Ayseguel Dogangün, Michael Schwarz, Katharina Kloppenborg, Robert Le
Daily physical activity not only empowers the body, but it also invigorates the mind and helps people cope with the struggle of everyday life. A balanced amount of moderate to vigorous physical activity is recommended. Major barriers that lead to low levels of physical activity are lack of time and motivation. The objective of this paper is to generate individual recommendations to improve physical activity by using if-then plans - so called Implementation Intentions. We developed a mobile application named DayActivizer to collect all the necessary activity data by the user. Based on the collected data, the application automatically recommends activities within if-then plans with an increasing degree of physical effort to counteract insufficient physical exercise concerning individual daily routine. To evaluate our approach, we conducted a field study (N=8) and qualitative interviews in which every participant was asked to examine the validity of the individual recommended implementation intentions.
{"title":"An Approach to Improve Physical Activity by Generating Individual Implementation Intentions","authors":"Ayseguel Dogangün, Michael Schwarz, Katharina Kloppenborg, Robert Le","doi":"10.1145/3099023.3099101","DOIUrl":"https://doi.org/10.1145/3099023.3099101","url":null,"abstract":"Daily physical activity not only empowers the body, but it also invigorates the mind and helps people cope with the struggle of everyday life. A balanced amount of moderate to vigorous physical activity is recommended. Major barriers that lead to low levels of physical activity are lack of time and motivation. The objective of this paper is to generate individual recommendations to improve physical activity by using if-then plans - so called Implementation Intentions. We developed a mobile application named DayActivizer to collect all the necessary activity data by the user. Based on the collected data, the application automatically recommends activities within if-then plans with an increasing degree of physical effort to counteract insufficient physical exercise concerning individual daily routine. To evaluate our approach, we conducted a field study (N=8) and qualitative interviews in which every participant was asked to examine the validity of the individual recommended implementation intentions.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"14 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114028631","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 are happy to present the 8 workshops and 2 tutorials selected for the 25th ACM Conference on User Modeling, Adaptation, and Personalization (UMAP 2017) from the proposals we received in response to the corresponding calls for workshops and tutorials. The workshops provide a venue to discuss and explore emerging areas of User Modelling and Adaptive Hypermedia research with a group of like-minded researchers and practitioners from Industry and Academia. The discussions are focused on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. The workshops held in conjunction with UMAP 2017 cover a wide range of topics.
{"title":"UMAP'17 Workshops & Tutorials Chairs' Introduction","authors":"Cécile Paris, O. Santos","doi":"10.1145/3099023.3099117","DOIUrl":"https://doi.org/10.1145/3099023.3099117","url":null,"abstract":"We are happy to present the 8 workshops and 2 tutorials selected for the 25th ACM Conference on User Modeling, Adaptation, and Personalization (UMAP 2017) from the proposals we received in response to the corresponding calls for workshops and tutorials. The workshops provide a venue to discuss and explore emerging areas of User Modelling and Adaptive Hypermedia research with a group of like-minded researchers and practitioners from Industry and Academia. The discussions are focused on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. The workshops held in conjunction with UMAP 2017 cover a wide range of topics.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"7 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":"123709739","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}
J. Boticario, O. Santos, Raúl Cabestrero, Pilar Quirós, Sergio Salmeron-Majadas, Raul Uria-Rivas, Mar Saneiro, M. Arevalillo-Herráez, F. Ferri
Recent research has provided solid evidence that emotions strongly affect motivation and engagement, and hence play an important role in learning. In BIG-AFF project, we build on the hypothesis that ``it is possible to provide learners with a personalised support that enriches their learning process and experience by using low intrusive (and low cost) devices to capture affective multimodal data that include cognitive, behavioural and physiological information''. In order to deal with the affect management complete cycle, thus covering affect detection, modelling and feedback, there is lack of standards and consolidated methodologies. Being our goal to develop realistic affect-aware learning environments, we are exploring different approaches on how these can be supported by either by traditional non-intrusive interaction sources or low intrusive and inexpensive sensing devices. In this work we describe the main issues involved in two user studies carried out with high school learners, highlight some open problems that arose when designing the corresponding experimental settings. In particular, the studies involved varied nature of information sources and each focused on one of the approaches. Our experience reflects the need to develop an extensive knowledge about the organization of this type of experiences that consider user-centric development and evaluation methodologies.
{"title":"BIG-AFF: Exploring Low Cost and Low Intrusive Infrastructures for Affective Computing in Secondary Schools","authors":"J. Boticario, O. Santos, Raúl Cabestrero, Pilar Quirós, Sergio Salmeron-Majadas, Raul Uria-Rivas, Mar Saneiro, M. Arevalillo-Herráez, F. Ferri","doi":"10.1145/3099023.3099084","DOIUrl":"https://doi.org/10.1145/3099023.3099084","url":null,"abstract":"Recent research has provided solid evidence that emotions strongly affect motivation and engagement, and hence play an important role in learning. In BIG-AFF project, we build on the hypothesis that ``it is possible to provide learners with a personalised support that enriches their learning process and experience by using low intrusive (and low cost) devices to capture affective multimodal data that include cognitive, behavioural and physiological information''. In order to deal with the affect management complete cycle, thus covering affect detection, modelling and feedback, there is lack of standards and consolidated methodologies. Being our goal to develop realistic affect-aware learning environments, we are exploring different approaches on how these can be supported by either by traditional non-intrusive interaction sources or low intrusive and inexpensive sensing devices. In this work we describe the main issues involved in two user studies carried out with high school learners, highlight some open problems that arose when designing the corresponding experimental settings. In particular, the studies involved varied nature of information sources and each focused on one of the approaches. Our experience reflects the need to develop an extensive knowledge about the organization of this type of experiences that consider user-centric development and evaluation methodologies.","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":"125210846","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}
Callum Parker, Joel Fredericks, M. Tomitsch, Soojeong Yoo
Public interactive displays (PIDs) are becoming more pervasive in urban environments as a means to engage passers-by and to provide interactive features such as wayfinding. However, one of the problems with current PIDs is that they are typically designed around an average specification, potentially excluding a large range of users that for instance might not be able to reach interactive elements. To address this challenge, we propose a number of design concepts for adjusting PIDs to users of different heights. We present a preliminary evaluation of our concepts through a cognitive walk-through study with 10 design experts using a custom-developed experience prototype featuring four height-aware modes. Based on qualitative feedback and observations we discuss design suggestions for future work.
{"title":"Towards Adaptive Height-Aware Public Interactive Displays","authors":"Callum Parker, Joel Fredericks, M. Tomitsch, Soojeong Yoo","doi":"10.1145/3099023.3099060","DOIUrl":"https://doi.org/10.1145/3099023.3099060","url":null,"abstract":"Public interactive displays (PIDs) are becoming more pervasive in urban environments as a means to engage passers-by and to provide interactive features such as wayfinding. However, one of the problems with current PIDs is that they are typically designed around an average specification, potentially excluding a large range of users that for instance might not be able to reach interactive elements. To address this challenge, we propose a number of design concepts for adjusting PIDs to users of different heights. We present a preliminary evaluation of our concepts through a cognitive walk-through study with 10 design experts using a custom-developed experience prototype featuring four height-aware modes. Based on qualitative feedback and observations we discuss design suggestions for future work.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"19 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":"130295153","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}
Evaluation of user modeling techniques is often based on the predictive accuracy of models. The quantification of predictive accuracy is done using performance metrics. We show that the choice of a performance metric is important and that even details of metric computation matter. We analyze in detail two commonly used metrics (AUC, RMSE) in the context of student modeling. We discuss different approaches to their computation (global, averaging across skill, averaging across students) and show that these methods have different properties. An analysis of recent research papers shows that the reported descriptions of metric computation are often insufficient. To make research conclusions valid and reproducible, researchers need to pay more attention to the choice of performance metrics and they need to describe more explicitly details of their computation.
{"title":"Measuring Predictive Performance of User Models: The Details Matter","authors":"Radek Pelánek","doi":"10.1145/3099023.3099042","DOIUrl":"https://doi.org/10.1145/3099023.3099042","url":null,"abstract":"Evaluation of user modeling techniques is often based on the predictive accuracy of models. The quantification of predictive accuracy is done using performance metrics. We show that the choice of a performance metric is important and that even details of metric computation matter. We analyze in detail two commonly used metrics (AUC, RMSE) in the context of student modeling. We discuss different approaches to their computation (global, averaging across skill, averaging across students) and show that these methods have different properties. An analysis of recent research papers shows that the reported descriptions of metric computation are often insufficient. To make research conclusions valid and reproducible, researchers need to pay more attention to the choice of performance metrics and they need to describe more explicitly details of their computation.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"53 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":"128815748","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}
Individualized and personalized learning has taken on different forms in the context of digital learning environments. In intelligent tutoring systems, individualization is focused on estimation of the cognitive mastery of the student and the speed at which the student progresses through the material is conditioned on her individual rate of mastery. In prior work, a recommendation framework based on learner behaviors, rather than learner's cognitive abilities, was proposed and developed. This framework trained a behavior model on millions of previous student actions in order to estimate how a future learner might behave. This behavior model can incorporate the amount of time spent on each course page, such that the model can take into account a learner's previous behaviors and provide a specific course page recommendation to where the learner may want to go next where they can be expected to spend a significant amount of time on. We stipulate that this approach touches on factors more aligned with personalization, since the prediction of behavior is an aggregation of the student's cognitive abilities, affective state, and preferences. This model was applied to a hand-picked pair of MOOC offerings where model results were expected to be favorable. In this paper, we investigate the suitability of this behavioral prediction approach by applying it to an expanded set of 13 UC Berkeley MOOCs run on the edX platform. Preliminary results from applying the time-augmented Recurrent Neural Network (RNN) based behavior model approach are presented and compared to baseline models. These findings contribute to the discussion of when and in what context this form of collaborative based personalized recommendation is appropriate in MOOCs.
{"title":"Personalized Behavior Recommendation: A Case Study of Applicability to 13 Courses on edX","authors":"Steven Tang, Z. Pardos","doi":"10.1145/3099023.3099038","DOIUrl":"https://doi.org/10.1145/3099023.3099038","url":null,"abstract":"Individualized and personalized learning has taken on different forms in the context of digital learning environments. In intelligent tutoring systems, individualization is focused on estimation of the cognitive mastery of the student and the speed at which the student progresses through the material is conditioned on her individual rate of mastery. In prior work, a recommendation framework based on learner behaviors, rather than learner's cognitive abilities, was proposed and developed. This framework trained a behavior model on millions of previous student actions in order to estimate how a future learner might behave. This behavior model can incorporate the amount of time spent on each course page, such that the model can take into account a learner's previous behaviors and provide a specific course page recommendation to where the learner may want to go next where they can be expected to spend a significant amount of time on. We stipulate that this approach touches on factors more aligned with personalization, since the prediction of behavior is an aggregation of the student's cognitive abilities, affective state, and preferences. This model was applied to a hand-picked pair of MOOC offerings where model results were expected to be favorable. In this paper, we investigate the suitability of this behavioral prediction approach by applying it to an expanded set of 13 UC Berkeley MOOCs run on the edX platform. Preliminary results from applying the time-augmented Recurrent Neural Network (RNN) based behavior model approach are presented and compared to baseline models. These findings contribute to the discussion of when and in what context this form of collaborative based personalized recommendation is appropriate in MOOCs.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"101 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":"125584241","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}
Cultural heritage (CH) is an attractive domain for experimenting with novel technologies for various reasons. In general, the research focuses is on experimenting the potential of the novel technology, while having a high-quality content is necessary for experimentation in a realistic setting, but it is not the focus of the research. While generally ignored, it seems that automatic content creation is one of the main challenges for wide adoption of CH application in practice. Some effort was invested in automating the process without providing a real solution. It seems that recent semantic web techniques and large content digitization and standardization efforts pave the way for trying again to suggest ways for automatic creation of personalized and context aware coherent presentations from freely available content on the fly.
{"title":"When will Cultural Heritage Content Creation Get to the Digital Age?","authors":"T. Kuflik, A. L. Bue, O. Stock, A. Wecker","doi":"10.1145/3099023.3099091","DOIUrl":"https://doi.org/10.1145/3099023.3099091","url":null,"abstract":"Cultural heritage (CH) is an attractive domain for experimenting with novel technologies for various reasons. In general, the research focuses is on experimenting the potential of the novel technology, while having a high-quality content is necessary for experimentation in a realistic setting, but it is not the focus of the research. While generally ignored, it seems that automatic content creation is one of the main challenges for wide adoption of CH application in practice. Some effort was invested in automating the process without providing a real solution. It seems that recent semantic web techniques and large content digitization and standardization efforts pave the way for trying again to suggest ways for automatic creation of personalized and context aware coherent presentations from freely available content on the fly.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"8 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":"122093840","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 growing interests in integration of Internet of Things (IoT) in smart environments, which creates an opportunity to understand users' information needs using onsite physical sensor logs. However, the physical context creates numerous external factors that play a role in users' information interactions, thus creating new external biases in the collected information interaction logs. In order to provide an effective personalized experiences for users in smart environment, we need to take care of these external biases in the behavioral user models. Our general aim is to understand users' onsite physical behaviors for providing online and onsite personalized services like personalized tour guides. We focus on the cultural heritage domain and collect onsite users' physical information interaction logs of visits in a museum. This prompts the question: How to understand users' behavior in the existence of external biases? Our main finding is that users behave differently in their solitude in comparison to a busy museum situation. Specifically, visitors' crowd bias has a considerable effect on users' following position rank bias based check-in behavior. Our study investigates on understanding users' onsite physical behavior accurately, which can improve the state-of-the-art onsite behavioral user models.
{"title":"Busy versus Empty Museums: Effects of Visitors' Crowd on Users' Behaviors in Smart Museums","authors":"Seyyed Hadi Hashemi, J. Kamps, W. Hupperetz","doi":"10.1145/3099023.3099088","DOIUrl":"https://doi.org/10.1145/3099023.3099088","url":null,"abstract":"There is a growing interests in integration of Internet of Things (IoT) in smart environments, which creates an opportunity to understand users' information needs using onsite physical sensor logs. However, the physical context creates numerous external factors that play a role in users' information interactions, thus creating new external biases in the collected information interaction logs. In order to provide an effective personalized experiences for users in smart environment, we need to take care of these external biases in the behavioral user models. Our general aim is to understand users' onsite physical behaviors for providing online and onsite personalized services like personalized tour guides. We focus on the cultural heritage domain and collect onsite users' physical information interaction logs of visits in a museum. This prompts the question: How to understand users' behavior in the existence of external biases? Our main finding is that users behave differently in their solitude in comparison to a busy museum situation. Specifically, visitors' crowd bias has a considerable effect on users' following position rank bias based check-in behavior. Our study investigates on understanding users' onsite physical behavior accurately, which can improve the state-of-the-art onsite behavioral user models.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"18 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":"122781771","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}
This paper describes and proposes a community evaluation task that is designed for evaluating learning systems that can automatically identify different types of problems, that students may encounter with their online courses. As a basis, the learning systems would use logs from an artificial learning environment to analyse the student interactions and behaviour with the online course. The learning systems will also use specific domain models to ensure that the course requirements such as task deadlines and learning content conditions (e.g., pre-requisites) are addressed. As a result, the outputs (identified student problems) can be used by a) the learning systems to provide personalised feedback and direction to students to overcome a problem b) notify an instructor for a more professional support and response c) inform a learning designer for potential problems on the design of the course.
{"title":"Proposing an Evaluation Task for Identifying Struggling Students in Online Courses","authors":"A. Staikopoulos, Owen Conlan","doi":"10.1145/3099023.3099049","DOIUrl":"https://doi.org/10.1145/3099023.3099049","url":null,"abstract":"This paper describes and proposes a community evaluation task that is designed for evaluating learning systems that can automatically identify different types of problems, that students may encounter with their online courses. As a basis, the learning systems would use logs from an artificial learning environment to analyse the student interactions and behaviour with the online course. The learning systems will also use specific domain models to ensure that the course requirements such as task deadlines and learning content conditions (e.g., pre-requisites) are addressed. As a result, the outputs (identified student problems) can be used by a) the learning systems to provide personalised feedback and direction to students to overcome a problem b) notify an instructor for a more professional support and response c) inform a learning designer for potential problems on the design of the course.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"7 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":"131930572","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}
Gamification in the era of chatbots is a novel way to engage users with the chatbot application. When developing a gamified chatbot system, there are factors related to user types (ages, gender and others) that we should consider to effectively integrate the game elements into the chatbot while targeting the right audience. In this study, we discuss the development of an educational chatbot game 'CiboPoli', that's specialised in teaching children about healthy lifestyle through an interactive social game environment. The presented game is based on a paper prototype that we developed to teach primary school students about healthy diet and food waste management. The current approach will be more engaging and pose AI capabilities. This is still a work in progress and we plan to improve its design by incorporating additional components, such as dialog management module, user-specific knowledge module or machine learning module. Future work will be devoted to integrating machine learning to automatically identify learners emotions and provide personalised suggestions. Moreover, we tested the initial prototype with school students and found that it outperforms the paper version. Future work will focus on applying it to other domains and demographics.
{"title":"An Adaptive Learning with Gamification & Conversational UIs: The Rise of CiboPoliBot","authors":"Ahmed Fadhil, Adolfo Villafiorita","doi":"10.1145/3099023.3099112","DOIUrl":"https://doi.org/10.1145/3099023.3099112","url":null,"abstract":"Gamification in the era of chatbots is a novel way to engage users with the chatbot application. When developing a gamified chatbot system, there are factors related to user types (ages, gender and others) that we should consider to effectively integrate the game elements into the chatbot while targeting the right audience. In this study, we discuss the development of an educational chatbot game 'CiboPoli', that's specialised in teaching children about healthy lifestyle through an interactive social game environment. The presented game is based on a paper prototype that we developed to teach primary school students about healthy diet and food waste management. The current approach will be more engaging and pose AI capabilities. This is still a work in progress and we plan to improve its design by incorporating additional components, such as dialog management module, user-specific knowledge module or machine learning module. Future work will be devoted to integrating machine learning to automatically identify learners emotions and provide personalised suggestions. Moreover, we tested the initial prototype with school students and found that it outperforms the paper version. Future work will focus on applying it to other domains and demographics.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"43 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":"127569591","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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