This demo presents a platform for the definition of IoT-enhanced visits to Cultural Heritage (CH) sites. The platform is characterized by an End-User Development paradigm applied to the Internet of Things technologies and customized for the CH domain. It allows different stakeholders to configure the behavior of smart objects in order to create more engaging visit experience and to increase the appropriation of CH content by visitors.
{"title":"Empowering CH Experts to Produce IoT-enhanced Visits","authors":"C. Ardito, P. Buono, Giuseppe Desolda, M. Matera","doi":"10.1145/3099023.3099089","DOIUrl":"https://doi.org/10.1145/3099023.3099089","url":null,"abstract":"This demo presents a platform for the definition of IoT-enhanced visits to Cultural Heritage (CH) sites. The platform is characterized by an End-User Development paradigm applied to the Internet of Things technologies and customized for the CH domain. It allows different stakeholders to configure the behavior of smart objects in order to create more engaging visit experience and to increase the appropriation of CH content by visitors.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133316062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yue Zhao, Dan Davis, Guanliang Chen, C. Lofi, C. Hauff, G. Houben
Massive Open Online Courses (MOOCs) play an ever more central role in open education. However, in contrast to traditional classroom settings, many aspects of learners' behaviour in MOOCs are not well researched. In this work, we focus on modelling learner behaviour in the context of continuous assessments with completion certificates, the most common assessment setup in MOOCs today. Here, learners can obtain a completion certificate once they obtain a required minimal score (typically somewhere between 50-70%) in tests distributed throughout the duration of a MOOC. In this setting, the course material or tests provided after "passing" do not contribute to earning the certificate (which is ungraded), thus potentially affecting learners' behaviour. Therefore, we explore how ``passing'' impacts MOOC learners: do learners alter their behaviour after this point? And if so how? While in traditional classroom-based learning the role of assessment and its influence on learning behaviour has been well-established, we are among the first to provide answers to these questions in the context of MOOCs.
{"title":"Certificate Achievement Unlocked: How Does MOOC Learners' Behaviour Change?","authors":"Yue Zhao, Dan Davis, Guanliang Chen, C. Lofi, C. Hauff, G. Houben","doi":"10.1145/3099023.3099063","DOIUrl":"https://doi.org/10.1145/3099023.3099063","url":null,"abstract":"Massive Open Online Courses (MOOCs) play an ever more central role in open education. However, in contrast to traditional classroom settings, many aspects of learners' behaviour in MOOCs are not well researched. In this work, we focus on modelling learner behaviour in the context of continuous assessments with completion certificates, the most common assessment setup in MOOCs today. Here, learners can obtain a completion certificate once they obtain a required minimal score (typically somewhere between 50-70%) in tests distributed throughout the duration of a MOOC. In this setting, the course material or tests provided after \"passing\" do not contribute to earning the certificate (which is ungraded), thus potentially affecting learners' behaviour. Therefore, we explore how ``passing'' impacts MOOC learners: do learners alter their behaviour after this point? And if so how? While in traditional classroom-based learning the role of assessment and its influence on learning behaviour has been well-established, we are among the first to provide answers to these questions in the context of MOOCs.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114541422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Systems for Community Question Answering (CQA) are well-known on the open web (e.g. Stack Overflow or Quora). They have been recently adopted also for use in educational domain (mostly in MOOCs) to mediate communication between students and teachers. As students are only novices in topics they learn about, they may need various scaffoldings to achieve effective question answering. In this work, we focus specifically on automatic recommendation of tags classifying students' questions. We propose a novel method that can automatically analyze a text of a question and suggest appropriate tags to an asker. The method takes specifics of educational domain into consideration by a two-step recommendation process in which tags reflecting course structure are recommended at first and consequently supplemented with additional related tags. Evaluation of the method on data from CS50 MOOC at Stack Exchange platform showed that the proposed method achieved higher performance in comparison with a baseline method (tag recommendation without taking educational specifics into account).
{"title":"Education-specific Tag Recommendation in CQA Systems","authors":"P. Babinec, Ivan Srba","doi":"10.1145/3099023.3099081","DOIUrl":"https://doi.org/10.1145/3099023.3099081","url":null,"abstract":"Systems for Community Question Answering (CQA) are well-known on the open web (e.g. Stack Overflow or Quora). They have been recently adopted also for use in educational domain (mostly in MOOCs) to mediate communication between students and teachers. As students are only novices in topics they learn about, they may need various scaffoldings to achieve effective question answering. In this work, we focus specifically on automatic recommendation of tags classifying students' questions. We propose a novel method that can automatically analyze a text of a question and suggest appropriate tags to an asker. The method takes specifics of educational domain into consideration by a two-step recommendation process in which tags reflecting course structure are recommended at first and consequently supplemented with additional related tags. Evaluation of the method on data from CS50 MOOC at Stack Exchange platform showed that the proposed method achieved higher performance in comparison with a baseline method (tag recommendation without taking educational specifics into account).","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123863874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Tkalcic, D. Thakker, Panagiotis Germanakos, K. Yacef, Cécile Paris, O. Santos, M. Bieliková, E. Herder, F. Cena, M. Desmarais
Submissions were assigned to 1 TC member and received at least 3 reviews. After the initial reviews were submitted, the designated TC facilitated discussion amongst reviewers in order to resolve differences and correct misunderstandings. The TC then provided a recommendation to the Program Chairs. The final decisions were based on these recommendations, the meta-reviews, and reviewer scores. A total of 131 submissions were reviewed. Out of 80 regular paper submissions, 29 were accepted (36% acceptance rate); out of 51 short paper submissions, 11 were accepted (22% acceptance rate). This year, we did not invite regular papers to be published as short papers, but instead invited them to either be included in the main proceedings as extended abstracts, or be published in the adjunct proceedings Late Breaking Results track (LBR). Six of them were published in the LBR track, and a total of 27 extended abstracts are published in the main proceedings. The program also features 3 demos, 3 theory, opinion and reflection papers and 14 late breaking results papers presented in UMAP poster session, which collectively showcase the wide spectrum of novel ideas and latest results in user modeling, adaptation and personalization. We also invited three distinguished keynote speakers, each illustrating significant issues and prospective directions for the field. Pearl Pu, School of Computer and Communication Sciences at EPFL, describes in her talk the various challenges related to understanding, detecting, and visualizing emotions in large text datasets. Jennifer Golbeck, University of Maryland, focuses on how to consider issues of privacy and consent when users cannot explicitly state their preferences, The Creepy Factor, and how to balance users concerns with the benefits personalized technology can offer. Paul De Bra, Eindhoven University of Technology, discusses in his talk "After twenty-five years of user modeling and adaptation what makes us UMAP?" how the field evolved, insights into where the field is headed, and the hottest topics for exploration. The conference includes a doctoral consortium that provides an opportunity for doctoral students to explore and develop their research interests under the guidance of distinguished scholars. This track received 15 submissions, of which seven were accepted as full papers and six as posters. A set of 8 workshops rounded off the program: EdRecSys: Educational Recommender Systems organized by Kurt Driessens (University of Maastricht, The Netherlands), Irena Koprinska (University of Sydney, Australia), Olga C. Santos (Spanish National University for Distance Education, Spain), Evgueni Smirnov (University of Maastricht, The Netherlands), Kalina Yacef (University of Sydney, Australia), Osmar Zaiane (University of Alberta, Canada) EvalUMAP: Towards Comparative Evaluation in User Modeling, Adaptation and Personalization organized by Owen Conlan, Liadh Kelly, Kevin Koidl, Seamus Lawless, Athanasi
{"title":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","authors":"M. Tkalcic, D. Thakker, Panagiotis Germanakos, K. Yacef, Cécile Paris, O. Santos, M. Bieliková, E. Herder, F. Cena, M. Desmarais","doi":"10.1145/3099023","DOIUrl":"https://doi.org/10.1145/3099023","url":null,"abstract":"Submissions were assigned to 1 TC member and received at least 3 reviews. After the initial reviews were submitted, the designated TC facilitated discussion amongst reviewers in order to resolve differences and correct misunderstandings. The TC then provided a recommendation to the Program Chairs. The final decisions were based on these recommendations, the meta-reviews, and reviewer scores. \u0000 \u0000A total of 131 submissions were reviewed. Out of 80 regular paper submissions, 29 were accepted (36% acceptance rate); out of 51 short paper submissions, 11 were accepted (22% acceptance rate). This year, we did not invite regular papers to be published as short papers, but instead invited them to either be included in the main proceedings as extended abstracts, or be published in the adjunct proceedings Late Breaking Results track (LBR). Six of them were published in the LBR track, and a total of 27 extended abstracts are published in the main proceedings. \u0000 \u0000The program also features 3 demos, 3 theory, opinion and reflection papers and 14 late breaking results papers presented in UMAP poster session, which collectively showcase the wide spectrum of novel ideas and latest results in user modeling, adaptation and personalization. \u0000 \u0000We also invited three distinguished keynote speakers, each illustrating significant issues and prospective directions for the field. \u0000 \u0000Pearl Pu, School of Computer and Communication Sciences at EPFL, describes in her talk the various challenges related to understanding, detecting, and visualizing emotions in large text datasets. \u0000 \u0000Jennifer Golbeck, University of Maryland, focuses on how to consider issues of privacy and consent when users cannot explicitly state their preferences, The Creepy Factor, and how to balance users concerns with the benefits personalized technology can offer. \u0000 \u0000Paul De Bra, Eindhoven University of Technology, discusses in his talk \"After twenty-five years of user modeling and adaptation what makes us UMAP?\" how the field evolved, insights into where the field is headed, and the hottest topics for exploration. \u0000 \u0000The conference includes a doctoral consortium that provides an opportunity for doctoral students to explore and develop their research interests under the guidance of distinguished scholars. This track received 15 submissions, of which seven were accepted as full papers and six as posters. \u0000 \u0000A set of 8 workshops rounded off the program: \u0000EdRecSys: Educational Recommender Systems organized by Kurt Driessens (University of Maastricht, The Netherlands), Irena Koprinska (University of Sydney, Australia), Olga C. Santos (Spanish National University for Distance Education, Spain), Evgueni Smirnov (University of Maastricht, The Netherlands), Kalina Yacef (University of Sydney, Australia), Osmar Zaiane (University of Alberta, Canada) \u0000EvalUMAP: Towards Comparative Evaluation in User Modeling, Adaptation and Personalization organized by Owen Conlan, Liadh Kelly, Kevin Koidl, Seamus Lawless, Athanasi","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129498644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Domenico Giammarino, Davide Feltoni Gurini, A. Micarelli, G. Sansonetti
With the increasing information overload, the identification of new users really relevant to the target user becomes more and more complicated. In this paper, we propose a social recommender based on a user model that takes into account not only her interests and preferences, but also their evolution over time and actual nature. To accurately assess the effectiveness of the proposed approach, over 1,600 users were monitored for a full year, thus collecting over 2,700,000 tweets. In this way, it was possible to deeply evaluate the proposed model, also through a comparative analysis with other state-of-the-art social recommender systems.
{"title":"Social Recommendation with Time and Sentiment Analysis","authors":"Domenico Giammarino, Davide Feltoni Gurini, A. Micarelli, G. Sansonetti","doi":"10.1145/3099023.3099104","DOIUrl":"https://doi.org/10.1145/3099023.3099104","url":null,"abstract":"With the increasing information overload, the identification of new users really relevant to the target user becomes more and more complicated. In this paper, we propose a social recommender based on a user model that takes into account not only her interests and preferences, but also their evolution over time and actual nature. To accurately assess the effectiveness of the proposed approach, over 1,600 users were monitored for a full year, thus collecting over 2,700,000 tweets. In this way, it was possible to deeply evaluate the proposed model, also through a comparative analysis with other state-of-the-art social recommender systems.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126911366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a new probabilistic working memory (WM) model that we intend to use to automatically personalize user interfaces with respect to Alzheimer patients' declining WM capacity. WM is the part of the human memory responsible for the conscious short-term storing and manipulation of information. It is known to be extremely limited and to be one of the strongest factors that impact individual differences in cognitive abilities. In particular, individuals suffering from Alzheimer's disease have significantly impaired WM capacities that worsen as the disease progresses. As a use case for our model, we describe a system that is designed to help patients with Alzheimer's disease choose the music track they would like to listen to from a given playlist. We discuss how our WM model could be used to adapt this system to each patient's disease progression in time and the consequent deterioration of her WM capacity.
{"title":"Oblivion Tracking: Towards a Probabilistic Working Memory Model for the Adaptation of Systems to Alzheimer Patients","authors":"B. Sguerra, P. Jouvelot, Samuel Benveniste","doi":"10.1145/3099023.3099052","DOIUrl":"https://doi.org/10.1145/3099023.3099052","url":null,"abstract":"We introduce a new probabilistic working memory (WM) model that we intend to use to automatically personalize user interfaces with respect to Alzheimer patients' declining WM capacity. WM is the part of the human memory responsible for the conscious short-term storing and manipulation of information. It is known to be extremely limited and to be one of the strongest factors that impact individual differences in cognitive abilities. In particular, individuals suffering from Alzheimer's disease have significantly impaired WM capacities that worsen as the disease progresses. As a use case for our model, we describe a system that is designed to help patients with Alzheimer's disease choose the music track they would like to listen to from a given playlist. We discuss how our WM model could be used to adapt this system to each patient's disease progression in time and the consequent deterioration of her WM capacity.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128265039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is a long tradition in recommender systems research to evaluate systems using quantitative performance measures on fixed datasets. As a reaction to this narrow accuracy-based focus in research, novel qualities beyond pure accuracy are emphasized in recent research; among them are surprise and opposition. This position paper considers that the perception of surprise and/or opposition may be purposely prepared when several recommendations are provided (e.g., in terms of a music playlist) or the user is given the choice between several options. Altering users' perception and triggering according behavior is well rooted in research on priming from psychology and nudge theory from the field of economic behavior. In this position paper, we propose how priming and nudging may be integrated into the design and evaluation of recommender systems to arouse surprise and opposition.
{"title":"Introducing Surprise and Opposition by Design in Recommender Systems","authors":"Christine Bauer, M. Schedl","doi":"10.1145/3099023.3099099","DOIUrl":"https://doi.org/10.1145/3099023.3099099","url":null,"abstract":"There is a long tradition in recommender systems research to evaluate systems using quantitative performance measures on fixed datasets. As a reaction to this narrow accuracy-based focus in research, novel qualities beyond pure accuracy are emphasized in recent research; among them are surprise and opposition. This position paper considers that the perception of surprise and/or opposition may be purposely prepared when several recommendations are provided (e.g., in terms of a music playlist) or the user is given the choice between several options. Altering users' perception and triggering according behavior is well rooted in research on priming from psychology and nudge theory from the field of economic behavior. In this position paper, we propose how priming and nudging may be integrated into the design and evaluation of recommender systems to arouse surprise and opposition.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129937241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Obaro Odiete, Tanvi Jain, I. Adaji, Julita Vassileva, R. Deters
The increasing variety of programming languages available to computer programmers has led to the discussion of what language(s) should be learned. A key point in the choice of a programming language is the availability of support from experienced programmers. In this paper, we explore the use of graph theory in recommending programming languages to novice and expert programmers in a question and answer collaborative learning environment, Stack Overflow. Using social network analysis techniques, we investigate the relationship between experts (using an expertise graph) in different programming languages to identify what languages can be recommended to novice and experienced programmers. In addition, we explore the use of the expertise graph in inferring the importance of a programming language to the community. Our results suggest that programming languages can be recommended within organizational borders and programming domains. In addition, a high number of experts in a programming language does not always mean that the language is popular. Furthermore, disconnected nodes in the expertise graph suggest that experts in some programming languages are primarily on Stack Overflow to support that language only and do not contribute to questions or answers in other languages. Finally, developers are comfortable with mastering a single, general purpose language. The results of our study can help educators and stakeholders in computer education to understand what programming languages can be suggested to students and what languages can be taught and learned together.
{"title":"Recommending Programming Languages by Identifying Skill Gaps Using Analysis of Experts. A Study of Stack Overflow","authors":"Obaro Odiete, Tanvi Jain, I. Adaji, Julita Vassileva, R. Deters","doi":"10.1145/3099023.3099040","DOIUrl":"https://doi.org/10.1145/3099023.3099040","url":null,"abstract":"The increasing variety of programming languages available to computer programmers has led to the discussion of what language(s) should be learned. A key point in the choice of a programming language is the availability of support from experienced programmers. In this paper, we explore the use of graph theory in recommending programming languages to novice and expert programmers in a question and answer collaborative learning environment, Stack Overflow. Using social network analysis techniques, we investigate the relationship between experts (using an expertise graph) in different programming languages to identify what languages can be recommended to novice and experienced programmers. In addition, we explore the use of the expertise graph in inferring the importance of a programming language to the community. Our results suggest that programming languages can be recommended within organizational borders and programming domains. In addition, a high number of experts in a programming language does not always mean that the language is popular. Furthermore, disconnected nodes in the expertise graph suggest that experts in some programming languages are primarily on Stack Overflow to support that language only and do not contribute to questions or answers in other languages. Finally, developers are comfortable with mastering a single, general purpose language. The results of our study can help educators and stakeholders in computer education to understand what programming languages can be suggested to students and what languages can be taught and learned together.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133432732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The research on group recommender systems is often oversimplifying the problem of generating group recommendations, as it is usually only considering the explicit preferences of the group members and, in some cases, enriching these preferences with additional information about the individual members. In this way, an essential aspect is frequently completely neglected: the characterization of the group as an entity with a specific composition and with group-related dynamics. The goal of this paper is multifaceted, firstly, to address the limitations of state-of-the-art approaches, secondly, to describe the problem of group recommendations in a more comprehensive fashion, thirdly, to summarize the results of our previously conducted analyses as a supporting evidence of a need for richer group models, and finally, to discuss an alternative and rather novel approach to group recommendations in the tourism domain. To this end, the results of the group decision-making study with 200 participants in 55 groups are summarized and related to the seven travel factors of the picture-based recommendation system.
{"title":"A Comprehensive Approach to Group Recommendations in the Travel and Tourism Domain","authors":"Amra Delic, J. Neidhardt","doi":"10.1145/3099023.3099076","DOIUrl":"https://doi.org/10.1145/3099023.3099076","url":null,"abstract":"The research on group recommender systems is often oversimplifying the problem of generating group recommendations, as it is usually only considering the explicit preferences of the group members and, in some cases, enriching these preferences with additional information about the individual members. In this way, an essential aspect is frequently completely neglected: the characterization of the group as an entity with a specific composition and with group-related dynamics. The goal of this paper is multifaceted, firstly, to address the limitations of state-of-the-art approaches, secondly, to describe the problem of group recommendations in a more comprehensive fashion, thirdly, to summarize the results of our previously conducted analyses as a supporting evidence of a need for richer group models, and finally, to discuss an alternative and rather novel approach to group recommendations in the tourism domain. To this end, the results of the group decision-making study with 200 participants in 55 groups are summarized and related to the seven travel factors of the picture-based recommendation system.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125740028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Al-Baddai, Barbara Ströhl, E. Lang, Bernd Ludwig
Digital museum guides - often together with eye trackers as innovative gadgets for intuitive interaction - provide attractive new ways for museums to communicate information to visitors and analyze their behaviour. In this paper, we investigate an approach to understand the gaze bedhaviour of persons viewing paintings in a museum. We present a method that can detect focussed areas (AOF) by analysing the fixation duration for the pixels of a painting. We can provide evidence that the viewing behaviour of laymen in a museum differs from what an expert expects according to the art historic relevance of certain regions of interest (ROI) in a painting. Consequently, museum educators have to apply intelligent assistance strategies that allow visitors to fully appreciate exhibits during their visit a of museum.
{"title":"Do Museum Visitors See what Educators Want Them to See?","authors":"S. Al-Baddai, Barbara Ströhl, E. Lang, Bernd Ludwig","doi":"10.1145/3099023.3099086","DOIUrl":"https://doi.org/10.1145/3099023.3099086","url":null,"abstract":"Digital museum guides - often together with eye trackers as innovative gadgets for intuitive interaction - provide attractive new ways for museums to communicate information to visitors and analyze their behaviour. In this paper, we investigate an approach to understand the gaze bedhaviour of persons viewing paintings in a museum. We present a method that can detect focussed areas (AOF) by analysing the fixation duration for the pixels of a painting. We can provide evidence that the viewing behaviour of laymen in a museum differs from what an expert expects according to the art historic relevance of certain regions of interest (ROI) in a painting. Consequently, museum educators have to apply intelligent assistance strategies that allow visitors to fully appreciate exhibits during their visit a of museum.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133079242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：