We have become increasingly reliant on recommender systems to help us make decisions in our daily live. As such, it is becoming essential to explain to users how these systems reason to enable them to correct system assumptions and to trust the system. The advantages of explaining the recommendation process has been shown by a vast amount of research. Additionally, previous studies showed that personality affects users' attitudes, tastes and information processing. However, it is still unclear whether personality has an impact on the way users process and perceive explanations. In this paper, we report the results of a study that investigated differences between personal characteristics of the perception and the gaze pattern of a music recommender interface in the presence and absence of explanations. We investigated the differences between Need For Cognition, Musical Sophistication and the Big Five personality traits. Results show empirical evidence of the differences between Musical Sophistication and Openness on both perception and gaze pattern. We found that users with a high Musical Sophistication and a low Openness score benefit the most from explanations.
{"title":"What's in a User? Towards Personalising Transparency for Music Recommender Interfaces","authors":"Martijn Millecamp, N. Htun, C. Conati, K. Verbert","doi":"10.1145/3340631.3394844","DOIUrl":"https://doi.org/10.1145/3340631.3394844","url":null,"abstract":"We have become increasingly reliant on recommender systems to help us make decisions in our daily live. As such, it is becoming essential to explain to users how these systems reason to enable them to correct system assumptions and to trust the system. The advantages of explaining the recommendation process has been shown by a vast amount of research. Additionally, previous studies showed that personality affects users' attitudes, tastes and information processing. However, it is still unclear whether personality has an impact on the way users process and perceive explanations. In this paper, we report the results of a study that investigated differences between personal characteristics of the perception and the gaze pattern of a music recommender interface in the presence and absence of explanations. We investigated the differences between Need For Cognition, Musical Sophistication and the Big Five personality traits. Results show empirical evidence of the differences between Musical Sophistication and Openness on both perception and gaze pattern. We found that users with a high Musical Sophistication and a low Openness score benefit the most from explanations.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125912147","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}
Bernd Dudzik, H. Hung, Mark Antonius Neerincx, J. Broekens
This paper contributes to the automatic estimation of the subjective emotional experience that audio-visual media content induces in individual viewers, e.g. to support affect-based recommendations. Making accurate predictions of these responses is a challenging task because of their highly person-dependent and situation-specific nature. Findings from psychology indicate that an important driver for the emotional impact of media is the triggering of personal memories in observers. However, existing research on automated predictions focuses on the isolated analysis of audiovisual content, ignoring such contextual influences. In a series of empirical investigations, we (1) quantify the impact of associated personal memories on viewers' emotional responses to music videos in-the-wild and (2) assess the potential value of information about triggered memories for personalizing automatic predictions in this setting. Our findings indicate that the occurrence of memories intensifies emotional responses to videos. Moreover, information about viewers' memory response explains more variation in video-induced emotions than either the identity of videos or relevant viewer-characteristics (e.g. personality or mood). We discuss the implications of these results for existing approaches to automated predictions and describe ways for progress towards developing memory-sensitive alternatives.
{"title":"Investigating the Influence of Personal Memories on Video-Induced Emotions","authors":"Bernd Dudzik, H. Hung, Mark Antonius Neerincx, J. Broekens","doi":"10.1145/3340631.3394842","DOIUrl":"https://doi.org/10.1145/3340631.3394842","url":null,"abstract":"This paper contributes to the automatic estimation of the subjective emotional experience that audio-visual media content induces in individual viewers, e.g. to support affect-based recommendations. Making accurate predictions of these responses is a challenging task because of their highly person-dependent and situation-specific nature. Findings from psychology indicate that an important driver for the emotional impact of media is the triggering of personal memories in observers. However, existing research on automated predictions focuses on the isolated analysis of audiovisual content, ignoring such contextual influences. In a series of empirical investigations, we (1) quantify the impact of associated personal memories on viewers' emotional responses to music videos in-the-wild and (2) assess the potential value of information about triggered memories for personalizing automatic predictions in this setting. Our findings indicate that the occurrence of memories intensifies emotional responses to videos. Moreover, information about viewers' memory response explains more variation in video-induced emotions than either the identity of videos or relevant viewer-characteristics (e.g. personality or mood). We discuss the implications of these results for existing approaches to automated predictions and describe ways for progress towards developing memory-sensitive alternatives.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128734427","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}
Argyris Constantinides, A. Pietron, Marios Belk, C. Fidas, Ting Han, A. Pitsillides
Picture passwords, which require users to draw selections on images as their secret password, typically provide globalized solutions without taking into consideration that people across diverse cultures exhibit differences within interactive systems. Aiming to shed light on the effects of culture towards users' interactions within picture password schemes, we conducted a between-subjects cross-cultural (Eastern vs. Western) study (n=67). Users created a password on a picture illustrating content highly related to their daily-life experiences (culture-internal) vs. a picture illustrating the same daily-life experiences, but in a different cultural context (culture-external). Results revealed that people across cultures exhibited differences in visual processing, comprehension, and exploration of the picture content prior to making their password selections. The observed differences can be accounted by considering sociocultural theories highlighting the holistic preference of Eastern populations compared to the analytic preference of Western populations. Qualitative data also triangulate the findings by exposing the likeability and users' engagement towards the picture content familiar to individual's culture. Findings underpin the necessity to consider cultural differences in the design of personalized picture passwords.
{"title":"A Cross-cultural Perspective for Personalizing Picture Passwords","authors":"Argyris Constantinides, A. Pietron, Marios Belk, C. Fidas, Ting Han, A. Pitsillides","doi":"10.1145/3340631.3394859","DOIUrl":"https://doi.org/10.1145/3340631.3394859","url":null,"abstract":"Picture passwords, which require users to draw selections on images as their secret password, typically provide globalized solutions without taking into consideration that people across diverse cultures exhibit differences within interactive systems. Aiming to shed light on the effects of culture towards users' interactions within picture password schemes, we conducted a between-subjects cross-cultural (Eastern vs. Western) study (n=67). Users created a password on a picture illustrating content highly related to their daily-life experiences (culture-internal) vs. a picture illustrating the same daily-life experiences, but in a different cultural context (culture-external). Results revealed that people across cultures exhibited differences in visual processing, comprehension, and exploration of the picture content prior to making their password selections. The observed differences can be accounted by considering sociocultural theories highlighting the holistic preference of Eastern populations compared to the analytic preference of Western populations. Qualitative data also triangulate the findings by exposing the likeability and users' engagement towards the picture content familiar to individual's culture. Findings underpin the necessity to consider cultural differences in the design of personalized picture passwords.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121284546","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}
Drowsiness is a major cause of fatal traffic accidents. Automated driving is intended to counteract this problem, but in the lower levels of automation, the driver is still responsible as a fallback. Current drowsiness detection methods are often based on driving behavior parameters. Since the automation of the driving task reduces the scope of use of these parameters, alternatives are necessary. Particularly methods that include physiological signals seem to be auspicious. However, inside a vehicle, only non- or minimally intrusive measurement techniques are allowed. In this work, a machine learning-based driver drowsiness detection method is presented applied solely to physiological data from non-intrusive wrist-worn smart wearable devices. A user study (N=30) on a test track with SAE level-2 automated driving was conducted where heart rate data with three commercially available fitness trackers were recorded. Different machine learning models were tested in a 2- and 3-level classification of drowsiness. For both cases and with all tested devices, high accuracies (>90%) could be achieved. The proposed methodology provides new options for the development of intelligent driver-vehicle interaction concepts and interfaces, especially for driver drowsiness detection on the way to fully automating the driving task.
{"title":"The Potential of Wrist-Worn Wearables for Driver Drowsiness Detection: A Feasibility Analysis","authors":"Thomas Kundinger, A. Riener","doi":"10.1145/3340631.3394852","DOIUrl":"https://doi.org/10.1145/3340631.3394852","url":null,"abstract":"Drowsiness is a major cause of fatal traffic accidents. Automated driving is intended to counteract this problem, but in the lower levels of automation, the driver is still responsible as a fallback. Current drowsiness detection methods are often based on driving behavior parameters. Since the automation of the driving task reduces the scope of use of these parameters, alternatives are necessary. Particularly methods that include physiological signals seem to be auspicious. However, inside a vehicle, only non- or minimally intrusive measurement techniques are allowed. In this work, a machine learning-based driver drowsiness detection method is presented applied solely to physiological data from non-intrusive wrist-worn smart wearable devices. A user study (N=30) on a test track with SAE level-2 automated driving was conducted where heart rate data with three commercially available fitness trackers were recorded. Different machine learning models were tested in a 2- and 3-level classification of drowsiness. For both cases and with all tested devices, high accuracies (>90%) could be achieved. The proposed methodology provides new options for the development of intelligent driver-vehicle interaction concepts and interfaces, especially for driver drowsiness detection on the way to fully automating the driving task.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127875993","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}
Yitong Meng, Xiao Yan, Weiwen Liu, Huanhuan Wu, James Cheng
Item cold-start recommendation, which predicts user preference on new items that have no user interaction records, is an important problem in recommender systems. In this paper, we model the disparity between user preferences on warm items (those having interaction record) and that on cold-start items using the Wasserstein distance. On this basis, we propose Wasserstein Collaborative Filtering (WCF), which predicts user preference on cold-start items by minimizing the Wasserstein distance under user embedding constraint. Our analysis shows that minimizing the Wasserstein distance ensures that users sharing similar tastes on warm items also have similar preferences on cold-start items. Experimental results show that WCF consistently outperform the state-of-the-art methods in recommendation quality, usually by a large margin.
{"title":"Wasserstein Collaborative Filtering for Item Cold-start Recommendation","authors":"Yitong Meng, Xiao Yan, Weiwen Liu, Huanhuan Wu, James Cheng","doi":"10.1145/3340631.3394870","DOIUrl":"https://doi.org/10.1145/3340631.3394870","url":null,"abstract":"Item cold-start recommendation, which predicts user preference on new items that have no user interaction records, is an important problem in recommender systems. In this paper, we model the disparity between user preferences on warm items (those having interaction record) and that on cold-start items using the Wasserstein distance. On this basis, we propose Wasserstein Collaborative Filtering (WCF), which predicts user preference on cold-start items by minimizing the Wasserstein distance under user embedding constraint. Our analysis shows that minimizing the Wasserstein distance ensures that users sharing similar tastes on warm items also have similar preferences on cold-start items. Experimental results show that WCF consistently outperform the state-of-the-art methods in recommendation quality, usually by a large margin.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131025372","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}
Andrew Emerson, Michael Geden, A. Smith, E. Wiebe, Bradford W. Mott, K. Boyer, James C. Lester
Recent years have seen a growing interest in block-based programming environments for computer science education. Although block-based programming offers a gentle introduction to coding for novice programmers, introductory computer science still presents significant challenges, so there is a great need for block-based programming environments to provide students with adaptive support. Predictive student modeling holds significant potential for adaptive support in block-based programming environments because it can identify early on when a student is struggling. However, predictive student models often make a number of simplifying assumptions, such as assuming a normal response distribution or homogeneous student characteristics, which can limit the predictive performance of models. These assumptions, when invalid, can significantly reduce the predictive accuracy of student models. To address these issues, we introduce an approach to predictive student modeling that utilizes Bayesian hierarchical linear models. This approach explicitly accounts for individual student differences and programming activity differences by analyzing block-based programs created by students in a series of introductory programming activities. Evaluation results reveal that predictive student models that account for both the distributional and hierarchical factors outperform baseline models. These findings suggest that predictive student models based on Bayesian hierarchical modeling and representing individual differences in students can substantially improve models' accuracy for predicting student performance on post-tests. By improving the predictive performance of student models, this work holds substantial potential for improving adaptive support in block-based programming environments.
{"title":"Predictive Student Modeling in Block-Based Programming Environments with Bayesian Hierarchical Models","authors":"Andrew Emerson, Michael Geden, A. Smith, E. Wiebe, Bradford W. Mott, K. Boyer, James C. Lester","doi":"10.1145/3340631.3394853","DOIUrl":"https://doi.org/10.1145/3340631.3394853","url":null,"abstract":"Recent years have seen a growing interest in block-based programming environments for computer science education. Although block-based programming offers a gentle introduction to coding for novice programmers, introductory computer science still presents significant challenges, so there is a great need for block-based programming environments to provide students with adaptive support. Predictive student modeling holds significant potential for adaptive support in block-based programming environments because it can identify early on when a student is struggling. However, predictive student models often make a number of simplifying assumptions, such as assuming a normal response distribution or homogeneous student characteristics, which can limit the predictive performance of models. These assumptions, when invalid, can significantly reduce the predictive accuracy of student models. To address these issues, we introduce an approach to predictive student modeling that utilizes Bayesian hierarchical linear models. This approach explicitly accounts for individual student differences and programming activity differences by analyzing block-based programs created by students in a series of introductory programming activities. Evaluation results reveal that predictive student models that account for both the distributional and hierarchical factors outperform baseline models. These findings suggest that predictive student models based on Bayesian hierarchical modeling and representing individual differences in students can substantially improve models' accuracy for predicting student performance on post-tests. By improving the predictive performance of student models, this work holds substantial potential for improving adaptive support in block-based programming environments.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132004034","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}
News articles are increasingly consumed digitally and recommender systems (RS) are widely used to personalize news feeds for their users. Thereby, particular concerns about possible biases arise. When RS filter news articles opaquely, they might "trap" their users in filter bubbles. Additionally, user preferences change frequently in the domain of news, which is challenging for automated RS. We argue that both issues can be mitigated by depicting an interactive version of the user's preference profile inside an overview of the entire domain of news articles. To this end, we introduce NewsViz, a RS that visualizes the domain space of online news as treemap, which can interactively be manipulated to personalize a feed of suggested news articles. In a user study (N=63), we compared NewsViz to an interface based on sliders. While both prototypes yielded high results in terms of transparency, recommendation quality and user satisfaction, NewsViz outperformed its counterpart in the perceived degree of control. Structural equation modeling allows us to further uncover hitherto underestimated influences between quality aspects of RS. For instance, we found that the degree of overview of the item domain influenced the perceived quality of recommendations.
{"title":"NewsViz: Depicting and Controlling Preference Profiles Using Interactive Treemaps in News Recommender Systems","authors":"Johannes Kunkel, Claudia Schwenger, J. Ziegler","doi":"10.1145/3340631.3394869","DOIUrl":"https://doi.org/10.1145/3340631.3394869","url":null,"abstract":"News articles are increasingly consumed digitally and recommender systems (RS) are widely used to personalize news feeds for their users. Thereby, particular concerns about possible biases arise. When RS filter news articles opaquely, they might \"trap\" their users in filter bubbles. Additionally, user preferences change frequently in the domain of news, which is challenging for automated RS. We argue that both issues can be mitigated by depicting an interactive version of the user's preference profile inside an overview of the entire domain of news articles. To this end, we introduce NewsViz, a RS that visualizes the domain space of online news as treemap, which can interactively be manipulated to personalize a feed of suggested news articles. In a user study (N=63), we compared NewsViz to an interface based on sliders. While both prototypes yielded high results in terms of transparency, recommendation quality and user satisfaction, NewsViz outperformed its counterpart in the perceived degree of control. Structural equation modeling allows us to further uncover hitherto underestimated influences between quality aspects of RS. For instance, we found that the degree of overview of the item domain influenced the perceived quality of recommendations.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128416138","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}
Ethical considerations are getting increased attention with regards to providing responsible personalization for robots and autonomous systems. This is partly as a result of the currently limited deployment of such systems in human support and interaction settings. The tutorial will give an overview of the most commonly expressed ethical challenges and ways being undertaken to reduce their impact using the findings in an earlier undertaken review supplemented with recent work and initiatives. The tutorial will exemplify the challenges related to privacy, security and safety through several examples from own and others' work.Ethics, Robotics, Autonomous systems, Privacy, Security and Safety
{"title":"Ethical Considerations in User Modeling and Personalization: ACM UMAP 2020 Tutorial","authors":"J. Tørresen","doi":"10.1145/3340631.3398667","DOIUrl":"https://doi.org/10.1145/3340631.3398667","url":null,"abstract":"Ethical considerations are getting increased attention with regards to providing responsible personalization for robots and autonomous systems. This is partly as a result of the currently limited deployment of such systems in human support and interaction settings. The tutorial will give an overview of the most commonly expressed ethical challenges and ways being undertaken to reduce their impact using the findings in an earlier undertaken review supplemented with recent work and initiatives. The tutorial will exemplify the challenges related to privacy, security and safety through several examples from own and others' work.Ethics, Robotics, Autonomous systems, Privacy, Security and Safety","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123646722","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}
Sergio Torrijos, Alejandro Bellogín, Pablo Sánchez
Users from Location-Based Social Networks can be characterised by how and where they move. However, most of the works that exploit this type of information neglect either its sequential or its geographical properties. In this article, we focus on a specific family of recommender systems, those based on nearest neighbours; we define related users based on common check-ins and similar trajectories and analyse their effects on the recommendations. For this purpose, we use a real-world dataset and compare the performance on different dimensions against several state-of-the-art algorithms. The results show that better neighbours could be discovered with these approaches if we want to promote novel and diverse recommendations.
{"title":"Discovering Related Users in Location-based Social Networks","authors":"Sergio Torrijos, Alejandro Bellogín, Pablo Sánchez","doi":"10.1145/3340631.3394882","DOIUrl":"https://doi.org/10.1145/3340631.3394882","url":null,"abstract":"Users from Location-Based Social Networks can be characterised by how and where they move. However, most of the works that exploit this type of information neglect either its sequential or its geographical properties. In this article, we focus on a specific family of recommender systems, those based on nearest neighbours; we define related users based on common check-ins and similar trajectories and analyse their effects on the recommendations. For this purpose, we use a real-world dataset and compare the performance on different dimensions against several state-of-the-art algorithms. The results show that better neighbours could be discovered with these approaches if we want to promote novel and diverse recommendations.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121506243","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}
Guojing Zhou, Xi Yang, Hamoon Azizsoltani, T. Barnes, Min Chi
Motivated by the recent advances of reinforcement learning and the traditional grounded Self Determination Theory (SDT), we explored the impact of hierarchical reinforcement learning (HRL) induced pedagogical policies and data-driven explanations of the HRL-induced policies on student experience in an Intelligent Tutoring System (ITS). We explored their impacts first independently and then jointly. Overall our results showed that 1) the HRL induced policies could significantly improve students' learning performance, and 2) explaining the tutor's decisions to students through data-driven explanations could improve the student-system interaction in terms of students' engagement and autonomy.
{"title":"Improving Student-System Interaction Through Data-driven Explanations of Hierarchical Reinforcement Learning Induced Pedagogical Policies","authors":"Guojing Zhou, Xi Yang, Hamoon Azizsoltani, T. Barnes, Min Chi","doi":"10.1145/3340631.3394848","DOIUrl":"https://doi.org/10.1145/3340631.3394848","url":null,"abstract":"Motivated by the recent advances of reinforcement learning and the traditional grounded Self Determination Theory (SDT), we explored the impact of hierarchical reinforcement learning (HRL) induced pedagogical policies and data-driven explanations of the HRL-induced policies on student experience in an Intelligent Tutoring System (ITS). We explored their impacts first independently and then jointly. Overall our results showed that 1) the HRL induced policies could significantly improve students' learning performance, and 2) explaining the tutor's decisions to students through data-driven explanations could improve the student-system interaction in terms of students' engagement and autonomy.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"55 48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122750827","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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