Naomi Wixon, Sarah E. Schultz, Kasia Muldner, Danielle A. Allessio, W. Burleson, B. Woolf, I. Arroyo
{"title":"Internal & External Attributions for Emotions Within an ITS","authors":"Naomi Wixon, Sarah E. Schultz, Kasia Muldner, Danielle A. Allessio, W. Burleson, B. Woolf, I. Arroyo","doi":"10.1145/2930238.2930277","DOIUrl":"https://doi.org/10.1145/2930238.2930277","url":null,"abstract":"Students self-reported not only their emotional state, but also the causal attributions of their emotions. After coding emotions with internal references to self, and external references to the environment or domain, we examined how sub-groups of students based on internal/external attributions and above or below median performance differ in terms of their emotional state, perceptions of item difficulty, and gender.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124826483","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}
{"title":"Deeper Knowledge Tracing by Modeling Skill Application Context for Better Personalized Learning","authors":"Yun Huang","doi":"10.1145/2930238.2930373","DOIUrl":"https://doi.org/10.1145/2930238.2930373","url":null,"abstract":"Traditional Knowledge Tracing, which traces students' knowledge of each decomposed individual skill, has been a popular learner model for adaptive tutoring. Typically, a student is guided to the next skill when the student's knowledge on current skill is inferred as mastery. Unfortunately, this traditional approach no longer suffices to model complex skill practices where simple decompositions can not capture potential additional skills underlying the context as a whole. In such cases, mastery should only be granted when a student not only understands the basic of a skill but also can fluently apply a skill in varied application contexts. In this thesis, we aim to propose a data-driven approach to construct learner models considering different skill application contexts for tracing deeper knowledge, primarily based on Bayesian Networks. We aim to conduct novel, comprehensive, ``deep\" evaluations, including internal data-drive evaluations, and external end-user evaluations examining the real world impact for students' personalized learning.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"34 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123349944","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}
{"title":"Killing the Hyperlink, Killing the Web: The Shift from Library-Internet to Television-Internet","authors":"Hossein Derakhshan","doi":"10.1145/2930238.2954034","DOIUrl":"https://doi.org/10.1145/2930238.2954034","url":null,"abstract":"The Web, as envisaged by its inventors, was founded on the idea of hyperlinks. Derived from the notion of hypertext in literary theory, a hyperlink is a relation rather than an object. It is a system of connections that connects distant pieces of text, resulting in a non-linear, open, active, decentralized, and diverse space we called the World Wide Web. But in the past few years, and with the rise of closed social networks, as well as mobile apps, the hyperlink - and thereby the Web - are in serious trouble. Most social networks have created a closed, linear, centralized, sequential, passive, and homogeneous space, where users are encouraged to stay in all the time -- a space that is more like television. The Web was imagined as an intellectual project that promoted knowledge, debate, and tolerance; as something I call library-internet. Now it has become more about entertainment and commerce; I call this tv-internet.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"8 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126473474","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}
{"title":"Reinforcement Learning: the Sooner the Better, or the Later the Better?","authors":"Shitian Shen, Min Chi","doi":"10.1145/2930238.2930247","DOIUrl":"https://doi.org/10.1145/2930238.2930247","url":null,"abstract":"Reinforcement Learning (RL) is one of the best machine learning approaches for decision making in interactive environments. RL focuses on inducing effective decision making policies with the goal of maximizing the agent's cumulative reward. In this study, we investigated the impact of both immediate and delayed reward functions on RL-induced policies and empirically evaluated the effectiveness of induced policies within an Intelligent Tutoring System called Deep Thought. Moreover, we divided students into Fast and Slow learners based on their incoming competence as measured by their average response time on the initial tutorial level. Our results show that there was a significant interaction effect between the induced policies and the students' incoming competence. More specifically, Fast learners are less sensitive to learning environments in that they can learn equally well regardless of the pedagogical strategies employed by the tutor, but Slow learners benefit significantly more from effective pedagogical strategies than from ineffective ones. In fact, with effective pedagogical strategies the slow learners learned as much as their faster peers, but with ineffective pedagogical strategies the former learned significantly less than the latter.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126078833","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}
{"title":"Detecting Student Engagement: Human Versus Machine","authors":"Nigel Bosch","doi":"10.1145/2930238.2930371","DOIUrl":"https://doi.org/10.1145/2930238.2930371","url":null,"abstract":"Engagement is complex and multifaceted, but crucial to learning. Computerized learning environments can provide a superior learning experience for students by automatically detecting student engagement (and, thus also disengagement) and adapting to it. This paper describes results from several previous studies that utilized facial features to automatically detect student engagement, and proposes new methods to expand and improve results. Videos of students will be annotated by third-party observers as mind wandering (disengaged) or not mind wandering (engaged). Automatic detectors will also be trained to classify the same videos based on students' facial features, and compared to the machine predictions. These detectors will then be improved by engineering features to capture facial expressions noted by observers and more heavily weighting training instances that were exceptionally-well classified by observers. Finally, implications of previous results and proposed work are discussed.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126667382","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}
{"title":"Exploring Music Diversity Needs Across Countries","authors":"B. Ferwerda, Andreu Vall, M. Tkalcic, M. Schedl","doi":"10.1145/2930238.2930262","DOIUrl":"https://doi.org/10.1145/2930238.2930262","url":null,"abstract":"Providing diversity in recommendations has shown to positively influence the user's subjective evaluations such as satisfaction. However, it is often unknown how much diversity a recommendation set needs to consist of. In this work, we explored how music users of Last.fm apply diversity in their listening behavior. We analyzed a dataset with the music listening history of 53,309 Last.fm users capturing their total listening events until August 2014. We complemented this dataset with The Echo Nest features and Hofstede's cultural dimensions to explore how music diversity is applied across countries. Between 47 countries, we found distinct relationships between the cultural dimensions and music diversity variables. These results suggest that different country-based diversity measurements should be considered when applied to a recommendation set in order to maximize the user's subjective evaluations. The country-based relationships also provide opportunities for recommender systems to personalize experiences when user data is limited by being able to rely on the user's demographics.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114964023","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}
{"title":"User-Oriented Context Suggestion","authors":"Yong Zheng, B. Mobasher, R. Burke","doi":"10.1145/2930238.2930252","DOIUrl":"https://doi.org/10.1145/2930238.2930252","url":null,"abstract":"Recommender systems have been used in many domains to assist users' decision making by providing item recommendations and thereby reducing information overload. Context-aware recommender systems go further, incorporating the variability of users' preferences across contexts, and suggesting items that are appropriate in different contexts. In this paper, we present a novel recommendation task, \"Context Suggestion\", whereby the system recommends contexts in which items may be selected. We introduce the motivations behind the notion of context suggestion and discuss several potential solutions. In particular, we focus specifically on user-oriented context suggestion which involves recommending appropriate contexts based on a user's profile. We propose extensions of well-known context-aware recommendation algorithms such as tensor factorization and deviation-based contextual modeling and adapt them as methods to recommend contexts instead of items. In our empirical evaluation, we compare the proposed solutions to several baseline algorithms using four real-world data sets.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124911362","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}
Kun Yu, S. Berkovsky, Dan Conway, R. Taib, Jianlong Zhou, Fang Chen
{"title":"Trust and Reliance Based on System Accuracy","authors":"Kun Yu, S. Berkovsky, Dan Conway, R. Taib, Jianlong Zhou, Fang Chen","doi":"10.1145/2930238.2930290","DOIUrl":"https://doi.org/10.1145/2930238.2930290","url":null,"abstract":"Trust plays an important role in various user-facing systems and applications. It is particularly important in the context of decision support systems, where the system's output serves as one of the inputs for the users' decision making processes. In this work, we study the dynamics of explicit and implicit user trust in a simulated automated quality monitoring system, as a function of the system accuracy. We establish that users correctly perceive the accuracy of the system and adjust their trust accordingly.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115694392","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}
{"title":"Gender Difference in the Credibility Perception of Mobile Websites: A Mixed Method Approach","authors":"Kiemute Oyibo, Yusuf Sahabi Ali, Julita Vassileva","doi":"10.1145/2930238.2930245","DOIUrl":"https://doi.org/10.1145/2930238.2930245","url":null,"abstract":"To persuade people to buy a product or service online, they must be visually convinced and attracted to use the sales website. Thus, there is need to understand how different user groups perceive various designs of websites for better adaptation. A lot of research has shown that users' judgment of the credibility of a website is critical to its success. However, in the mobile domain, little has been done empirically to 1) investigate users' credibility perception of a website; and 2) how it changes as the user interface (UI) design is systematically altered. This paper bridges this gap by carrying out sentiment and statistical analyses of users' perceptions of four systematically modified mobile websites among 285 subjects from North America, Africa and Asia. The results show that mobile website design affects the perception of its credibility, with 1) females being more critical and sensitive to UI changes than males; and 2) the grid-layout website design preferred to the list-layout website design by both genders. The study contributes to knowledge in three ways. First, it provides a concise model for understanding users' UI perceptions, expectations and gender differences. Second, it presents important findings that will enable a gender-based mobile website adaptation. Third, it provides a set of empirically backed guidelines for mobile web design.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122428429","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}
{"title":"An Eye-Tracking Study: Implication to Implicit Critiquing Feedback Elicitation in Recommender Systems","authors":"Li Chen, Feng Wang","doi":"10.1145/2930238.2930286","DOIUrl":"https://doi.org/10.1145/2930238.2930286","url":null,"abstract":"The critiquing-based recommender system (CBRS) stimulates users to critique the recommended item in terms of its attribute values. It has been shown that such critiquing feedback can effectively improve users' decision quality, especially in complex decision environments such as e-commerce, tourism, and finance. However, because its explicit elicitation process unavoidably demands extra user efforts, the application in real situations is limited. In this paper, we report an eye-tracking experiment with the objective of studying the relationship between users' eye gazes as laid on recommended items and their critiquing feedback. The results indicate the feasibility of inferring users' feedback based on their eye movements. It hence points out a promising roadmap to developing unobtrusive eye-based feedback elicitation for recommender systems.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114919901","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}