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}
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}
Exercise is essential for health and well-being. However, it can be difficult for people to meet the recommended amount of daily exercise simply due to the lack of motivation. It has recently become apparent that virtual reality games, even though they were not explicitly designed for exercise, have the potential to provide enough exercise to achieve recommended levels of activity for a day, while keeping people motivated. However, as these games have not generally not been designed for exercise, there is a risk that people may either under- or over-exert themselves. Therefore, in this paper we present and discuss our design for a virtual reality exergame that utilizes a user model and dynamic difficulty adjustment to deliver personalized activity levels and experiences.
{"title":"Designing a Personalized VR Exergame","authors":"Soojeong Yoo, Callum Parker, J. Kay","doi":"10.1145/3099023.3099115","DOIUrl":"https://doi.org/10.1145/3099023.3099115","url":null,"abstract":"Exercise is essential for health and well-being. However, it can be difficult for people to meet the recommended amount of daily exercise simply due to the lack of motivation. It has recently become apparent that virtual reality games, even though they were not explicitly designed for exercise, have the potential to provide enough exercise to achieve recommended levels of activity for a day, while keeping people motivated. However, as these games have not generally not been designed for exercise, there is a risk that people may either under- or over-exert themselves. Therefore, in this paper we present and discuss our design for a virtual reality exergame that utilizes a user model and dynamic difficulty adjustment to deliver personalized activity levels and experiences.","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":"130904713","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}
A. D. Angelis, Fabio Gasparetti, A. Micarelli, G. Sansonetti
This article describes a recommender system (RS) in the cultural heritage area, which takes into account the activities on social media performed by the target user and her friends. For this purpose, the system exploits linked open data (LOD) as well. More specifically, the proposed RS (i) extracts information from social networks (e.g., Facebook) by analyzing content generated by users and those included in their social networks; (ii) performs disambiguation tasks through LOD tools; (iii) profiles the user as a social graph; (iv) provides the actual user with personalized suggestions of artistic and cultural resources by integrating collaborative filtering algorithms with semantic technologies for leveraging LOD sources such as DBpedia and Europeana.
{"title":"A Social Cultural Recommender based on Linked Open Data","authors":"A. D. Angelis, Fabio Gasparetti, A. Micarelli, G. Sansonetti","doi":"10.1145/3099023.3099092","DOIUrl":"https://doi.org/10.1145/3099023.3099092","url":null,"abstract":"This article describes a recommender system (RS) in the cultural heritage area, which takes into account the activities on social media performed by the target user and her friends. For this purpose, the system exploits linked open data (LOD) as well. More specifically, the proposed RS (i) extracts information from social networks (e.g., Facebook) by analyzing content generated by users and those included in their social networks; (ii) performs disambiguation tasks through LOD tools; (iii) profiles the user as a social graph; (iv) provides the actual user with personalized suggestions of artistic and cultural resources by integrating collaborative filtering algorithms with semantic technologies for leveraging LOD sources such as DBpedia and Europeana.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"11 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":"134403592","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}
Many studies have sought to understand the behavior of music listeners to design an improved music listening experience. This is especially important in music recommendation systems in that listening behavior can directly relate to the purpose of the system. For example, a listener who likes to discover new music will be more satisfied with a list of suggestions that present different types of music, while others prefer to listen to their same old music and artists. Previous research has focused on performing user research to explicitly extract information about listening behavior but few studies have attempted a data-driven approach to suggest listener personas or groups. In this study, we applied two clustering methods to user playrate distribution data to see if meaningful user clusters appear, and performed analysis on the results by comparing the patterns of the result clusters with the major characteristics of listener groups derived from previous user researches. Our experiments show that two large clusters and two small clusters are formed, with each cluster representing an intuitive difference in terms of listening behavior.
{"title":"A Data-driven Approach to Identifying Music Listener Groups based on Users' Playrate Distributions of Listening Events","authors":"Sooyeon Yoo, Kyogu Lee","doi":"10.1145/3099023.3099075","DOIUrl":"https://doi.org/10.1145/3099023.3099075","url":null,"abstract":"Many studies have sought to understand the behavior of music listeners to design an improved music listening experience. This is especially important in music recommendation systems in that listening behavior can directly relate to the purpose of the system. For example, a listener who likes to discover new music will be more satisfied with a list of suggestions that present different types of music, while others prefer to listen to their same old music and artists. Previous research has focused on performing user research to explicitly extract information about listening behavior but few studies have attempted a data-driven approach to suggest listener personas or groups. In this study, we applied two clustering methods to user playrate distribution data to see if meaningful user clusters appear, and performed analysis on the results by comparing the patterns of the result clusters with the major characteristics of listener groups derived from previous user researches. Our experiments show that two large clusters and two small clusters are formed, with each cluster representing an intuitive difference in terms of listening behavior.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"20 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":"132657854","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 exploration of cultural heritage information is challenged by the fact that most data provided by online resources is fragmented and it is repository or application-centered. In order to address this issue, a data integration approach should be adopted, that makes it possible to generate custom views, focused on the user's information needs, but easily extensible to support the inspection of topically related contents. In this paper, we present a model supporting the management of thematic maps for information exploration, and their integration with query expansion during the interaction with the user. Our model is based on: (i) an ontological domain knowledge representation for describing the meaning of concepts and their semantic relations; (ii) a semantic interpretation model for identifying the concepts referenced in the user's queries. We are experimenting our model in the OnToMap Participatory GIS, which manages interactive community maps for information sharing and participatory decision-making.
{"title":"Thematic Maps for Geographical Information Search","authors":"Noemi Mauro, L. Ardissono","doi":"10.1145/3099023.3099087","DOIUrl":"https://doi.org/10.1145/3099023.3099087","url":null,"abstract":"The exploration of cultural heritage information is challenged by the fact that most data provided by online resources is fragmented and it is repository or application-centered. In order to address this issue, a data integration approach should be adopted, that makes it possible to generate custom views, focused on the user's information needs, but easily extensible to support the inspection of topically related contents. In this paper, we present a model supporting the management of thematic maps for information exploration, and their integration with query expansion during the interaction with the user. Our model is based on: (i) an ontological domain knowledge representation for describing the meaning of concepts and their semantic relations; (ii) a semantic interpretation model for identifying the concepts referenced in the user's queries. We are experimenting our model in the OnToMap Participatory GIS, which manages interactive community maps for information sharing and participatory decision-making.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"16 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":"117215183","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}
Common design practices of current cultural heritage activities barely take into account the contextual, cultural, and cognitive characteristics of visitors. Bearing in mind that information processing is substantial in such activities, this paper investigates the interplay among human cognitive differences and cultural heritage gaming activities towards players' performance and visual behavior. Three user studies were conducted under the field dependence/independence theory, which underpin cognitive differences in visual perceptiveness and contextual information handling. Findings are expected to provide useful insights for practitioners and researchers with the aim to design playful cultural activities tailored to the users' cognitive preferences.
{"title":"Cultural Heritage Gaming: Effects of Human Cognitive Styles on Players' Performance and Visual Behavior","authors":"G. Raptis, C. Fidas, N. Avouris","doi":"10.1145/3099023.3099090","DOIUrl":"https://doi.org/10.1145/3099023.3099090","url":null,"abstract":"Common design practices of current cultural heritage activities barely take into account the contextual, cultural, and cognitive characteristics of visitors. Bearing in mind that information processing is substantial in such activities, this paper investigates the interplay among human cognitive differences and cultural heritage gaming activities towards players' performance and visual behavior. Three user studies were conducted under the field dependence/independence theory, which underpin cognitive differences in visual perceptiveness and contextual information handling. Findings are expected to provide useful insights for practitioners and researchers with the aim to design playful cultural activities tailored to the users' cognitive preferences.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"6 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":"123921059","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}
Recommender systems have become important tools to support users in identifying relevant content in an overloaded information space. To ease the development of recommender systems, a number of recommender frameworks have been proposed that serve a wide range of application domains. Our TagRec framework is one of the few examples of an open-source framework tailored towards developing and evaluating tag-based recommender systems. In this paper, we present the current, updated state of TagRec, and we summarize and reflect on four use cases that have been implemented with TagRec: (i) tag recommendations, (ii) resource recommendations, (iii) recommendation evaluation, and (iv) hashtag recommendations. To date, TagRec served the development and/or evaluation process of tag-based recommender systems in two large scale European research projects, which have been described in 17 research papers. Thus, we believe that this work is of interest for both researchers and practitioners of tag-based recommender systems.
{"title":"The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems","authors":"Dominik Kowald, Simone Kopeinik, E. Lex","doi":"10.1145/3099023.3099069","DOIUrl":"https://doi.org/10.1145/3099023.3099069","url":null,"abstract":"Recommender systems have become important tools to support users in identifying relevant content in an overloaded information space. To ease the development of recommender systems, a number of recommender frameworks have been proposed that serve a wide range of application domains. Our TagRec framework is one of the few examples of an open-source framework tailored towards developing and evaluating tag-based recommender systems. In this paper, we present the current, updated state of TagRec, and we summarize and reflect on four use cases that have been implemented with TagRec: (i) tag recommendations, (ii) resource recommendations, (iii) recommendation evaluation, and (iv) hashtag recommendations. To date, TagRec served the development and/or evaluation process of tag-based recommender systems in two large scale European research projects, which have been described in 17 research papers. Thus, we believe that this work is of interest for both researchers and practitioners of tag-based recommender systems.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"27 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":"124546260","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}
In a rapidly growing mobile device market, a new and unique type of IT product, the smartwatch, has started gaining the users' attention. After several years of technological development, it has finally become a viable device that extends the functions of a smartphone to a more intimate level. In the industry of smart devices, particularly in wearable devices, smartwatches are widely considered as the next big thing which is going to have a significant impact on our daily lives. However, smartwatches still have a limited use in public transport information systems. In this paper, we present promising use cases for smartwatches in public transport which were extracted through a survey questionnaire we conducted. We designed and developed a prototype to realize one of the most promising use cases: real-time public transport navigation. We evaluated our prototype in a comprehensive user study. The comparison of the smartwatch-based navigation with a pure smartphone-based solution shows that the smartwatch outperforms the smartphone in all user experience metrics.
{"title":"Real-Time Public Transport Navigation on Smartwatches: A Comparison with a Smartphone-based Solution","authors":"S. Siddiqui, Daniel Herzog, W. Wörndl","doi":"10.1145/3099023.3099053","DOIUrl":"https://doi.org/10.1145/3099023.3099053","url":null,"abstract":"In a rapidly growing mobile device market, a new and unique type of IT product, the smartwatch, has started gaining the users' attention. After several years of technological development, it has finally become a viable device that extends the functions of a smartphone to a more intimate level. In the industry of smart devices, particularly in wearable devices, smartwatches are widely considered as the next big thing which is going to have a significant impact on our daily lives. However, smartwatches still have a limited use in public transport information systems. In this paper, we present promising use cases for smartwatches in public transport which were extracted through a survey questionnaire we conducted. We designed and developed a prototype to realize one of the most promising use cases: real-time public transport navigation. We evaluated our prototype in a comprehensive user study. The comparison of the smartwatch-based navigation with a pure smartphone-based solution shows that the smartwatch outperforms the smartphone in all user experience metrics.","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":"115899202","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}
Although there is abundant evidence that individual differences such as cognitive abilities impact visualization effectiveness, this influence has mostly been shown for fictional tasks/scenarios. This paper extends previous findings by investigating the impact of individual differences on user experience with a real-world information visualization tool designed to support preferential choices in public engagement. We show that several cognitive abilities do have an influence on user experience in this task, and show that this influence can be explained by eye tracking. We discuss how these results are promising towards the design of visualizations for preferential choice in public engagement that can adapt to the user's needs, abilities and expertise.
{"title":"Impact of Individual Differences on User Experience with a Visualization Interface for Public Engagement","authors":"Sébastien Lallé, C. Conati, G. Carenini","doi":"10.1145/3099023.3099055","DOIUrl":"https://doi.org/10.1145/3099023.3099055","url":null,"abstract":"Although there is abundant evidence that individual differences such as cognitive abilities impact visualization effectiveness, this influence has mostly been shown for fictional tasks/scenarios. This paper extends previous findings by investigating the impact of individual differences on user experience with a real-world information visualization tool designed to support preferential choices in public engagement. We show that several cognitive abilities do have an influence on user experience in this task, and show that this influence can be explained by eye tracking. We discuss how these results are promising towards the design of visualizations for preferential choice in public engagement that can adapt to the user's needs, abilities and expertise.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"12 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":"130431313","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}
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