekt är att flytta E4 från centrum.

Örnsköldsviks Kommun har sedan 1979 reserverat utrymme i översiktsplaner för en förbifart av E4, via en vägtunnel genom Åsberget. Kommunfullmäktige har vid flera tillfällen uttalat att en flytt av E4-trafiken ska prioriteras. Det är dock inte kommunen som kan ta beslut om en omdragning av E4, utan det är en statlig fråga som beslutas av Trafikverket.

Under vintern 2015/2016 yttrade sig kommunen över två lokaliseringsalternativ genom Åsberget som Trafikverket tagit fram i en utredning. Syftet med lokaliseringsstudien är att kunna välja en slutlig korridor för framtida E4 utifrån de två möjliga sträckningar för en tunnel genom Åsberget som togs fram i en förstudie 2012.

Åtgärdsprogrammet - Renare luft i centrum

Då miljökvalitetsnormen för partiklar med en diameter mindre än 10 mikrometer (PM10) överskreds under 2008 och 2009 beslutade Naturvårdsverket att Örnsköldsviks kommun skulle ta fram ett åtgärdsprogram för att ta itu med problemet. Ett åtgärdsprogram för hur halten av partiklar i luften ska kunna minskas togs fram av kommunen i sammarbete med Trafikverket och Länsstyrelsen och fastställdes av kommunfullmäktige den 31 oktober 2011.

På senare år har miljökvalietsnormen för partiklar klarats i Örnsköldsvik.

Kvävedioxid

Miljökvalitetsnormen för kvävedioxid har överskridits vid Centralesplanaden. Naturvårdsverket har beslutat att Örnsköldsviks kommun ska ta fram ett åtgärdsprogram för att hitta åtgärder för att sänka halterna av kvävedioxid. Arbetet med åtgärdsprogrammet har påbörjats.

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Vad görs för att förbättra luftkvaliteten?

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Då fordonstrafiken är den klart dominerande luftföroreningskällan i centrala Örnsköldsvik är det viktigt att försöka minska på trafikflödet och därmed sänka föroreningshalterna. Den lösning som beräknas ge störst effekt är att flytta E4 från centrum. Örnsköldsviks Kommun har sedan 1979 reserverat utrymme i översiktsplaner för en förbifart av E4, via en vägtunnel genom Åsberget.

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Partikelmassan för partiklar med en diameter upp till 2,5 mikrometer refereras som PM2,5. Partiklar upp till dessa storlekar bildas främst vid förbränning och genom att gaser från förbränningen kondenserar. Små partiklar emitteras även från vägslitage, däck och bromsar. Under 2018 var utsläppen av små partiklar 18,5 tusen ton. Det är en minskning med 59 procent sedan 1990.

Små partiklar är en allvarlig luftförorening

Små partiklar har oftast liten massa, men är många till antalet och tillhör en av de luftföroreningar som ger störst hälsoproblem. De kan genom inandning transporteras in i kroppen och påverka både andningsorganen och hjärt- och kärlsystemen.

Till de akuta effekterna hör hosta, attacker av astma samt kroniskt obstruktiv lungsjukdom (KOL). Studier har också visat att nedsatt lungfunktion kan vara en effekt som uppstår på lång sikt, särskilt hos barn som bor i områden med höga halter av PM2,5.

Vedeldning orsakar stora utsläpp av små partiklar

Den största källan till utsläpp av små partiklar i Sverige är vedeldning vid egen uppvärmning av bostäder och lokaler, vilket svarade för 33 procent de totala utsläppen av PM2,5 2018.

Utsläppen från denna sektor har minskat med 56 procent sedan 1990, då övergång från uppvärmning med biobränsle till el- och fjärrvärme har blivit vanligare och att dagens vedpannor är mer effektiva i förbränningen än äldre vedpannor.

Nya processer inom industrin har minskat utsläppen

Sedan 1990 har utsläppen av små partiklar från industrin minskat med 75 procent. 2018 stod industrin för 24 procent av de totala svenska utsläppen. Framför allt har industriprocesserna för tillverkning av papper och massa bidragit till minskningen. Utsläppen av partiklar från industrin minskade kraftigt fram till 2015, men har sedan planat ut och ligger runt 4 tusen ton per år.

Ökad trafik ökar utsläppen av små partiklar

Utsläppen av PM2,5 från inrikes transporter har minskat med 37 procent sedan 1990 och svarade 2018 för 24 procent av de totala utsläppen av små partiklar. Trots att utsläppen från inrikes transporter har minskat totalt sett, har utsläppen av små partiklar som uppkommer vid slitage av däck, bromsar och vägbana ökat med cirka 30 procent. Det beror på att den totala mängden trafik på våra vägar ökar. 2018 stod slitagepartiklar för cirka 17 procent av de totala utsläppen.

Utsläpp av PM2,5 från tunga lastbilars avgaser har minskat med 92 procent under perioden. Den kraftiga minskningen beror på att striktare avgaskrav har införts.

Sveriges officiella statistik

Denna statistik tillhör Sveriges officiella statistik.

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Luftstatistik - Utsläpp av små partiklar (PM2,5) till luft

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Partikelmassan för partiklar med en diameter upp till 2,5 mikrometer refereras som PM2,5. Partiklar upp till dessa storlekar bildas främst vid förbränning och genom att gaser från förbränningen kondenserar. Små partiklar emitteras även från vägslitage, däck och bromsar. Under 2018 var utsläppen av små partiklar 18,5 tusen ton.

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https://digitaldesign.padlet.org/johanna_zackaroff00111/1lr4heo3fxx6

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Image: MASHABLE COMPOSITE/SHOWCAKE/GETTY/ITUNES

As part of the 2019 Social Good Summit on Sept. 22, Mashable is tackling the climate change problem head on, highlighting the progress the world needs, and celebrating the experts making change happen.
Climate change can often feel like a huge problem with few actionable solutions. Just look at the facts: July was the hottest month on record. Eighteen of the last 19 years have been the warmest recorded. And the rate of CO2 increase in the atmosphere is unprecedented in the geologic record.

While it's true that politicians and entire countries hold many of the keys to fixing these issues, there are simple way you can help - through apps.

Your smartphone isn't only your connection to the world - it can also be your connection to the conversation around climate change. With these four apps, awareness and action are at your fingertips.

1. For the animal lover: WWF Together

The WWF Together app, created by the World Wildlife Fund, shares the stories of species impacted by changing climate with vivid visuals and stunning video. The app presents wildlife news and facts on animals such as elephants and giant pandas, along with information on conservation efforts.

You can also use the app to share information you learn on social media, accompanied by a 3D origami image of the threatened species in question.

Price: Free

Platforms: iOS

2. For the amateur climate scientist: Earth-Now

NASA's Earth-Now app visualizes recent global climate data, pulling information from satellites to create globes depicting a given climate condition or "vital sign." The color-coded globe indicates measurements of a particular environmental condition, making this app ideal for those who like to track climate change with their own eyes.

Price: Free

Platforms: iOS and Android

3. For the policy person: UN Climate Change

Brough to you by the United Nations Framework Convention on Climate Change (UNFCCC), this app provides updated information on the UN Climate Change process so you can participate in UNFCCC events from your phone. It also offers up-to-date news, a calendar of events, and other helpful links to make sure you stay informed.

Price: Free

Platforms: iOS and Android

4. For the visual learner: WWF Free Rivers

Another app from the World Wildlife Fund, WWF Free Rivers demonstrates just how important rivers are to the Earth. It also shows how they're impacted by human actions. Through interactive augmented reality and beautiful visuals, the app proves why we need to keep rivers healthy and flowing.
This story was originally published in 2015 and updated in 2019.
Price: Free

Platforms: iOS and Android

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4 climate change apps every tech-savvy advocate needs to download

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As part of the 2019 Social Good Summit on Sept. 22, Mashable is tackling the climate change problem head on, highlighting the progress the world needs, and celebrating the experts making change happen. Climate change can often feel like a huge problem with few actionable solutions.

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EcoChange är ett strategiskt forskningsprogram med fokus på hav och klimat.

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Burning fossil fuels, cutting down forests and farming livestock are increasingly influencing the climate and the earth's temperature.

This adds enormous amounts of greenhouse gases to those naturally occurring in the atmosphere, increasing the greenhouse effect and global warming.

Global warming

2011-2020 was the warmest decade recorded, with global average temperature reaching 1.1°C above pre-industrial levels in 2019. Human-induced global warming is presently increasing at a rate of 0.2°C per decade.

An increase of 2°C compared to the temperature in pre-industrial times is associated with serious negative impacts on to the natural environment and human health and wellbeing, including a much higher risk that dangerous and possibly catastrophic changes in the global environment will occur.

For this reason, the international community has recognised the need to keep warming well below 2°C and pursue efforts to limit it to 1.5°C.

Greenhouse gases

The main driver of climate change is the greenhouse effect. Some gases in the Earth's atmosphere act a bit like the glass in a greenhouse, trapping the sun's heat and stopping it from leaking back into space and causing global warming.

Many of these greenhouse gases occur naturally, but human activities are increasing the concentrations of some of them in the atmosphere, in particular:

carbon dioxide (CO₂)

methane

nitrous oxide

fluorinated gases

CO ₂ produced by human activities is the largest contributor to global warming. By 2020, its concentration in the atmosphere had risen to 48% above its pre-industrial level (before 1750).

Other greenhouse gases are emitted by human activities in smaller quantities. Methane is a more powerful greenhouse gas than CO ₂, but has a shorter atmospheric lifetime. Nitrous oxide, like CO ₂, is a long-lived greenhouse gas that accumulates in the atmosphere over decades to centuries. Non-greenhouse gas pollutants, including aerosols like soot, have different warming and cooling effects and are also associated with other issues such as poor air quality.

Natural causes, such as changes in solar radiation or volcanic activity are estimated to have contributed less than plus or minus 0.1°C to total warming between 1890 and 2010.

Causes for rising emissions

Burning coal, oil and gas produces carbon dioxide and nitrous oxide.

Cutting down forests (deforestation). Trees help to regulate the climate by absorbing CO₂ from the atmosphere. When they are cut down, that beneficial effect is lost and the carbon stored in the trees is released into the atmosphere, adding to the greenhouse effect.

Increasing livestock farming. Cows and sheep produce large amounts of methane when they digest their food.

Fertilisers containing nitrogen produce nitrous oxide emissions.

Fluorinated gases are emitted from equipment and products that use these gases. Such emissions have a very strong warming effect, up to 23 000 times greater than CO₂.

Countering climate change

Asevery tonne of CO ₂ emitted contributes to global warming, all emissions reductions contribute to slowing it down. In order to stop global warming completely, CO ₂ emissions have to reach net zero worldwide. In addition, reducing emissions of other greenhouse gases, such as methane, can also have a powerful effect on slowing global warming - especially in the short term.

The consequences of climate change are extremely serious, and affect many aspects of our lives. Both countering climate change and adapting to a warming world are top priorities for the EU.We need climate action now. Find out about what the EU is doing to fight the climate crisis.

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Burning fossil fuels, cutting down forests and farming livestock are increasingly influencing the climate and the earth's temperature.

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Scenarion för Västernorrland

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IPCCs femte huvudrapport (AR5) har nu presenterats och med den helt nya klimatmodellberäkningar på hur framtidens klimat kan komma att utvecklas. Dessa modellberäkningar grundar sig på en ny uppsättning scenarier som beskriver fyra olika utvecklingsbanor för framtida koncentrationer av växthusgaser i atmosfären. Dessa utvecklingsbanor går under benämningen Representativa koncentrationsutvecklingsbanor eller RCP:er (från engelskans representative concentration pathways).

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Vår klimatpåverkan är i snitt 9 ton per person och år i Sverige, vilket är avsevärt högre än det globala genomsnittet. Parisavtalets mål är att hålla den globala medeltemperaturökningen väl under två grader och sträva mot att hålla temperaturökningen under 1,5 grader. Det innebär att de genomsnittliga globala utsläppen bör vara högst 1 ton per person 2050.

Vad vi som privatpersoner gör är en viktig faktor för att minska de globala utsläppen av växthusgaser.

Alla val som vi gör är viktiga och kan bidra till att vi minskar våra utsläpp. Om du känner att just dina val inte påverkar tillräckligt mycket och känns oviktiga - tänk om! Hur du agerar kan påverka att andra gör mer klimatsmarta val. Genom att resa med flyg mindre ofta, förändra våra matvanor och köpa begagnat istället för nytt gör stor klimatnytta och kan minska din klimatpåverkan med flera ton per år.

De sista 2-3 ton utsläppen per person är svåra att påverka själv eftersom de är utsläpp som härrör till exempel från byggande av infrastruktur, konsumtion inom den offentliga sektorn.

Ansvaret för att minska utsläppen ligger inte endast på konsumenten utan delas även med den offentliga sektorn och näringslivet.

Våra resvanor har störst klimatpåverkan

Flygresor är för många den största källan till utsläpp. Att resa mer sällan och vara borta längre när man väl reser är ett sätt att minska sin klimatpåverkan. Till exempel släpper en tur-och-retur-resa till södra Spanien ut cirka 1 ton per resenär (inklusive höghöjdseffekten). Att resa mer sällan, välja en kortare resväg och vara borta längre när man väl reser är sätt att minska sin klimatpåverkan.

På din flygbiljett kan du se din klimatpåverkan eller så kan du använda ICAOs flygutsläpp kalkylator för beräkna dina utsläpp. Observera att dessa utsläpp inte inkluderar den så kallade höghöjdseffekten vilket innebär att dina utsläpp troligen är ungefär dubbelt så stora under längre flygresor. Detta då utsläpp på hög höjd ger upphov till kondensstrimmor och bildandet av kväveoxider som också har en klimatvärmande effekt.

Att välja tåget eller resa kollektivt är alltid bättre jämfört med en fossildriven bil. Om du behöver ta bilen så är det bättre att samåka och välja en miljövänlig bil. En medelstor bil släpper ut i snitt nästan 2 kg växthusgaser per mil. Om en familj reser ungefär 2000 mil per år motsvarar det 4 ton per år.

Du kan beräkna dina utsläpp av växthusgaser och jämföra dem med andra transportslag i beräkningsverktyget nedan.

Vad vi äter spelar stor roll för klimatet

Att minska vår konsumtion av vissa livsmedel såsom rött kött har stor betydelse för att få ner våra utsläpp. Idag orsakar endast konsumtionen av kött knappt ett ton utsläpp per person och år i genomsnitt i Sverige.

Klimatpåverkan i genomsnitt för olika livsmedel

Livsmedelverket rekommenderar att vi inte äter mer än 500 gram rött kött per person och vecka av hälsoskäl, idag äter vi ungefär 600 gram i genomsnitt. Att kunna säga hur mycket kött du kan äta en genomsnittlig vecka för att du ska komma under en total klimatpåverkan på två ton beror på hur mycket man reser och konsumerar i övrigt.

Det enkla svaret är att vi behöver äta mindre rött kött och ost. Att minska konsumtionen av rött kött är en effektiv åtgärd för att minska vår klimatpåverkan och speciellt viktig då det är svårt att minska utsläppen från produktionen. Exakt hur mycket som är hållbart för att klara klimatmålen är svårt att säga då det beror på hur mycket man släpper ut i övrigt.

Även om köttkonsumtionen måste minska för klimatets skull så har betande djur även en positiv påverkan på miljön, exempelvis genom ökad biologisk mångfald.

Hur vi väljer att bo påverkar klimatet

Att välja att bo på mindre yta och att byta till en mer klimatsmart uppvärmning kan ha en stor skillnad beroende på hur ditt nuvarande boende ser ut.

Du kan även påverka genom att energieffektivisera ditt boende. Isolera fönster och dörrar för att minska uppvärmningsbehovet.

Du kan minska din energianvändning genom att sänka inomhustemperaturen något. Hushålla med varmvatten och använd diskmaskin istället för att diska under rinnande vatten. Tvätta dina kläder i lägre temperatur och häng din tvätt istället för att torktumla. Håll rätt temperatur i dina vitvaror, minus 18 grader i frysen och plus fem grader i kylen.

Om du behöver nya möbler, apparater och elektronik tänk på att återbruk och köp av begagnat alltid är bättre än att köpa nytt. Reparera istället för att byta ut dina vitvaror och byt ut gamla vitvaror mot mer energieffektiva när det väl är dags.

Hur den el som du använder har producerats påverkar också klimatet. Här kan du välja att köpa miljömärkt el från förnybara källor eller kanske investera i egen produktion av förnybar el genom att installera solceller på taket.

Svensk konsumtion av textilier bidrar till negativ miljöpåverkan

Att producera textilier och kläder orsakar inte bara utsläpp av växthusgaser utan orsakar även miljöpåverkan genom användning av stora mängder råvaror, kemikalier och vatten samt utsläpp till mark och vatten vid odlingen av t.ex. bomull. Svenskarnas klädinköp gav 2017 en klimatpåverkan som motsvarar 4,2 miljoner ton koldioxidekvivalenter (CO2-eq) totalt över hela livscykeln. En fördubblad livslängd av alla textila produkter skulle kunna minska klimatpåverkan till nästan hälften.

Varje svensk konsumerar runt 14 kilo textil per person och år. Sedan 2000 har konsumtionen ökat med 3,5 kilo. Den ökade konsumtionen bidrar till flera miljö- och hälsoproblem. Eftersom de faktiska miljökostnaderna inte är inkluderade i priserna på textilen och arbetskraften billig är tillgången på billiga textilier hög. Följden blir hög konsumtion och låga incitament att återanvända och återvinna. Kläder produceras som inte håller länge, eller slängs fast de är hela, vilket betyder att resurser förbrukas i snabb takt och miljöutsläppen blir onödigt höga. Det är också svårt för konsumenter att bedöma vad som är bra respektive dåliga produkter.

Klimatkompensera som en sista utväg

Det är alltid bättre att undvika att ge upphov till utsläpp om du vill minska din klimatpåverkan. Men om det inte går så kan du klimatkompensera. Att klimatkompensera innebär att du betalar för en minskning av en angiven mängd utsläpp någon annanstans eller att mängden utsläpp tas upp i skog och mark. Därmed kan du kompensera för utsläppen från till exempel en flygresa eller hela din livsstil. Eller varför inte ge bort det som present!

Det finns olika sätt att klimatkompensera. Det vanligaste är att köpa en utsläppsminskning som skapats genom klimatkompensationsprojekt, ofta i utvecklingsländer. Typiska projekt kan exempelvis vara energiåtgärder där fossila bränslen ersätts med förnybar energi, till exempel genom att byta ut till renare spisar eller genom att installera solpaneler. Andra projekt kan syfta till att öka upptaget och lagringen av koldioxid genom exempelvis trädplanteringar.

För att säkerställa att utsläppsminskningen är verklig bör projekten vara certifierade enligt någon av de standarder som har utvecklats. Gold Standard är ett exempel på en certifiering som har utvecklats av miljöorganisationer och som ställer höga krav på både säkerställda utsläppsminskningar och hållbar utveckling.

Våra besparingar är viktiga för klimatomställningen

Hur du sparar dina pengar påverkar också klimatet. I stor utsträckning bidrar våra besparingar idag inte till den omställning som krävs för att nå klimatmålen. Oavsett om du har pengar på ett sparkonto, i en fond eller i aktier i ett företag, kan dina pengar vara till nytta för klimatet. Det kan vara värt att se över vilka investeringar som du har och välja fonder, aktier eller en bank som har en bra klimatprofil. Du kan också ta en aktiv roll för att påverka hur dina fonder, företagen du investerar i eller din bank agerar.

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Vår klimatpåverkan är i snitt 9 ton per person och år i Sverige, vilket är avsevärt högre än det globala genomsnittet. Parisavtalets mål är att hålla den globala medeltemperaturökningen väl under två grader och sträva mot att hålla temperaturökningen under 1,5 grader. Det innebär att de genomsnittliga globala utsläppen bör vara högst 1 ton per person 2050.

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Ja, mobilappen Aero (iOS) och datorprogrammet Aero har liknande kapacitet vad gäller funktionalitet. I mobilappen Aero (iOS) går det att skapa AR i AR med den extra kontextvyn man får tack vare den framåtriktade kameran. Datorprogrammet Aero ger ingen realtidsvy med AR i AR eftersom det inte finns någon framåtriktad kamera på de flesta datorer.

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Tillsammans gör vi skillnad! Sedan 2006 har vi tillsammans förhindrat över 1 miljon ton CO2e att släppas ut i atmosfären! SÄNK MINA UTSLÄPP KLIMATKOMPENSERA NU Beräkna och klimatkompensera

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Start building

Watch Meta Spark's best-in-class courses to build your first AR experience and learn AR fundamentals. If you're ready to become an expert, our Meta Spark AR Pro curriculum helps you become a certified Meta Spark Creator.
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Meta Spark Studio - Create Immersive AR Experiences

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Create immersive experiences with Meta Spark Studio, reaching billions of people through Meta's family of apps and devices.

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Det finns stora problem med utsläpp och hur det påverkar temperaturen och dess följder. Allt från textilindustrin, till plast och resor påverkar detta och gör att våra klimatförändringar förändras drastiskt. Det finns många sätt att kunna bidra för att själv sänka sitt klimatavtryck i dagsläget, både via att kompensera med pengar till organisationer eller genom att själv ändra sina beteenden.

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För att kunna minska riskerna behöver användaren kunna se hur det påverkar dem i sitt närområde för att få ett så kallat wake up-call. Detta kan göras genom att genom ett foto/video kunna se hur det skulle påverka med till exempel förhöjda temperaturer som i sin tur leder till torka och bränder.

Fördelen med det ska vara att kunna se värsta tänkbara scenario, och se hur det förändras om man väljer att sänka sitt avtryck.

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För användare mellan 20-40 år som vill kunna se hur deras beteenden påverkar klimatet.

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Användaren kan i samband med olika gärningar se hur det påverkar risken med klimatförändringarna. Den utgår från dystopi och visar värsta tänkbara resultat i området med bränder, torka, osv för att sedan visa hur det minskar eftersom om användaren väljer att inte ta bilen, eller att inte köpa nyproducerat till exempel.

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Tjänsten är tänkt att användas i samband med olika ageranden, oavsett om det gäller kost, resor eller liknande för att se hur deras beteende kan påverka omgivningen.

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En app som kan visa användaren hur deras närområde ser ut vid en dystopi, och sedan hur det påverkas positivt eller negativt beroende på hur de agerar.

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Inför skapandet av de 3 prototyperna har jag tagit mitt koncept och delat upp det i olika specifierade delar för att kunna hitta vad jag kan jobba vidare med gällande detaljer, interaktion och filterdimensioner för att se vad jag kan jobba vidare på.

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Detalj: Utsläpp vs möjligheter.

Att kunna se hur mycket utsläpp jag eller mitt hushåll skapar i dagsläget per år för att få ett perspektiv över hur jag lever gentemot normen, samt se vad jag kan "spara in" utsläpp på.

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Varianter på hur själva appen ska se ut och fungera, AR-funktion ska visas tydligt med hjälp av hur omgivningen förändras, eventuellt med hjälp av foto/video som redigeras till

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Pappersprototyp rörande AR-funktionen för att se hur reaktioner/känslor upplevs när de får se hur den ska agera.
Låg filterdimension med fokus mestadels på utseende och funktionalitet

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Prototyp med fokus på implementation med medel filterdimension för att kunna se hur lättförståeligt konceptet är.

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Look & Feel prototyp för att utvärdera och testa närmare en slutgiltig helhet och se så alla delar fungerar ihop.

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10 Screens, Published on Mar 08, 2020 21:18 GMT

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Att skapa prototyper med låg filterdimension  blir en möjlighet för utforskande testning, medan prototyper med hög filterdimension fokuserar på designutvärdering för att se så att de tidigare skisserna och funderingarna blir en bra helhet (Lim, Stolterman & Teneberg, 2008, sid 7:5).

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Det finns så många delar att arbeta med i en prototyp, detaljmässigt så som helhet. Detta gör dock att det kan bli svårt att hålla det hela divergent i början och inte börja snöa in sig på saker direkt och arbeta enbart med konvergens för mig som designer. Att tvingas arbeta divergent är någonting jag behöver och utvecklas mycket av men många gånger är det svårt att hitta ett sätt att arbeta "utåt" och få det att bli större så man får mer att gå på.

Det har dock gett många lärdomar i det här arbetet att hela tiden se över hur folk runt mig reagerar när de får se arbetet, hur mycket de förstår och vad de tänker för att se om det är rätt riktning på det hela. En sak jag där också måste lära mig är att dela med mig till andra klasskamrater/designers för att få deras syn på det och inte enbart från folk som inte har bakgrundskunskapen som jag har från utbildningen. Samma sak med att uppdatera digitalt på Padlet, att göra det till en naturlig del att spara alla tankar och inspiration och inte snöa fast mig i det "analoga" där jag skriver och skissar för hand hela tiden istället för att lägga in det digitalt direkt så det finns tillgängligt och är lättsamt att dela för feedback.

Mitt arbete kan defintivt förbättras på många plan, men den största delen för mig är att kunna dela med mig oftare och bredare, samt att tvinga mig själv till divergens och att utöka designen och möjligheterna för att hitta nya vägar då det så lätt blir att gå från punkt A till B än att ha flera olika startpunkter att jobba från för att komma till en slutprodukt.

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- Bra tankeställare
- Kommer den alltid ta bort skog eller kommer den ändra andra saker med?
- Enkelt
-Skulle vilja förstå hur den fungerar i andra områden med

Slutsatser: En bra start men möjligheten att testa/visa fler områden och vad som händer rent naturmässigt skulle vara en klar fördel för att folk ska kunna känna igen sig och hur deras område påverkas och inte enbart skogsområden.

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- Enkel och överskådlig
- Plats kanske inte behövs då man ändå tar sin egen bild/video av området
-Skulle vilja se valen man kan göra tydligare
-Kan man välja permanenta val av de dagliga?

Slutsatser: Se över om platsen behövs eller inte, platsen skulle isf behöva kopplas till andra personers bilder från närområde om man till exempel inte är utomhus för att kunna testa själv på plats.
Eventuellt kunna flytta valen från en lista, till permanent, eller dagligt. Men att grunden är densamma.

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- Smidig och lätt att navigera i
- Kan man klimatkompensera till något specifikt?
-Vill kunna se förbättring i AR när jag gör bättre dagliga val.

Slutsatser: En vidareutveckling på denna applikation hade inneburit att arbeta vidare på de val man kan göra dagligen/permanent för att detta ska påverka även AR, och inte bara hur mycket ens snitt blir i utsläpp per år.
Välja klimatkompensation till någonting specifikt som man själv vill gynna.
Men också att kunna jobba vidare på AR så den kan visa andra platser och hur det ser ut trots att man inte befinner sig där, för att se hur det skiljer sig i världen.
Utöver detta behöver AR-upplevelsen jobbas fram i ett AR-verktyg.

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Arbetet med prototyperna går framåt och det visar olika tankeställningar från både mig som designer och vad som går att jobba framåt i processen samt från de som fått göra användartester.

Grunden är bra och genomförbar, men en fundering kring hur AR-funktionen blir mer anpassningsbar för olika områden finns och hur detta ska gestaltas. Dock ett senare problem.

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Konceptet är både svårt och enkelt under tiden. Då det är väldigt specifikt riktat mot torka och bränder pga klimatförändringar för det att det är enkelt att fokusera på kompensation eller personliga åtgärder, men samtidigt gör det också AR-funktionaliteten lite svår.

Hur man ska kunna jobba med konceptet överlag och få det hållbart och intressant blir en utmaning, då inte alla har samma typ av område de kan "påverka" med AR. Och hur det i sig ska gå att leverera funktionaliteten är också en fråga, kanske går det bara att lägga till eldsvådor, men inte alterera verkligheten med att påverka med torka eller ta bort växtlighet.

Trots att ingen feedback kommit om att de vill se hur mycket de personliga valen ändrar i utsläpp så kan det vara en bra sak att ha tagit med för att få en typ av indikator för användaren hur pass bra det är att ta beslutet med en köttfri måltid till exempel. Att den stora påverkan det har i sin tur kan påverka användaren att tänka till, och inte bara att de reggar sina olika delar.

Även om feedback generellt är bra så hade jag velat testa olika varianter av prototyper och se hur det påverkar valen, mer än att testa olika filterdimensioner. Men begränsning med tid och liknande gör att det mestadels hamnar i tankestadiet.

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Min designprocess har under tidens gång, och framförallt efter feedback tagit nya former.

Från en början så utgick jag från den information jag tagit fram i forskningsarbetet där jag kontrollerat vilka möjligheter till olika dystopier kan påverka framtiden, och därefter konvergerat mot ett specifikt område som då föll på skogsbränder. Då detta var ett relativt nytt område för mig utöver den information man sett i media under de senaste somrarna så har jag valt att jobba vidare in på just den lättillgängliga informationen som finns angående detta, men utforskade samtidigt möjligheterna kring vilken åtgärd man själv kan göra. Detta blev ett stort arbete för att kunna se hur man lever idag, vad det ger för utsläpp, så har jag gjort massa olika tester själv som finns ute för att se vad jag själv har för utsläpp och vad klimatmålen ger för förslag på förändringar för individen. Detta arbete blev väldigt divergent och jag har därefter valt att välja ut vilka delar jag själv anser som applicerbara på individnivå och dessutom valt att jag vill ge möjligheten att kompensera direkt i tjänsten.

Efter detta påbörjade jag ett prototyparbete där jag testade några olika idéer för att se hur det fungerar främst att interagera med, och hur jag vill få användaren att skapa en relation med tjänsten och inte bara för att testa en AR-funktion och sedan inget mer. Prototyperna i detta läge föll på att göra wireframes och att visualisera digitalt hur AR skulle visa skillnaden i sitt närområde baserat på sina val, samt en klickbar prototyp för att se hur interaktionen fungerar och vad det ger för feedback.

Efter detta var det dags att presentera det jag fått fram hittills och det gav då en möjlighet att få åsikter och tankar från lärare och medstudenter i min grupp. Det blev inte väldigt mycket information eller feedback just där förutom från Michael som gav förslag på "vykort från framtiden" som ett koncept man kan jobba med.

Detta har jag tagit i beaktning för nästa revidering och det arbete som fortgår nu samtidigt som min komplettering, och brainstormat kring hur det ska kunna ge AR-funktionen en möjlighet till att göra det mer intressant. Detta gjorde att idéfasen och utforskning startade om igen. Under tiden skapade jag samtidigt en AR-prototyp för att testa just AR-funktionen, vilket gav mig insikt i att "bara" eldsvådor kommer inte att ge en fullkomlig upplevelse för användaren då det inte blir tillräckligt med information om vad som händer.

Detta tillsammans gör att utforskningen börjar om till viss del, och jag har för kommande del gått tillbaka och tar med mig all feedback och prototyperna och vad som fungerar och inte till början av min "double diamond". Detta har gett en ögonöppnare kring att det måste bli mer av en delningsmöjlighet, konceptet "vykort från framtiden" kan appliceras på att skicka det till nya användare som inbjudan att testa tjänsten där en person skickar vidare sin dystopi som ett MMS med inbjudan till tjänsten, samt att AR-funktionaliteten behöver utvecklas, med mer information och visa upp i AR direkt olika faror och risker med skogsbränder i området man befinner sig i, och eventuellt se hur det går att AR ska känna av och markera utsatta områden.

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1.

Enhancing User Experiences of Mobile-Based Augmented Reality via Spatial Augmented Reality: Designs and Architectures of Projector-Camera Devices

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Journal Homepage
Journal Title: Advances in Multimedia

ISSN: 1687-5680 (Print); 1687-5699 (Online)

Publisher: Hindawi Limited

LCC Subject Category: Science: Mathematics: Instruments and machines: Electronic computers. Computer science

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

AUTHORS

Thitirat Siriborvornratanakul (Graduate School of Applied Statistics, National Institute of Development Administration (NIDA), 118 Seri Thai Rd., Bangkapi, Bangkok, Thailand)

Abstract | Full Text

As smartphones, tablet computers, and other mobile devices have continued to dominate our digital world ecosystem, there are many industries using mobile or wearable devices to perform Augmented Reality (AR) functions in their workplaces in order to increase productivity and decrease unnecessary workloads. Mobile-based AR can basically be divided into three main types: phone-based AR, wearable AR, and projector-based AR. Among these, projector-based AR or Spatial Augmented Reality (SAR) is the most immature and least recognized type of AR for end users. This is because there are a small number of commercial products providing projector-based AR functionalities in a mobile manner. Also, prices of mobile projectors are still relatively high. Moreover, there are still many technical problems regarding projector-based AR that have been left unsolved. Nevertheless, it is projector-based AR that has potential to solve a fundamental problem shared by most mobile-based AR systems. Also the always-visible nature of projector-based AR is one good answer for solving current user experience issues of phone-based AR and wearable AR systems. Hence, in this paper, we analyze what are the user experience issues and technical issues regarding common mobile-based AR systems, recently widespread phone-based AR systems, and rising wearable AR systems. Then for each issue, we propose and explain a new solution of how using projector-based AR can solve the problems and/or help enhance its user experiences. Our proposed framework includes hardware designs and architectures as well as a software computing paradigm towards mobile projector-based AR systems. The proposed design is evaluated by three experts using qualitative and semiquantitative research approaches.

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Information about the open-access article 'Enhancing User Experiences of Mobile-Based Augmented Reality via Spatial Augmented Reality: Designs and Architectures of Projector-Camera Devices' in DOAJ. DOAJ is an online directory that indexes and provides access to quality open access, peer-reviewed journals.

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The Motivation of Technological Scenarios in Augmented Reality (AR): Results of Different Experiments

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Facultad de Ciencias de la Educación, Universidad de Sevilla, C/ Pirotecnia, S/N, 41013 Sevilla, Spain

Facultad de Educación, Universidad de Alicante, Campus Sant Vicent del Raspeig, 03080 Alicante, Spain

Author to whom correspondence should be addressed.

These authors contributed equally to this work.

Received: 19 June 2019 / Revised: 12 July 2019 / Accepted: 17 July 2019 / Published: 19 July 2019

Augmented Reality (AR) is an emergent technology that is acquiring more and more relevance in teaching every day. Together with mobile technology, this combination arises as one of the most effective binomials to support significant and ubiquitous learning. Nevertheless, this binomial can only prove valid if the student is motivated to use it during the learning process. An attempt was made through the implementation of Keller's Instructional Material Motivational Survey model o determine the degree of motivation of Pedagogy, Medicine and Art students from the University of Seville for using AR-enriched notes available by means of mobile devices in the classroom. Three applications designed for the subjects of Educational Technology, Anatomy and Art served to assess it positively in terms of the motivation raised by the participation in the experiment, as well as regarding academic performance improvement. It can additionally be stated that our main finding was a link between students' motivation to use the enriched notes and the performance obtained in the subject in which they use them. Evidence was also found that the utilization of Augmented Reality benefits the learning process.

1. Introduction

Augmented Reality (AR) is becoming an emergent technology with great potential in various and largely different fields of application, such as geography, with regard to watershed control [ 1], medicine, cultural heritage [ 2] or industry [ 3]. It is likewise being used in the educational context [ 4, 5, 6, 7, 8, 9], amongst other reasons because AR permits easy access to information-most often through mobile devices. It must be remembered that these devices are highly present in the life of university students all over Latin America [ 10, 11].

According to Azuma [ 12], AR can be defined as a system with the following characteristics: it combines real and virtual; it is interactive in real time, and it is registered in 3D. AR technology consequently allows us to combine digital and physical information in real time by means of different technological media such as tablets, smartphones [ 13] or Augmented Reality glasses [ 3], to quote but three, thus providing the chance to create a new reality [ 14]. "Augmented reality visibly introduces the knowledge that students need to learn inside its real environment" [ 15].

Such a mixed integration of physical and digital reality may take place at various levels, ranging from the utilization of QR codes, the use of images, the exploitation of 3D objects and the Global Positioning System (GPS)-mediated mobilization of coordinates to thermal footprints [ 6]. Furthermore, one can classify AR systems depending on their location or according to images. "Location-based AR systems take advantage of the data about the position of mobile devices, determined by the Global Positioning System (GPS) or other Wi-Fi-based positioning systems... Instead, image-based AR focuses on image recognition techniques that serve to identify the position of physical objects in a real environment, so that the virtual contents related to such objects can be properly situated" [ 16].

The relevance of AR for learning has to do:

(a) with its specific characteristics: being a mixed reality, it offers the possibility for a real-time integration both of different information layers and of the various formats where that information may appear (texts, URLs, videos...). Furthermore, as an interesting, user-friendly technology, it allows us to enrich or alter the information about reality, adding further contents to it [ 6]; and (b) with the fact that the devices most frequently used for its observation, e.g., smartphones, are technologies within easy reach of university learners.

Numerous authors [ 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28] have highlighted that AR utilization opens a wide range of possibilities for us, namely: (1) removing information that might hinder the assimilation of significant information by students; (2) increasing or enriching the information about reality so that it can be more easily understood by students; (3) being able to observe an object from different points of view, thus enabling the student to select the time and place of observation; (4) fostering ubiquitous learning; e) creating safe "artificial" scenarios, such as laboratories or simulators; (5) enriching the materials printed for students with additional information in different formats; (6) turning students into "proconsumers" of learning objects in AR format; and (7) achieving an improved learning through its use. Furthermore, AR can be used in a variety of disciplines and at different educational levels [ 29, 30], even though university learning admittedly stands out as the context where it appears the most often.

As for its impact on education, the number of research studies carried out so far is still limited-even through that number is gradually increasing [ 31]. This possibly constitutes one of the most serious problems for its incorporation into teaching. Some aspects have been stressed in those works, however: that students show favorable attitudes towards AR and that its utilization enhances motivation towards learning [ 32, 33]; that it helps create a constructivist learning context [ 16, 34]; that it favors an active teaching environment [ 35]; that it reduces the cognitive load to which students are subjected in learning tasks [ 36, 37]; that students have a high degree of acceptance as well as positive attitudes in relation to AR technology [ 38, 39, 40, 41, 42, 43]; that it increases satisfaction levels amongst both students and teachers [ 24, 44, 45, 46]; that it improves students' spatial skills and orientation [ 47, 48]; and finally, that its use enhances academic performance [ 26, 30, 49]. Additionally, it deserves to be highlighted that experiments are already being carried out for its incorporation into e-learning-based learning initiatives [ 31, 50], as well as into others where students act as learning object producers [ 51, 52].

In any case, we must bear in mind that the incorporation of AR into teaching faces a number of obstacles: this is a new technology; no development-focused educational experiments have been carried out yet; no learning resources and objects produced in AR format exist; teachers have a poor training; teachers need to develop positive attitudes towards AR incorporation into educational practice; no educational experiments have so far been performed in the development of AR-based learning objects; screens do not offer a suitable observation size; there are no conceptual frameworks which can help us find innovative practices in AR implementation; support centers for teachers need to be created so that they will find it easier to produce AR-based objects, as well as their maintenance in servers; a base/basic technology must be available for its observation by students; and an information overload clearly exists [ 28, 52].

Concerning the last obstacle mentioned above, some precautions should be adopted when designing AR objects. Modules must have short and direct contents and be flexible as well as straightforward. They have to reflect an accessible and tolerant approach with a multimedia design, oriented to the student's action and involvement. Tablets and smartphones do not constitute the goal of the educational process, and therefore, the methodology must focus on practicality and interactivity and adapt to the diversity of devices where it can be observed [ 53].

This paper shows an experiment carried out within a university context, additionally providing a detailed description of the AR products generated. This serves as the starting point to explain the educational research performed, where the Instructional Material Motivational Survey (IMMS) developed by Keller was used as the assessment tool [ 4]. The paper finishes with a summary of the results obtained as well as the corresponding conclusions drawn from them.

2. Materials and Methods

2.1. Research Aims

The different research works described below pursued the following objectives:

Ascertaining the degree of motivation measured by means of Keller's IMMS model and the dimensions shaping it (attention/relevance/satisfaction) that the utilization of AR objects generated amongst students.

Analyzing if the degree of motivation (attention/relevance/satisfaction) measured using Keller's IMMS model influenced students' knowledge acquisition.

Below can be found the hypotheses that we pose in this study:

H0 (null hypothesis): There are no significant differences, with a 0.05 alpha risk of us being wrong with regard to the scores achieved by students in the pre-test and the post-test.

H1 (alternative hypothesis): There are significant differences, with a 0.05 alpha risk of us being wrong with regard to the scores achieved by students in the pre-test and the post-test.

2.2. Research Design

It must be stressed from the very beginning that the research was replicated with AR objects specifically made for investigation purposes that belonged to three different areas of knowledge: Educational Sciences; Fine Arts; and Medicine.

Two types of single-case pre-experimental design were used: one intended to analyze the relevance that the interaction with subjects had for the degree of motivation raised, which used a post-test measure; and the other referred to performance, which revolved around the analysis of academic performance and included pre-test and post-test measures [ 54, 55]. More precisely, a treatment (the AR object created) was administered in both designs, complemented with a measurement (the student's motivation to develop the experiment and to improve academic performance).

The experiment took place following the same sequences in all cases:

Explaining to students what AR was all about;

Showing the different objects produced and their possibilities;

Specifying the website where they could download the different guides and apps for the objects produced;

Encouraging students to work on their own using their mobile devices with the different objects; and

Completing questionnaires

2.3. The Sample

All the students involved in the experiment were enrolled in degrees in Medicine (Anatomy) and Pedagogy (Educational Technology), and in the master's degree in "Art: ideas and production" -all of them taught at the University of Seville- Table 1. The samples had a non-probabilistic nature; in other words, they were "convenience" or "causal" samples [ 56, 57] determined by the researcher's ease of access to the subjects making up the sample in question.

It is worth highlighting that the experiment with the Art group took place after a three-hour-long interaction of students with the learning objects, as opposed to the one-week-long interaction period in the other two groups.

2.4. The Objects Produced

The experiment involved three AR objects specifically developed for this study and referred to contents associated with (a) the discipline of Educational Technology: "ways to use video in teaching-learning processes"; (b) Medicine (more precisely, Anatomy); and (c) the field of Fine Arts. A program guide prepared for students told them where they had to download the apps which allowed for the operation of the objects as well as the actions that they could perform with those objects. This was all complemented with a reference bibliography for students to delve deeper into each thematic area.

The three aforementioned objects had different typologies; those related to contents framed within "Educational Technology" (educational roles played by video use) and "Anatomy" can be categorized as belonging to Type II [ 6] in the format of "notes enriched" with AR objects, transferring the former with a video resource, and the latter with a 3D object that the student could manipulate ( Figure 1 shows images of the object created for the "Educational Technology" subject).

Four AR objects were created for the "Anatomy" area, namely: Coxofemoral2; Shoulde2r; Ankle2; and Cervical2. All of them shared the following features: 3D animations; stop and monitoring of the object; enlargement of the size of the component presented so that details become visible; and transfer to an information extending video located on YouTube ( Figure 2).

The final object produced can be classified as belonging to the "Mixed Reality" category due to the combination of elements from both "Augmented Reality" and "Virtual Reality." It shows the Church of the Annunciation in Seville, which belongs to the University of Seville, but initially was the "Professed House of the Company/Society of Jesus". This church dates back to 1565, and its façade stands out as one of the masterpieces of Sevillian Mannerism. The application developed allows the user to view-on a marker-3D models of the sculptures that Martínez Montáñez made of San Ignacio de Loyola and San Francisco de Borja. These figures, sculpted between 1610 and 1624, are located at the Main Altarpiece of this church. Users can likewise have access to a gigapixel photograph of the Altarpiece, where an extremely high zoom allows them not only to look at every single detail but also to experience a 360º viewing of the central nave, the entrance, and the crossing. They can also watch a video filmed with the help of a drone that shows the interior of the central nave ( Figure 3).

Most importantly, the material generated in this case included a series of questions designed to find out if students had paid attention during their interaction ( Figure 4). A final result corresponding to the number of questions posed appears at the end of the questionnaire.

We can classify the material produced on this occasion as: Level-2 AR; Immersive virtual reality; 360° vision; 3D modelling; incorporation of audio and gigapixel which helps enlarge the image ( Figure 5).

The production of these objects was possible thanks to the use of the different software programs listed in Appendix A, which also specifies the type of use given to each one of them. As can be seen, we used a variety of programs, including Metaio Creator, Eclipse and Ffmpeg, since the production of the respective objects required some specific characteristics. It deserves to be highlighted that all the programs were prepared in such a way that they could work on Android as well as on iOS devices. Appendix B contains the URLs which feature the videos showing the operation of objects and where the apps can be downloaded.

2.5. Information Collection Instruments

The basic tool to diagnose the degree of motivation raised by the participation in the experiment was the Instructional Material Motivational Survey (IMMS), developed by Keller (2010).

According to Keller [ 4], motivation "refers to the magnitude and direction of behavior. In other words, it has to do with the choice made by an individual in relation to what they experience or the goals that they will try to achieve or avoid, and the degree of effort that they will make for that purpose. Motivation depends on internal looks and external characteristics. Individuals respond to their context on the basis of internal reflections, impulses, perceptions and goals, and according to the perceived base and the real opportunities, together with the reinforcement of the external context". ( Figure 6). In turn, Cheng [ 41] stresses that this is an internal state or condition which leads us to action, which guides us and persists in our behavior, involving us in certain activities. It also refers-in the classroom context-"to subjective experiences, particularly students' predisposition to take part in class activities and their reasons to do so" [ 41].

Keller used a number of different works [ 1, 58, 59] to formulate his ARCS model for the motivation applied to instructional design. He sees motivation in instructional contexts as being determined by the interaction of four dimensions: attention (A); relevance (R); confidence (C); and satisfaction (S) ( http://www.arcsmodel.com/).

For Keller [ 59], Attention includes human characteristics such as orientation reflection, curiosity and the search for sensations. Relevance refers to those things which a person perceives as a tool to meet needs and to achieve personal Satisfaction, including the fulfilment of personal objectives. Confidence comprises several motivational constructs. Finally, Satisfaction constitutes the final step that will allow us to maintain the motivation to learn.

The model illustrates how "a person's curiosity ("attention") and the motives or values ("relevance"), combined with their hope of success ("confidence") will determine which objectives acquire the highest prominence, consequently resulting in an intentional effort to reach the goal. Environmental influences such as teacher enthusiasm and social values, instruction quality, clear expectations and the availability of resources will also have some bearing on an objective effort and orientation towards some degree of fulfilment. "Individuals are thus influenced by their own knowledge, capacity and skills too" [ 1]. Therefore, this model assumes that, if the instruction strategy is considered useful to achieve the goals set, then that strategy acquires importance for students [ 60], and they will be likely to learn the contents and to acquire the skills.

Keller's IMMS consists of 36 items which collect information about four dimensions: attention (12 items); confidence (9 items); relevance (9 items); and satisfaction (6 items). The instrument has a Likert-type construction with seven response levels (from 1 = "Extremely unlikely/disagree with" to 7 = "Extremely likely/I agree").

As stressed by Loorbach, Peters, Karreman and Steehouder [ 61], the model formulated by Keller "has been used on countless occasions to implement motivation strategies for instruction materials-and to test their effects. Despite being originally designed to influence students' motivation within a traditional learning environment based on a face-to-face teacher-student interaction, this model has also been applied and thoroughly tested in contexts that require other adjustments, e.g., computer-assisted teaching and virtual or distance education". More specifically, Keller's model proved helpful to check the degree of motivation raised by different technological resources amongst students. Examples are: videogames [ 62]; the inclusion of video in e-learning learning sessions [ 63]; audio podcasts [ 64]; Massive Open Online Courses [ 65, 66]; or AR [ 67, 68, 69].

We obtained the reliability index for this instrument using Cronbach's alpha-one of the most suitable tools to obtain it with Likert-type scales [ 70, 71]. Table 2 provides the values obtained both for the instrument as a whole and for the different dimensions shaping it, in all three applications.

The values reached allow us to state that the instrument has a high reliability level, both globally and in the different dimensions shaping it [ 70, 72]. Only one dimension (Confidence) and one of the experiments (Anatomy) reveal values which can be considered moderate. The scores suggest high reliability indices in the other cases.

As for the tool created to evaluate academic performance, it actually adopted the format of two multiple-choice tests administered under the pre-test + post-test modality. These tests included 15 items which collected information about the three following categories extracted from Bloom's taxonomy: Apply (4 items); Remember (6 items); and Understand (5 items). Both modalities contained the same items in the pre-test and the post-test, albeit in a different presentation order.

3. Results

We present the results of the average scores reached in each of the items of the instrument used. We present the total results and the results of the three experiments in Appendix C.

It becomes visible in most items that the scores obtained exceed the central value of the scale (3.5), which suggests to us that the experiments were positively valued; furthermore, the items receiving the lowest scores from students are those linked to negative attitudes towards the experiment.

As for the means and standard deviations obtained for the instrument as a whole, in its different dimensions, in its different applications, and in the application as a whole, see Table 3.

The average scores obtained tell us that students very positively assessed their participation in this experiment, regardless of the type of AR objects produced and of the knowledge area we were dealing with. They expressed a high level of satisfaction after taking part in the interaction experiment with AR objects. Most importantly, the items which obtained slightly higher scores were the ones which combined AR and VR objects.

The following two hypotheses were posed for the purpose of checking whether the pre-test and the post-test differed significantly in terms of scores obtained:

*

H0 (null hypothesis): There are no significant differences, with a 0.05 alpha risk of us being wrong with regard to the scores achieved by students in the pre-test and the post-test.

*

H1 (alternative hypothesis): There are significant differences, with a 0.05 alpha risk of us being wrong with regard to the scores achieved by students in the pre-test and the post-test.

Seeking to verify H0, a Student's statistical t-test for related samples was applied to the pre-test and post-test scores. This statistical analysis separately focused on the group of students who interacted with the AR object "Video use" and those who did so with "Anatomy". Table 4 shows the Student's t-values reached and their significance level. After applying Student's t-test, we obtained the values of t = 16.107 for "Video use" and t = 17.323 for "Anatomy". These values allow us to reject H0 (null hypothesis) and to accept H1 (alternative hypothesis) at a significance level of p ≤ 0.001 in both cases, which ultimately leads us to conclude that students' participation in the learning experience with AR objects proved useful to acquire the contents offered and, therefore, for them to enhance their learning.

Pearson's correlation coefficient served to verify whether the motivation shown by students and the levels of confidence, attention, satisfaction and relevance assessed by means of IMMS influenced the performance reached after their participation in the AR experiment. The results achieved in both experiments can be seen in Table 5.

It follows from the values obtained that: (a) the relationships between the different variables compared (performance and motivation; attention-confidence-relevance-satisfaction) are direct, i.e., if one of them increases, so does the other; (b) relationships are moderate; and (c) all of them are significant at a 0.05 or lower level.

4. Discussion and Conclusions

A number of conclusions can be drawn from our study:

The diagnostic instrument-Keller's IMMS-appears to be a good predictor to explain the motivation, attention, confidence, relevance and satisfaction raised by the interaction with AR objects. This is true regardless of the specific AR object that they interacted with and of the subject in which the students were enrolled. In this respect, our findings match those obtained in other works carried out by different authors [ 24, 73, 74, 75, 76, 77, 78, 79].

The instrument utilized has a high reliability index, once again showing a coincidence with the results achieved by other authors.

Our first findings clearly suggest that students' interaction with learning objects created by means of AR technology significantly improved the scores obtained in the pre-test that had been administered to them with the aim of checking those students' previous knowledge in relation to the contents presented in the AR objects-regardless of the specific content developed. Therefore, AR arguably constitutes a useful means to facilitate learning and knowledge acquisition. These results also follow along the same lines as the conclusions drawn by previous authors [ 32, 73, 76, 77, 78, 79, 80].

As a limitation faced in this study, it is worth highlighting the impossibility of generalizing the results obtained. Being a pre-experimental design, its results must be adopted with caution. The preceding conclusions thus refer to the context where the research took place. Furthermore, the experiences of students with the AR objects produced were very specific and focused on three knowledge areas, to which it must be added that the duration of the experience was short.

This suggests the need to propose new research initiatives, either enlarging the sample or designing the sample in accordance with another methodological model. The possibility also exists to replicate the study with other groups of students and with students from other universities. We could additionally undertake longitudinal studies, in which students work with AR-based learning objects, and verify the extent to which the findings remain constant. It would finally be possible to enlarge the knowledge areas presented here or to add other cognitive variables worthy of consideration, such as cognitive styles.

Author Contributions

Conceptualization, J.C. and R.R.; methodology, J.C and R. R..; software, J.C.; validation, J.C. and R.R.; formal analysis, R.R.; investigation, J.C. and R.R.; resources, R.R.; data curation, J.C.; writing-original draft preparation, J.C. and R.R.; writing-review and editing, J.C. and R.R.; visualization, R.R.; supervision, R.R.; project administration, J.C.; funding acquisition, J.C.

Funding

This research was funded by the Ministerio de Economía y Competitividad del Gobierno de España [Ministry of Economy and Competitiveness of the Spanish Government] entitled: "Realidad aumentada para aumentar la formación. Diseño, producción y evaluación de programas de realidad aumentada para la formación universitaria [Augmented reality to enhance learning. Design, production and evaluation of augmented reality programs for university learning]" (EDU-5746-P - Rafodiun Project).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Programs used to create (the) AR objects

AUGMENTED REALITY

SOFTWARE WHAT IT IS USED FOR

Metaio Creator

Augmented reality programming.

Metaio SDK

AR software development kit.

Eclipse

Java development environment. Exporting apk for Android.

Xcode

Java development environment. Exporting .ipa for iOS. Uploading to the App Store.

Adobe After effects

Video and sound post-production. Chroma, Rotobrush, Key Light.

Adobe Photoshop

Image post-production. Graphic designs. Photomerge. 3D Texturization.

Macromedia Fireworks

Image post-production. Graphic designs.

Ffmpeg

Programming on the codec to export 3g2 videos-AR.

MS Powerpoint

Buttons in video format with transition effects.

Notepad ++

Professional word processor for code retouching.

Metaio toolbox

Extraction of a 3D-based marker.

Autocatch123D

Photogrammetry.

Artec Studio

3D scanning.

Astrum

Creator of Windows installer.

Android Studio

Java development environment. Exporting apk for Android.

Blender

3D retouching.

Gigapan Stich

Gigapixel creation.

VIRTUAL REALITY

Unity 3d

Virtual reality programming. Exporting apk for Android.

SDK Google Cardboard

Virtual reality software development kit.

Xcode

Java development environment. Exporting .ipa for IOS. Uploading to the App Store.

Adobe Photoshop

Equirectangular creation. Graphics.

Appendix B

URLs to download markers and apps

Appendix C

S-mean by items and for the instrument as a whole (note: 1 = ways to use video; 2 = Anatomy; and 3 = Art).

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Figure 6. Keller's ARCS (Attention, Relevance, Confidence, Satisfaction) model (Keller, 1983).

Educational Technology

Medicine

Art

Video Use

Anatomy

Art

Total for the instrument

0.940

0.898

0.951

Attention

0.876

0.704

0.893

Confidence

0.780

0.582

0.900

Relevance

0.764

0.746

0.789

Satisfaction

0.821

0.775

0.843

Student's t-Value

Significance

Video use

16.107

0.000 (**)

Anatomy

17.323

0.000 (**)

Art

14.468

0.000 (**)

Pearson's Correlation Coefficient

Global

Attention

Confidence

Relevance

Satisfaction

Video use

0.321(**)

0.348(**)

0.289(*)

0.256(**)

0.201(*)

Anatomy

0.52(**)

0.491(**)

0.323*(*)

0.321(**)

0.383(**)

Art

0.432 (**)

0.398 (**)

0.315 (**)

0.2989 (**)

0.356 (**)

# data[].attributes.attachment_link.title
The Motivation of Technological Scenarios in Augmented Reality (AR): Results of Different Experiments

# data[].attributes.attachment_link.description
Augmented Reality (AR) is an emergent technology that is acquiring more and more relevance in teaching every day. Together with mobile technology, this combination arises as one of the most effective binomials to support significant and ubiquitous learning. Nevertheless, this binomial can only prove valid if the student is motivated to use it during the learning process.

# data[].attributes.body
User Experience in Mobile Augmented Reality: Emotions, Challenges, Opportunities and Best Practices

# data[].attributes.attachment_link.readable_text
by 1 and 2,*

Business Information Technology, Haaga-Helia University of Applied Sciences, 00520 Helsinki, Finland

Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 93187 Skellefteå, Sweden

Author to whom correspondence should be addressed.

1. Introduction

The rapid evolution of technology has brought new opportunities for developing new applications and for interacting with them. Augmented Reality (AR) has become an alternative way to engage users with applications more efficiently [ 1]. Kipper and Rampolla [ 2] define Augmented Reality (AR) as a blending of real-world and digital information. This combination of the real world and the virtual world has been harnessed for various purposes, such as marketing, education, entertainment and health care. For example, AR has been used for educational and training purposes across subject areas both in formal and informal settings [ 3, 4, 5, 6, 7, 8], and it has been shown to possess a multitude of affordances for learning and training scenarios [ 7, 8, 9, 10, 11]. Despite these success stories, AR (and mobile AR in particular) has not yet fulfilled a previously made prediction of becoming the eighth mass media [ 12]. There is a significant body of research on AR capabilities in different sectors [ 13], and the challenges and limitations of AR have been discussed [ 7, 14].

Aligned with the popularity of smart phones and tablets, Mobile Augmented Reality (MAR) has emerged as a mainstream technology and is currently a hot topic among researchers across fields. As in our earlier work [ 3], MAR is defined here as "a type of AR where a mobile device (smartphone or tablet) is used to display and interact with virtual content that are overlaid on top of a real-time camera feed of the real world". Virtual content in MAR applications can be context-sensitive and take various forms including, but not limited to three-dimensional (3D) models, animations, annotations and videos. Recently, popular mobile applications with AR features, such as Pokémon Go and Snapchat, have changed the developers' and users' perceptions of AR. These applications brought MAR to people's smartphones in earnest, especially targeting kids and youth. Moreover, the increase in the number of toolkits for creating MAR applications has enabled even non-technical individuals to create their own MAR content [ 3], thus further strengthening the position of MAR as a mainstream interaction technology.

Although Pokémon Go has been associated with many benefits, such as making people exercise more [ 15] and helping those who have withdrawn from social life [ 16], little attention has been put on investigation of User Experience (UX) factors that contribute to positive reception of MAR applications. Following our earlier work [ 17], UX is defined here as the emotions that the user encounters while using a service, a product or an application. An example of this is the negative emotions experienced by some Pokémon Go players when the AR feature of the game drains the smartphone battery and makes their smartphones slow [ 18, 19].

Although UX of MAR applications have received some attention from researchers (e.g., [ 20, 21]), there is a need for a study that focuses on MAR UX from the perspective of emotions. To address this need, the main objectives of this study are to assess the UX of two MAR applications and to draw conclusions as to what challenges, opportunities and best practices are associated with MAR application development from the UX perspective.

The rest of the paper is organized as follows. We start with an overview of previous research on mobile user experience and its relation to mobile augmented reality. Then, after presenting the research design, we describe two MAR applications-a commercial AR experience and our own Virtual Campus Tour application-that are used as case studies in this paper. In the Results Section, we present the results of mixed-method evaluations of the two case studies and then describe challenges, opportunities and best practices associated with mobile augmented reality user experience. The identified best practices are then utilized to improve the second case study. Before concluding the study, we provide discussion on the findings.

2. Background

2.1. Defining Mobile User Experience

Despite a more than a decade's worth of research on UX, the concept still suffers from vague and broad definitions, which is reflected in the 27 definitions of UX reported as the result of the Dagstuhl Seminar of Demarcating User Experience [ 22]. UX is a multidimensional phenomenon [ 23] in which many factors influence success, and it has been around for many years [ 17]. Carlos et al. [ 24] identify users, context, interaction and artifacts as the key elements of UX. Moreover, Hassenzahl [ 25] refers to UX as the "quality of interactive technology", focusing on the human and not on the product. Hassenzahl and Tractinsky [ 26] believe that with the advancement of technology, interactive products and services become not only useful and usable, but also trendy and fashionable. In this study, UX is defined as the emotions that the user encounters while using a service, a product or an application [ 17]. This definition is a consequence of the recent development of design thinking, which recognizes emotional engagement as a key factor in enabling sustainable use of mobile applications.

In mobile applications, UX requires special attention due to limitations of mobile devices (e.g., screen size) and the context-awareness afforded by the embedded sensors. Various attempts have been made to promote mobile UX through evaluation metrics and design approaches in recent years. Yong [ 27] recommends UX evaluation methods for mobile devices and proposes that these methods should measure the emotional responses of users on visceral, behavioral and reflective dimensions. Väätäjä and Roto [ 28] recommend mobile questionnaires for mobile UX evaluation, but point out that in the design of the questionnaires, one must pay special attention to the clarity and simplicity of the structure including questions, icons, items and scales. Pelet and Taieb [ 29] focus on mobile application design to promote UX, with recommendations on how color contrast can be used to impact the positive effects on behavioral intentions. Furthermore, Dirin and Nieminen [ 30] propose a usability and UX framework for mobile learning applications. The framework has been proven to promote UX in a mobile-based application by making the application adjustable, delightful and reliable, all of which are factors contributing to the emotional dimension of UX.

The existing mobile UX measurement and design approaches may also be to some extent applied to MAR applications. However, MAR UX design is not the same as mobile UX design. Although emotional engagement, personalization and reliability are important in all applications and services, in MAR UX, users are engaged both mentally and physically in the application. The mental engagement in this context means that users fully concentrate on the application, and short- and long-term memory are engaged in the application. Physical engagement means that users often need both hands, eyes, ears and even the rest of their bodies to be involved with the application.

2.2. Mobile Augmented Reality and User Experience

Previous studies on MAR from the UX perspective are vague, and there is certainly room for further research [ 31]. The main reasons for the lack of MAR UX research and design guidelines is the recentness of MAR as a mainstream technology. Major advancements in the development of MAR happened during the last decade [ 32]. The main breakthrough occurred with the emergence of smartphones equipped with advanced technologies [ 33]. Yet, there are some researchers who have investigated the area of MAR UX, as we briefly explain in the following.

Irshad et al. [ 31] conducted a review on MAR UX research published in 2005-2014 and presented a categorization of the MAR UX domain, including aspects such as UX as a phenomenon, UX as a field of study and UX as a practice. Furthermore, they promoted User-Centered Design (UCD) as a recommended method for MAR application development. When we reviewed previous research on MAR UX, we discovered that MAR applications, where UX was specifically considered, were developed and researched either through case study evaluations or through MAR application development frameworks. In both cases, the user of the application is typically at the center of the design and development, thus confirming the results of Irshad et al. For example, Rashidi et al. [ 34] proposed a framework based on UCD for developing MAR tourism applications. In another example, Olsson et al. [ 21] and Olsson and Väänänen-Vainio-Mattila [ 35] elaborated through a case study MAR application and came to the conclusion that MAR applications' acceptance by potential users requires that designers learn about users and their essential needs in advance. This is not unlike most UCD frameworks that involve end-users early in the design process.

Furthermore, usability (i.e., effectiveness, efficiency, satisfaction), which is an important factor in positive UX, has traditionally been associated with mobile application design and development. Aligned with this, Ritsos et al. [ 20] advocated the importance of usability as a key factor for the adoption of MAR applications by users, whereas Jung et al. [ 36] focused on the satisfaction attribute of usability while conducting a study on a marker-based MAR application for tourists. The findings of Jung et al. indicated that content, personalized service and system quality affect users' satisfaction.

To promote MAR usability and UX, some researchers have focused on framework development. For example, Kim [ 37] proposed a framework for context immersion in MAR applications. The framework is constructed of three contexts: time- and location-based context, object-based context and user-based context. Understanding users' needs in these contexts is a crucial part of UX design in MAR applications. Aligned with this, Irshad and Awang Rambli [ 38] proposed a multi-layered MAR framework for achieving positive UX. Their framework is divided into various dimensions of a MAR application, such as product, time, specific context and UX.

Our literature review revealed that MAR UX is attracting interest among researchers, with most of the related publications having been published in recent years. Previous studies indicated that the focus has been on UX and usability evaluation of MAR applications, but we still lack information on how users perceive MAR applications emotionally and what are the best practices for MAR UX designers. Important questions to answer include: "What kinds of emotions arise among users of MAR applications?" and "What concerns do users have when they deal with MAR applications?" The aim of this paper is to answer these questions through evaluations of two MAR case studies.

3. Research Design

The following sections describe the research questions, participants and methods used in the evaluation of the two case study applications, which are described in Section 4.

3.1. Research Questions

It is evident that UX of a mobile application is different from that of a MAR application [ 39]. To investigate the role of UX in the development and evaluation of MAR applications, this study aims to answer the following research questions through two case studies (see Section 4):

How do users perceive two MAR case study applications emotionally?

What are the major opportunities and challenges associated with MAR UX?

What are the best practices for MAR UX?

We chose to analyze the case studies by a mixed-method approach comprising qualitative and quantitative methods. We explain these methods in the following sections after describing the study participants.

3.2. Participants

The case study evaluations were conducted at the Haaga-Helia University of Applied Sciences in March 2018. Table 1 summarizes the demographics of the participants for both case studies. The participants were recruited on a voluntary basis from a user experience design course at Haaga-Helia. No rewards were given to the participants. All collected data were anonymized so that the participants cannot be identified from the results presented in this study.

The participants were experts in using information technology, thus no technology training was provided to them before the experiments. Many participants used Android smartphones, and a few owned iPhones. The experiments were conducted in English because the participants were from different countries.

It is noteworthy that although the sample sizes are relatively small, and we therefore cannot draw statistically-significant conclusions, the quantitative and qualitative results together indicate useful information on the UX of the two case studies. The number of recommended test users for usability experiments varies between three and five. For example, Nielsen [ 40] states that with five test users, one almost gets close to the maximum benefit-cost ratio in usability testing. Nielsen [ 40] further promotes qualitative user research for collecting insights to drive the design, not the number of involved people.

3.3. Research Methods: Case Study 1

3.3.1. Data Collection

In Case Study 1, we asked the participants to experiment with a commercial MAR application featuring an interactive cat (described in Section 4.1) and then administered a questionnaire based on [ 41] about emotions that the participants felt during the experiment. The questionnaire consisted of basic information, such as gender, age and previous experiences with MAR. Additionally, the questionnaire had Likert-scale questions about the participant's emotions during and after the experiment. Figure 1 presents samples of the Likert statements used for collecting data on the participants' emotions.

We also conducted short interviews with selected participants about their experiences with the application. We randomly selected nine participants (n = 9, females: 5, males: 4) for interviews among those who finished the experiment in 3-5 min (n = 4), in 6-10 min (n = 3) and in more than 10 min (n = 2). The interviews were recorded as audio and transcribed later. The following questions were asked in the interview:

Question #1: What was your first impression when you saw the cat?

Question #2: Did you find it difficult to test the application and bring the cat to the screen?

Question #3: Would you like to have this type of application in your daily life?

Question #4: How would you change this application?

The experiment was conducted in a classroom with the following procedure. The participants were given smartphones (Samsung Galaxy S6) and were instructed to download the Arilyn application from Google Play. The participants were then asked to scan the cat picture on a milk package (we had two empty packages of milk in the classroom) or through a cat picture, which, for the sake of accessibility, was presented to the participants through a PowerPoint slide. The duration of the experiment varied between 3 and 15 min among the participants. The participants were given freedom to explore the functionality of the MAR application; therefore, no specific instructions or tasks were given. The researchers observed the participants during the experiment and took notes accordingly. After the participants finished the experiment, they were asked to fill in a questionnaire that was delivered as a hardcopy. After the experiment, a researcher conducted interviews with the selected participants.

3.3.2. Data Analysis

In Case Study 1, we applied statistical tools to the collected quantitative data to assess how the participants felt about the MAR application. The collected questionnaire data on emotions was based on a Likert-scale; thus, a simple proportional analysis of the participants' answers was considered to be an appropriate approach to represent the users' emotional states towards the tested MAR application. To analyze the questionnaire data, we used the ToolPak add-in for Microsoft Excel, which provides various data analysis tools for statistical and engineering analysis.

After transcribing the interview data, the participants' answers and researchers' observations were analyzed to learn more about the participants' perceptions and reactions towards the MAR application. The analysis was conducted by applying the method of coding [ 42] in a word processor whereby keywords were assigned to the qualitative data excerpts. These keywords were, for example, emotional words that the participants mentioned or the emotional expressions that were extracted by observing. The frequencies of the keywords are not reported in this study due to the relatively small sample size.

3.4. Research Methods: Case Study 2

3.4.1. Data Collection

In Case Study 2, the participants used a Virtual Campus Tour application that was designed and developed at the Haaga-Helia University of Applied Sciences (see more details in Section 4.2). We conducted a usability and UX evaluation in a usability lab at the Haaga-Helia University of Applied Sciences. The usability evaluation focused on the performance of a set of predefined tasks to measure the efficiency of the application. The UX evaluation, which was based on observations and post-experiment interviews of the participants, focused on how the participants felt about the MAR application. The aim of this case study was to investigate the emotions that the participants felt about the application and to find out whether the participants found the application easy and efficient to use and whether it fulfilled their essential needs. Table 2 presents the details of the test plan.

In the usability assessment, which was video-recorded, we applied the think-aloud method to learn about the participants' ways of performing the given tasks. In the think-aloud method, which is rooted in psychology [ 43], the participants are advised to say what they think while performing a given task. In our experiment, the objective of using the think-aloud method was to give us feedback on whether the interface is logically designed and whether the participants find the components in expected places.

There were two people conducting the Case Study 2 experiment: the facilitator and the observer. The facilitator briefly introduced the application prototype and the purpose of the test to the participants and answered any questions that the participants had about the experiment. During the experiment, the facilitator assisted the participants if they had problems, but without guiding them to solve the tasks independently. Finally, the facilitator interviewed the participants. The observer silently recorded the times of the tasks and observed the verbal language and facial expressions of the participants. The observer also took notes on the participants' actions, comments, procedural errors and problems and assisted the facilitator in writing down the participants' interview answers.

3.4.2. Data Analysis

To analyze the data collected in Case Study 2, we applied usability evaluation metrics, such as the time spent on performing the predefined tasks and the number of tasks performed in a given time, to measure the application's efficiency. Furthermore, the qualitative dataset comprised transcribed think-aloud videos, observations and interview recordings. As in Case Study 1, we applied coding [ 42] in a word processor to identify emotional reactions and usability issues raised by the participants during the experiment. The frequencies of the keywords are not reported in this study due to the relatively small sample size.

4. Case Studies

4.1. Case Study 1: Aamu Cat

The first case study is a commercial advertisement MAR application by a Scandinavian dairy corporation (Arla), which was in the market during a promotional period (in 2018) in Finland. The customers were advised to download the application (Arilyn) to their phones and scan a picture of a cat embedded in a milk package, thereby activating the AR content. The AR content features a cat, called Aamu (morning in Finnish), that jumps to the phone screen out of the milk package, thus granting the user the opportunity to play and interact with the cat (e.g., stroking). In this application, the Aamu cat first wanders around, and by swiping the screen, the user can trigger the cat to perform different tricks, such as jumping. Figure 2 presents screenshots of the Arilyn application with Aamu. A detailed description of the application and the integrated AR solution are available at https://www.arla.fi/aamukissa/.

4.2. Case Study 2: Virtual Campus Tour

The second case study was designed and developed by two students at the Haaga-Helia University of Applied Sciences. The aim of this MAR application is to provide a virtual tour of the Pasila campus to those students who received admission to the Business Information Technology (BITE) degree program for the fall 2018 semester. The concept design and implementation processes are out of the scope of this paper.

Figure 3 presents the screenshots of the Virtual Campus Tour application. In this MAR application, an avatar of the BITE study advisor, Riitta, orients new students to the Pasila campus, including information on facilities, lecture rooms, the library and key people. The application helps new students to navigate in the first, third and sixth floors where the classrooms, library and teachers' and advisors' offices are located. The avatar helps the student select the correct floor based on the place where they want to visit. For the implementation, we used Unity 3D, 3D videos and Vuforia AR. The Android-based application scans a marker, thus causing the avatar to appear. The student starts to move with the smartphone to get to the door of the elevator where more virtual content is shown. The avatar appears in the front of the elevator next to the buttons, and at the same time, a virtual button panel is displayed. The student is then advised to pick the floor where they want to visit.

5. Results

In the following, we present the results of two case study evaluations based on the methods described in Section 3. Then, we collectively summarize the emotions that were identified from the case studies.

5.1. Case Study 1: Aamu Cat

5.1.1. Questionnaire Results

The demographics section of the questionnaire indicated that 15 out of 22 participants had previous experience with MAR, whereas seven participants were inexperienced with MAR, thus suggesting that MAR as a technology has reached a relatively high degree of awareness among young adults.

Figure 4 shows what types of emotions the participants felt towards the Aamu Cat MAR application. The results indicate that positive emotions (relaxing, inspiring, encouraging, exciting) were experienced by most of the participants. Among all participants, a majority (59%) agreed that the MAR application made them relaxed, whereas 14% did not find the MAR application relaxing. Similarly, a majority experienced also other positive emotions, as 73%, 87% and 73% of the participants agreed that the MAR application was inspiring, encouraging and exciting, respectively, with very few negative responses.

Compared to positive emotions, negative emotions gained much less agreements. Among all participants, 87%, 86% and 77% disagreed that the MAR application was unpleasant, depressing and boring, respectively. Only 9-14% of the participants felt neutral about the negative emotion statements. Although low in proportions, it is noteworthy that 5% and 9% of the participants felt that the MAR experiment was unpleasant and boring, respectively.

5.1.2. Interview Results

In the following, we present the results related to the four interview questions that were asked from the selected participants (9).

Question #1: What was your first impression when you saw the cat?

The participants considered their MAR experiment with the milk package and the Aamu Cat to be interesting and expected to see similar applications of MAR more in public places, for example:

"I really like its simple interactions. Cannot wait for it to kick off in everyday public places."

Some participants identified the MAR application to be intuitive, thus giving more control to the user on how to utilize the interactions in the application, for example:

"The interaction was engaging and with fun, I almost knew how I should play with the cat."

Several participants considered that the application engaged them emotionally, which, as per our definition, is the essence of positive user experiences:

"It was emotionally interesting and delightful, and having a real connection with the object, through getting it working took a while."

"Getting the cat out of the box for the first time was cool [...] and its fun for a short time."

"The cat jumping out of the box was surprising."

The participants' emotional engagement in the application was also visible in the following terms that frequently appeared in the participants' comments as their first impressions: unexpected, surprised, liked it, fun, friendly and interactive. In addition to emotional engagement, some participants considered that they became involved with AR and the objects within:

"AR is more interactive, and I feel involved."

"AR is about feeling the objects."

Question #2: Did you find it difficult to test the application and bring the cat to the screen?

All students managed to perform the experiment successfully despite no detailed instructions given by researchers. Therefore, all students answered "no" to this question. The main concerns that emerged during the experiment were getting the application to work and that interacting with the cat was time consuming.

Question #3: Would you like to have this type of application in your daily life?

Some participants considered that MAR is suited well to advertisements, buying and selling. The applicability of MAR as a learning technology, however, received more skepticism, for example:

"I've tried AR in a language learning application, but I dropped the language course [where the AR learning application was used], so I cannot say whether it is helpful. I prefer the normal two-dimensional (2D) view when it comes to language learning. MAR drains battery way faster."

The following comment from a participant shows that the perceived benefit for the invested time may depend on the user's age:

"Maybe kids want to play with cats for minutes if not hours, I do not like to spend time with a virtual pet in my phone."

Question #4: How would you change this application?

The changes that almost all participants recommended were having more functionalities in the application, such as: communication with the cat, performing more tricks with the cat and selecting different avatars (e.g., a dog). Furthermore, some participants suggested that the cat's avatar should be more versatile and realistic and that users should have a choice of personalizing the avatar, as indicated in these comments:

"Different animal, more colorful surroundings."

"More entertainment actions."

"I guess some alert that it would popup is good to have."

"Feel like this is a beta version, I want to have a more realistic figure. Seems that the cat moves very slow."

Furthermore, we discovered that the cat avatar promoted positive UX and may even compensate some factors that contributed to negative UX:

"My first AR experience wasn't surprising because the app lagged a bit but the cat on class today was really nice."

5.2. Case Study 2: Virtual Campus Tour

In the usability evaluation, we measured the efficiency of the Virtual Campus Tour application usage, i.e., how well and fast the participants find the locations defined in the tasks with the Virtual Campus Tour application. The loading time of the application was around 0.5-3 s from launching until the participant could start interacting with it. It took on average around 10 min (minimum: 5 min, maximum: 15 min) for the participants to complete the predefined tasks. The participants were generally satisfied with the application's user interface and the way of interacting with the application.

When prompted in the interviews what the participants would like to change in the application, all of them said that the application should have more instructions of where they should go, the names and locations of places/areas and special places, such as laboratory and computer rooms. Moreover, some participants suggested that the application should also have a map or a list of places in the beginning, so users would know what they can learn from the navigation and where they are in relation to the whole school map. Finally, the navigation could be more playful and interesting by applying a game concept, such as missions to find different areas in the school, and including more interactable objects, as well as background music, to make the tour more relaxing and exciting.

The participants' main objections concerned the application's graphics and insufficient control over the application's performance. For example, the augmented elevator panel did not work properly, as the participants needed to touch a specific area on the floor number to activate it. Although non-active keys were dimmed, some participants still attempted to click them. Moreover, it was not possible to cancel a selected key.

In the UX part, we assessed how the participants felt during and after the experiment. In general, the participants found the application concept to be appealing, especially due to the simple and straightforward interaction and intuitive and obvious movement. The emotions experienced by the participants are listed in Section 5.3.

5.3. Positive Emotions Experienced in the Case Studies

Table 3 summarizes the positive emotions that the participants experienced in our case studies. These emotions were extracted from the data collected by observations, questionnaire (Case Study 1), interviews and the think-aloud method (Case Study 2). It is noteworthy that the diversity of emotions that MAR applications can evoke in users is far beyond those that emerged in this study.

6. Challenges, Opportunities and Best Practices for MAR UX

The following sections present the challenges, the opportunities and the best practices for MAR UX that emerged from our collective analysis and interpretation of literature and case study findings, as well as from our experiences in developing the Virtual Campus Tour application. Then, we apply some of the identified best practices to create an updated version of the Virtual Campus Tour.

6.1. Challenges of MAR UX

There are many challenges associated with UX design that are specific to MAR applications. Table 4 summarizes some of the challenges that we learned in this study.

6.2. Opportunities of MAR UX

Despite many challenges, there are significant opportunities that MAR applications bring to UX design. Some of these opportunities, as identified through the case studies, are listed in Table 5.

6.3. Best Practices for MAR UX Design

As MAR applications seek to create a bridge between the real world and a virtual world, UX designers need to pay attention to the special requirements that arise from the interplay between the two worlds. To alleviate this challenge, we have devised a set of best UX design practices for MAR applications based on the analysis of the case studies. These practices are listed in Table 6, together with descriptions and examples. By following these practices, UX designers can increase the likelihood of users becoming emotionally attached to the target MAR application. It is noteworthy that this list is intended to complement, not replace, existing mobile UX design guidelines.

6.4. Applying Best Practices to Virtual Campus Tour

Figure 5 presents an updated version of the Case Study 2 application (Virtual Campus Tour) based on some of the best practices presented above. First, we designed a new avatar for Riitta, whom the freshmen meet when they arrive at the university for the first time. We designed the avatar to be as realistic as possible, thus providing a true reality-virtuality object correspondence and object detail to new students. It is not merely a design transformation from a 2D to 3D object; the MAR object actually represents a real-world entity, in this case a person. Hence, we used an existing and expected metaphor for MAR content representation.

Furthermore, to make MAR objects more realistic, all of them were resized to match their real sizes. To further improve spatial correspondence, we ensured that the locations of MAR objects were aligned with their real-world counterparts, where applicable. This is unlike in the original Virtual Campus Tour, where the virtual panel size and location were far from those of the actual panel.

Furthermore, to engage users emotionally with the MAR application through natural interaction, we enabled voice commands. The student can start to communicate with the avatar by saying "Hello" as soon as the avatar appears. The avatar responds, and then the student may ask, for example: "Who are you?" The avatar first introduces herself, and then, she explains about the different floors, classrooms and offices. Finally, after the tour ends, the avatar wishes the student all the best. A future study is needed to measure whether the added voice commands and other changes to the avatar make it more emotion-evoking and natural.

7. Discussion

With the advancement of smart device technologies and the popularity of smartphones, users' expectations have exceeded mere usability of mobile applications. In contemporary application design and development, designers must not only expect that the application is easy and efficient to use while satisfying the user's essential needs, but that it also makes the user emotionally engaged. Emotional engagement is associated with the experience that the user constructs while interacting with the application. Yet, UX is a complex concept, as it deals with many different factors. The focus of this paper was on the emotional aspects of UX in the context of MAR applications. Furthermore, MAR UX design, development and evaluation are somehow different than those of traditional mobile applications. From the MAR concept design perspective, UCD frameworks, such as mLUX (m-Learning User eXperience) [ 30], provide relevant concept development methods that ensure appropriate usability together with UX factors, including, but not limited to delightfulness, reliability and adjustability.

From the development perspective, various integrated development environments, such as Unity 3D and Visual Studio, and AR toolkits, such as Vuforia AR, provide great assistance to developers to come up with MAR applications. In fact, there even exist AR toolkits (e.g., ARIS, Augment, Aurasma, Blibbar and Layar) that enable the creation of full MAR experiences without programming skills [ 3]. Consequently, in the wake of the success of Pokémon Go, the number of published MAR applications is soaring, but research is needed to determine to what extent they engage users emotionally.

Unlike the MAR concept design and MAR application development, MAR UX design and assessment are associated with many challenges ( Table 4) that require further research and tool development. This has also been identified previously by other researchers who have investigated users' overall impressions of MAR applications. For example, some Pokémon Go players believe that the AR feature of the game drains the smartphone, which may lead them to disable the AR feature [ 18, 19]. Furthermore, MAR applications often require bodily involvement beyond mere touching. For example, users may need to walk to the correct location to launch a location-aware MAR experience, orient the device appropriately in relation to the real world to see the target MAR scene, use hand gestures to control MAR objects in the scene and possibly use voice commands or movement gestures to further interact with the application. If done poorly, this rich interaction can make the application unfriendly, difficult to use and therefore emotionally disengaging.

The interaction with MAR applications in public spaces requires special design considerations. It is not enough to make the application easy to use, which is a de facto design goal for all mobile applications, including MAR applications. Designers of mobile applications should acknowledge that, for safety reasons, the application should not disrupt users' awareness of their surroundings. This is even more important in the case of MAR applications that often require deeper interaction than ordinary mobile applications. Moreover, Carmigniani et al. [ 44] believe that the main problem regarding social acceptance comes from the level of disruption caused by mobile devices (in other words: mobile applications) in public places and during conversations.

The acceptance of MAR applications from the UX perspective requires that the application follows the user's mental models. For example, in Case Study 1, an interviewed participant mentioned that she prefers dogs over cats. Consequently, she did not find the cat as appealing as some other participants did. This indicates that designers must put attention on the correspondence of selected metaphors and mental models of the target users. The best way to achieve this is to utilize a UCD method that involves end-users as co-designers, thus revealing their mental models in the design process.

The acceptance of MAR also depends on whether users are sufficiently skilled to perform the necessary application setup and configuration tasks, which should be minimal. For example, the participants of Case Study 1 had to install the Arilyn application before interacting with the cat. Moreover, the results demonstrated that the participants, who were university students, were ready to use MAR applications if and only if they find an added-value in having the application installed in their smartphones. This conclusion is only valid in the context where the experiment was conducted; in other contexts, and with other users, the results could be different.

There are several limitations in this study that are noteworthy. Firstly, the sample sizes in case studies was relatively small; thus, we cannot draw statistically-significant conclusions. Statistical analysis of a large quantity of interview data could, for example, be used in a future study to identify emotions that are particularly prominent among MAR application users. Secondly, because the evaluation experiments were conducted in a single session, long-term effects of UX design remain to be investigated. For example, it would be important to investigate how long a MAR application succeeds in supporting emotional engagement of its users, as it directly relates to sustainable use of the application. Thirdly, the interviews were fully structured, thus leaving little room for drilling deeper into specific topics of interest. Considering these shortcomings and the results of this study, we aim to develop a richer MAR application and investigate its UX with a larger sample size and versatile data collection instruments over a long period of time in the future.

8. Conclusions

The aims of this study were to find out how users perceive MAR applications emotionally, to identify challenges and opportunities associated with MAR applications and to recommend best practices from the perspective of MAR UX design. These aims were reached through evaluations of two case studies: a commercial advertisement application featuring an animated AR cat and our Virtual Campus Tour application designed to orient new students at a university campus. The analysis of the evaluation results, together with the conducted literature review and the experiences acquired during the development of the Virtual Campus Tour application, were the basis for identifying several challenges, opportunities and best practices for MAR UX. The identified best practices were then used to update the Virtual Campus Tour application, thus increasing its potential to engage users emotionally in the future. Whether this potential is realized or not remains to be evaluated in a future study.

The evaluations of the two case studies demonstrated that the participants value MAR applications based on their functionalities, usefulness and ease of use. A major reason as to why designers should utilize AR in mobile applications is to provide positive experiences to users by engaging them emotionally with the application. Indeed, as we have shown in this study, emotional engagement is closely associated with the term user experience. We further demonstrated that having AR objects embedded in a mobile application requires proper design considerations, such as identifying users' needs, providing natural interaction and engaging users with the application through a realistic avatar.

The results of this study may help UX designers come up with more user-friendly and robust MAR applications. Yet, there remain many research gaps to be investigated, such as developing an acceptance model for MAR UX, modeling MAR content interaction, personalizing MAR experiences and sustainable use through appropriation of MAR experience lengths. We also recommend further studies on MAR applications in various contexts to draw universal MAR UX guidelines based on the best practices presented here.

Author Contributions

A.D. designed and performed the experiments. A.D. and T.H.L. analyzed the data. A.D. and T.H.L. wrote the paper.

Acknowledgments

Special thanks go to the students at the User Experience Design and User Experience Project courses in spring 2018 at the Haaga-Helia University of Applied Sciences.

Conflicts of Interest

The authors declare no conflict of interest.

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Case Study

Participants

N

Males

Females

Age (Average)

Case Study 1

Haaga-Helia students

22

13

9

18-31 (23)

Case Study 2

Haaga-Helia students (4) and other students (2)

6

2

4

20-34 (25)

Plan category

Details

Equipment:

Mobile phone (Samsung Galaxy A8, Android), test instructions

Observations:

Notes of the user's actions and the time it takes for the user to complete the tasks.

Pre-test:

Introduction and application orientation

Tasks:

Go to Riitta's room on the 6th floor.
Proceed to the library and be welcomed by the guide
Go to the 1st floor lobby entrance/ exit door

Test Method:

Facilitator instructs the users of the tasks.

Data Collection:

Video and audio recording, written notes, think-aloud, interviews

Interview questions:

Comments about the hardware
Comments about the app. What would they change? Negative and positive aspects?
Comments about how users felt while accomplishing the tasks. Emotions?
Is it fun to use, interesting, anything surprising?
Other comments

Introduction:

5 min

Test Tasks:

4-15 min (Usability and UX)

Debriefing:

5-10 min

Reporting

120 min

Total:

15-30 min per user
1.5-3 h for 6 users

Emotional Impact

Case Studies

Best Practice

Description

Examples

Natural interaction

Allow users to interact with MAR content by multiple natural methods (e.g., speech, touch, gestures), thus promoting realism.

The interaction in the first and second case studies was based on a touch-based user interface, which is a typical approach in MAR applications [ 3].

AMA Style

Chicago/Turabian Style

# data[].attributes.attachment_link.title
User Experience in Mobile Augmented Reality: Emotions, Challenges, Opportunities and Best Practices

# data[].attributes.attachment_link.description
Mobile Augmented Reality (MAR) is gaining a strong momentum to become a major interactive technology that can be applied across domains and purposes. The rapid proliferation of MAR applications in global mobile application markets has been fueled by a range of freely-available MAR software development kits and content development tools, some of which enable the creation of MAR applications even without programming skills.

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Location based information to support understanding of landscape futures

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Implementing an augmented reality-enabled wayfinding system through studying user experience and requirements in complex environments

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Which Moral Foundations Predict Willingness to Make Lifestyle Changes to Avert Climate Change in the USA?

# data[].attributes.body
1. Målgrupp
00.00-00.46
Varaktighet 46 sek.

2. Koncept
00.47-02.26
Varaktighet 1 min 39 sek

3. Det verkliga
02.29-03.43
Varaktighet 1 min 14 sek

4. Det sanna
03.45-05.27
Varaktighet 1 min 42 sek

5. Det ideala
05.29-07.00
Varaktighet 1 min 31 sek

# data[].attributes.body
Del 1:
0.04-0.48 (design etnography)
0.49-2.24 (Simultation Exercise)
Del 2:
2.25- 3.35

# data[].attributes.body
Att försöka arbeta utifrån Role, Implementation och Look&Feel ger en snabb överblick över vad jag vill och kan testa och se hur allt fungerar.

Det blir en relativt naturlig process att kunna skapa någonting som både utvecklas framåt, men samtidigt iterativt när man får feedback och ser vad som funkar/inte funkar. Det gör att arbetet hela tiden utvecklas till en ny punkt och kan jobbas vidare på, även i detaljnivå.

Från prototyp 1 där arbetet enbart ligger på själva AR-kameran och att se hur möjligheten kring konceptet fungerar på en person gav en klar indikation på att det är förståeligt och att det går att arbeta vidare på, för att kunna belysa just risken med dystopin som tankeställare.

I arbetet kring prototyp 2 så är det gjort för en grundläggande förståelse kring de olika elementen, att interagera med både AR-kameran såväl som sina egna val i livet samt att kunna agera förebyggande samt som konsekvens i princip av sitt agerande. Tack vare feedback så kunde det ta arbetet vidare och se att de olika beståndsdelarna och sättet att interagera fungerar överlag.

Till den sista som handlar om just Look & Feel och samtidigt en stor del implementation också så förverkligar det möjligheten att kunna testa sig fram, se hur man kan interagera på ett större plan och det visade sig vara positivt under användartesterna.

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Denna prototyp ska ge svar på "hur visar AR den faktiska förändringen för användaren?"

Jag har valt att använda mig av filterdimensionerna appearance och interactivity. Dessa har jag utforskat genom att ge AR-funktionen ett utseende och visualiserar det via eld. Detta ger informationsbeteende till användaren och ger möjligheten att se hur den även visar output för användaren efter att de angett sina utsläpp i applikationen.  I och med att jag gjort detta inomhus på grund av begränsade möjligheter nu så har jag ändå valt att testa hur det upplevs vid att se hur eld placeras ut som AR-objekt i omgivningen. Det gav mig svar på min frågeställningen att det inte ger tillräckligt med information för användaren då de inte vet vad 1, 2 eller fler bränder egentligen visar upp. Statistik och informationsruta kan behövas för att förklara för användaren vad bränderna egentligen symboliserar. Främst på grund av att den inte kan förändra hela omgivningen och visa ett konkret sätt. Så ytterligare information kommer behövas för att kunna visa upp vad de egentligen visualiserar.

Prototypen gav mer insikt i vad som krävs framöver och framförallt att den måste kunna skapa mer intresse och locka till större användning. Informationsmässigt så är den i underläge och kan dra nytta av någonting "mer". Både vid användning i sig för specifika användare, för att påvisa hur deras val har påverkat visualiseringen av bränder, men även innan. Viss information kan behöva visas upp kring området i sig som man utforskar, så som plats, statistik kring bränder under åren eller liknande.

Konceptet behöver därmed utvecklas vidare för att kunna belysa förändringarna utan att bara ladda upp nån eldsvåda här och där, utan snarare ge faktiskt information utöver detta.

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Improving Public Engagement With Climate Change: Five “Best Practice” Insights From Psychological Science

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Gammalt test

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Skapa en stuktur inför användartester och metodtesterna för att få fram rätt typ av information.

Användartester:
Testa prototyp och wireframes för att se hur de fungerar och hur de känns för tänkta användare och på så sätt se var det styper, eller vad som flyter på bra.

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Bra funktioner
Flyter på bra och det finns många   olika saker att agera på
Lättsam att förstå

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Lite tråkigt utseende
Skulle vilja ha mer och tydligare    statistik och sätt att följa min egen utveckling
AR-funktionen känns rolig, men    svår att föreställa sig ändå

Metoder:
Valde nr 27 och nr 77 för att testa och se hur användare rör sig och vad de gör ute och på så sätt lära mig lite mer om deras generella beteenden och åsikter kring miljö och klimatsmarta val, samt se hur de interagerar med telefonen och när de gör det och därmed kunna följa ett mönster i hur applikationen kan komma att användas.

Simultation är främst framtagen för att kunna testa en mer high fidelity-prototyp och se hur de interagerar med den och vad som får dem att vilja testa mer, vad de vill klicka på och vad som blir mer irrelevant. Att kunna följa deras vägar och hur de klickar blir en intressant feedback för vad som är överflödigt i applikationen och vad som saknas.

Anteckningar:
Svårt att få till bra Design Etnography pga restriktionerna, men ska försöka få till att vara utomhus och fokusera på hur telefonen används och eventuellt små val man kan göra ute då beroende på vad som sker. Om möjligheten hade funnits för att kunna följas åt på affärer och liknande skulle man kunna se över fler val och agerande man gör i vardagen, så som val av varor, huruvida man väljer kollektivtrafik eller eget. Men då det i dagsläget är restriktioner som styr och gör att man inte ska vara i butiker och heller inte i mataffärer mer än 1 så blir det omstrukturerat.

Det är tydligt att föregående koncept har brister och därför har jag omformaterat och adderat saker i min prototyp för att kunna testa det bättre och använda inför uppgiftens film. Denna har jag gjort i XD, AR-funktionaliteten har jag tidigare använt Torch för men då det inte finns kvar får jag se vad jag lyckas ordna för att kunna visualisera för testpersonen.

Jag har laddat hem och testar applikationen ARLOOPA men får inte rätt funktionalitet att fungera för att lägga in egna AR-animationer, och det finns ingen som visualiserar det jag har tänkt mig med bränder eller liknande.

Jag har testat att spela in ett kortare klipp med ARLOOPA med enbart visualisering av skog för att se om detta går att använda i testerna framöver.

Metod-testerna är utförda och de gav ett strongt incitament gällande hur telefoner används under vissa perioder och huruvida den används till någonting viktigt eller ej, vilket gjorde att jag kände att det måste adderas ett notis-center innan jag fortsätter med simulationstestet.
Detta kan göra att det påminner användaren att använda applikationen istället för att gå in på t.ex sociala medier av gammal vana.

Den nya variationen med notis-center känns smidig och utgör en del som tidigare saknats för att få användaren att fortsätta vara delaktig. De tester som gjort via simulation visar att det finns många förbättringsdelar att utföra i ett eventuellt kommande projekt för att få den att bli så bra som möjligt vilket känns bra. Det gör att den går att fortsätta att utveckla och arbeta med om man vill.

I dagsläget så har jag valt att addera och inkludera:

AR-funktionalitet även i stadsmiljö
Notis-center för att fånga upp användaren och skapa engagemang
Möjligheten att kunna spara sina vykort eller dela dem med vänner

Arbetet med vidareutveckling av AR skulle innebära att testa i After Effects för att implementera en informationsruta och dessutom skapa förändringar via AR-bränder, i dagsläget har jag dock inte kompetensen eller tid att fortsätta utforska för att få till en bättre prototyp inom AR, utan det skulle få vara ett framtida projekt för att kunna visualisera det bättre.

Jag inser att applikationen får ett starkare fokus på samarbete och samhälle ju mer jag fokuserar på den än just de faktiska bränderna, och gör att jag känner att den kanske skulle ha haft en annan utgång från första början, men samtidigt så har det blivit tydligt allt eftersom hur jag arbetat så det är ändå ingenting som hade kommit fram till mig utan att gå de vägar jag gått hittills. Kanske hade AR varit enklare att arbeta med om det hade varit mer fokus på ens personliga arbete med miljö, och inte att den ska visualisera riskerna. Men då fokus var på det via uppgiften har det legat starkt som grund även om jag nu kan se att man hade kunnat tolka det annorlunda.

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ARLOOPA-applikation