Mosaic: Designing Online Creative Communities for Sharing Works-in-Progress

Online creative communities allow creators to share their work with a large audience, maximizing opportunities to showcase their work and connect with fans and peers. However, sharing in-progress work can be technically and socially challenging in environments designed for sharing completed pieces. We propose an online creative community where sharing process, rather than showcasing outcomes, is the main method of sharing creative work. Based on this, we present Mosaic---an online community where illustrators share work-in-progress snapshots showing how an artwork was completed from start to finish. In an online deployment and observational study, artists used Mosaic as a vehicle for reflecting on how they can improve their own creative process, developed a social norm of detailed feedback, and became less apprehensive of sharing early versions of artwork. Through Mosaic, we argue that communities oriented around sharing creative process can create a collaborative environment that is beneficial for creative growth

Becoming the Expert - Interactive Multi-Class Machine Teaching

Compared to machines, humans are extremely good at classifying images into categories, especially when they possess prior knowledge of the categories at hand. If this prior information is not available, supervision in the form of teaching images is required. To learn categories more quickly, people should see important and representative images first, followed by less important images later - or not at all. However, image-importance is individual-specific, i.e. a teaching image is important to a student if it changes their overall ability to discriminate between classes. Further, students keep learning, so while image-importance depends on their current knowledge, it also varies with time. In this work we propose an Interactive Machine Teaching algorithm that enables a computer to teach challenging visual concepts to a human. Our adaptive algorithm chooses, online, which labeled images from a teaching set should be shown to the student as they learn. We show that a teaching strategy that probabilistically models the student's ability and progress, based on their correct and incorrect answers, produces better 'experts'. We present results using real human participants across several varied and challenging real-world datasets.Comment: CVPR 201

Feedback aggregation in crowd feedback systems

Abstract. The purpose of this literature review is to research the way different crowd feedback systems aggregate and visualize their data for the user. First the concept of crowdsourcing for design purposes is introduced as well as four different crowd feedback systems, which are Voyant, CrowdCrit, Decipher and Paragon. Crowdsourcing means giving a task for a crowd of people to perform, usually online. Crowdsourcing is often used when there is a need for a large amount of responses because of its low cost compared to other methods. Crowd feedback systems use crowdsourcing to achieve their goal that is collecting feedback from a crowd. For a crowd feedback system to provide value into the design process, they should not only collect feedback but also convey the collected data to the designer in an informative but also easily understandable manner. This requires that the system provides support for non-experts for them to give feedback in a professional manner. The results of this thesis give an insight into how crowd feedback systems differ from each other. The results showed that different crowd feedback systems collect and present their feedback in very different ways. Voyant and CrowdCrit both visualize feedback using visual markers and stacked bar charts, but Voyant also uses word clouds for this purpose. Decipher shows whether the feedback is considered negative or positive and what the feedback provider had to say about the design. Paragon presents collected feedback with the help of examples that the feedback provider has chosen to help describe their feelings about the design. Voyant and CrowdCrit were eventually considered to be the most visually pleasing of these four crowd feedback systems. The way Voyant aggregated its feedback was seen more versatile but CrowdCrit collected feedback in a way that provided more useful feedback from non-experts.Palautteen koostaminen joukkoistamisen palautejärjestelmissä. Tiivistelmä. Tämän kirjallisuuskatsauksen tarkoitus on tutkia, millä tavoin erilaiset joukkoistavat palautejärjestelmät koostavat ja visualisoivat keräämänsä palautteen käyttäjälle. Ensin esitellään joukkoistamisen rooli suunnittelussa ja sen myötä myös neljä palautejärjestelmää, jotka ovat Voyant, CrowdCrit, Decipher ja Paragon. Joukkoistamisella tarkoitetaan tehtävien antamista joukolle ihmisiä, yleensä verkossa. Joukkoistamista käytetään usein, kun tarvitaan iso määrä palautetta, johtuen sen käytön edullisuudesta verrattuna muihin metodeihin. Joukkoistamisen palautejärjestelmät hyödyntävät joukkoistamista saavuttaakseen tavoitteensa, joka on palautetteen kerääminen joukolta ihmisiä. Jotta palautejärjestelmä voisi tuoda lisäarvoa suunnitteluprosessiin, täytyy sen palautteen keräämisen lisäksi myös esittää saatu data käyttäjälleen informatiivisessa, mutta myös helposti ymmärrettävässä muodossa. Tämä vaatii, että palautejärjestelmä tukee jollain tavalla ei-asiantuntijoita, jotta he voisivat antaa palautetta asiantuntevalla tavalla. Tämän kandidaatintyön tulokset antavat käsityksen siitä, miten joukkoistamisen palautejärjestelmät eroavat toisistaan. Tulokset osoittivat, että eri joukkoistamisen palautejärjestelmät keräävät ja esittävät keräämänsä palautteen hyvin eri tavoilla. Voyant ja CrowdCrit visualisoivat palautteen visuaalisten markkereiden ja pinottujen pylväsdiagrammien avulla, mutta Voyant käyttää myös sanapilviä tähän tarkoitukseen. Decipher ilmoittaa, onko palaute nähty positiivisena, negatiivisena vai neutraalina ja mitä mieltä palautteen antaja on ollut designista. Paragon esittää keräämänsä palautteen esimerkkikuvien avulla, jotka palautteenantaja on valinnut kuvaamaan tuntemuksiaan. Lopulta Voyant ja CrowdCrit nähtiin visuaalisesti miellittävimpinä näistä neljästä palautejärjestelmästä. Voyantin tapa koostaa palaute koettiin monipuolisempana, mutta CrowdCrit keräsi palautetta tavalla, joka tuotti hyödyllisempää palautetta ei-asiantuntijoilta

Crowdsourced intuitive visual design feedback

For many people images are a medium preferable to text and yet, with the exception of star ratings, most formats for conventional computer mediated feedback focus on text. This thesis develops a new method of crowd feedback for designers based on images. Visual summaries are generated from a crowd’s feedback images chosen in response to a design. The summaries provide the designer with impressionistic and inspiring visual feedback. The thesis sets out the motivation for this new method, describes the development of perceptually organised image sets and a summarisation algorithm to implement it. Evaluation studies are reported which, through a mixed methods approach, provide evidence of the validity and potential of the new image-based feedback method. It is concluded that the visual feedback method would be more appealing than text for that section of the population who may be of a visual cognitive style. Indeed the evaluation studies are evidence that such users believe images are as good as text when communicating their emotional reaction about a design. Designer participants reported being inspired by the visual feedback where, comparably, they were not inspired by text. They also reported that the feedback can represent the perceived mood in their designs, and that they would be enthusiastic users of a service offering this new form of visual design feedback

Designing professional learning

The Designing Professional Learning report provides a snapshot of the key elements involved in creating effective and engaging professional learning in a globally dispersed market. AITSL commissioned Learning Forward to undertake this study to give greater guidance around the ‘how’ of professional learning. Learning design involves making careful decisions based on an integration of theories, research and models of human learning in order to contribute to the effectiveness of professional learning. This work is not presented as definitive findings, but seeks to draw attention to observed trends and areas of commonality between learning designs that have demonstrated success. Following an analysis of a broad range of professional learning activities, a Learning Design Anatomy was developed to provide a framework for understanding the elements of effective professional learning. Each learning design element is framed by a detailed series of questions that challenge users to refine and clarify aims, intended learning outcomes and the most effective ways in which to engage—taking into consideration the unique context for learning. Examples of professional learning design are provided to illustrate elements of the Anatomy. The report is designed to be of use to teachers, school leaders, policy makers, system administrators and professional learning providers. It is intended that this report and the Anatomy will serve as provocation for a broader conversation about the composition of professional learning and the elements that establish the strongest correlation between participants, environment, delivery and action