Mara Houbraken, Chang Sun, E. Smirnov, K. Driessens
This paper presents a data driven approach to autonomous course-competency requirement and student-competency level discovery starting from the grades obtained by a sufficiently large set of students. The approach relies on collaborative filtering techniques, more precisely matrix decomposition, to derive the hidden competency requirements and levels that together should be responsible for observed grades. The discovered hidden features are translated into human understandable competencies by matching the computed values to expert input. The approach also allows for grade prediction for so far unobserved student course combinations, allowing for personalized study planning and student guidance. The technique is demonstrated on data from a "Data Science and Knowledge Engineering" Bachelor study, Maastricht University.
{"title":"Discovering Hidden Course Requirements and Student Competences from Grade Data","authors":"Mara Houbraken, Chang Sun, E. Smirnov, K. Driessens","doi":"10.1145/3099023.3099034","DOIUrl":"https://doi.org/10.1145/3099023.3099034","url":null,"abstract":"This paper presents a data driven approach to autonomous course-competency requirement and student-competency level discovery starting from the grades obtained by a sufficiently large set of students. The approach relies on collaborative filtering techniques, more precisely matrix decomposition, to derive the hidden competency requirements and levels that together should be responsible for observed grades. The discovered hidden features are translated into human understandable competencies by matching the computed values to expert input. The approach also allows for grade prediction for so far unobserved student course combinations, allowing for personalized study planning and student guidance. The technique is demonstrated on data from a \"Data Science and Knowledge Engineering\" Bachelor study, Maastricht University.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"59 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":"121821456","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 this paper, we propose a framework for computational serendipity. The framework is used in a recommender system context to find personalized serendipity and meanwhile stimulate user's curiosity. The framework is novel to the serendipity research community in that it decomposes the concept of serendipity into two elements: surprise and value; and provides computational approaches to modeling both of them. The framework also incorporates the concept of curiosity to keep users' interests over a long term. It brings together several fields including information retrieval, cognitive science, computational creativity in artificial intelligence, and text mining. We will describe the framework first and then evaluate it with an implementation called StumbleOn in the health news context. The evaluation serves as a proof-of-concept of this computational serendipity framework.
{"title":"A Framework for Computational Serendipity","authors":"Xi Niu, Fakhri Abbas","doi":"10.1145/3099023.3099097","DOIUrl":"https://doi.org/10.1145/3099023.3099097","url":null,"abstract":"In this paper, we propose a framework for computational serendipity. The framework is used in a recommender system context to find personalized serendipity and meanwhile stimulate user's curiosity. The framework is novel to the serendipity research community in that it decomposes the concept of serendipity into two elements: surprise and value; and provides computational approaches to modeling both of them. The framework also incorporates the concept of curiosity to keep users' interests over a long term. It brings together several fields including information retrieval, cognitive science, computational creativity in artificial intelligence, and text mining. We will describe the framework first and then evaluate it with an implementation called StumbleOn in the health news context. The evaluation serves as a proof-of-concept of this computational serendipity framework.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"23 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":"115740578","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 recommendation poses several specific challenges compared to other domains, such as freshness and serendipity. The proposed research will develop new methods and techniques to address some of such challenges. With the aim of handling the users' changing interests and the fast evolution overtime of news, my solution will be proposed in the social network domain, exploiting an adaptive focused crawling algorithm. Moreover, it will consider a given user's attitude towards her interests, with the purpose of recommending articles in line with her beliefs. An experimental evaluation is currently being implemented to assess the effectiveness of my approach, also in comparison with state-of-the-art techniques.
{"title":"An Approach to Social News Recommendation based on Focused Crawling and Sentiment Analysis","authors":"Matteo Amadei","doi":"10.1145/3099023.3099027","DOIUrl":"https://doi.org/10.1145/3099023.3099027","url":null,"abstract":"News recommendation poses several specific challenges compared to other domains, such as freshness and serendipity. The proposed research will develop new methods and techniques to address some of such challenges. With the aim of handling the users' changing interests and the fast evolution overtime of news, my solution will be proposed in the social network domain, exploiting an adaptive focused crawling algorithm. Moreover, it will consider a given user's attitude towards her interests, with the purpose of recommending articles in line with her beliefs. An experimental evaluation is currently being implemented to assess the effectiveness of my approach, also in comparison with state-of-the-art techniques.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"227 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":"122859082","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}
My doctoral research will investigate adapting group formation in computer supported collaborative learning (CSCL) based on learners' characteristics. As Group based learning leverages on interaction for effective cognition, this project aims to investigate the effect of individual behavioural characteristics on interaction within a group. Based on our findings, we will develop and evaluate a model for adapting group formation for effective interaction in CSCL.
{"title":"Adaptive Support For Group Formation In Computer Supported Collaborative Learning","authors":"A. Adeniran","doi":"10.1145/3099023.3099026","DOIUrl":"https://doi.org/10.1145/3099023.3099026","url":null,"abstract":"My doctoral research will investigate adapting group formation in computer supported collaborative learning (CSCL) based on learners' characteristics. As Group based learning leverages on interaction for effective cognition, this project aims to investigate the effect of individual behavioural characteristics on interaction within a group. Based on our findings, we will develop and evaluate a model for adapting group formation for effective interaction in CSCL.","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":"114173606","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 investigates the influence of learner personality and learning styles on the selection of different styles of learning materials. We considered the big five personality traits (focusing in particular on Extroversion and Openness to Experience) and Felder and Soloman's Index of Learning Styles instrument (ILS). We found no real impact of learning styles, except for a small effect for the visual/verbal style. We also did not find an impact of personality on the selection of different styles of learning materials.
{"title":"Should Learning Material's Selection be Adapted to Learning Style and Personality?","authors":"Manal Alhathli, J. Masthoff, Advaith Siddharthan","doi":"10.1145/3099023.3099079","DOIUrl":"https://doi.org/10.1145/3099023.3099079","url":null,"abstract":"This paper investigates the influence of learner personality and learning styles on the selection of different styles of learning materials. We considered the big five personality traits (focusing in particular on Extroversion and Openness to Experience) and Felder and Soloman's Index of Learning Styles instrument (ILS). We found no real impact of learning styles, except for a small effect for the visual/verbal style. We also did not find an impact of personality on the selection of different styles of learning materials.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"40 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":"116615225","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 generate recommendations by analysing which items the user consumes or likes. Moreover, in many scenarios, e.g., when a user is visiting an exhibition or a city, users are faced with a sequence of decisions, and the recommender should therefore suggest, at each decision step, a set of viable recommendations (attractions). In these scenarios the order and the context of the past user choices is a valuable source of data, and the recommender has to effectively exploit this information for understanding the user preferences in order to recommend compelling items. For addressing these scenarios, this paper proposes a novel preference learning model that takes into account the sequential nature of item consumption. The model is based on Inverse Reinforcement Learning, which enables to exploit observations of users' behaviours, when they are making decisions and taking actions, i.e., choosing the items to consume. The results of a proof of concept experiment show that the proposed model can effectively capture the user preferences, the rationale of users decision making process when consuming items in a sequential manner, and can replicate the observed user behaviours.
{"title":"Learning User Preferences by Observing User-Items Interactions in an IoT Augmented Space","authors":"David Massimo, Mehdi Elahi, F. Ricci","doi":"10.1145/3099023.3099070","DOIUrl":"https://doi.org/10.1145/3099023.3099070","url":null,"abstract":"Recommender systems generate recommendations by analysing which items the user consumes or likes. Moreover, in many scenarios, e.g., when a user is visiting an exhibition or a city, users are faced with a sequence of decisions, and the recommender should therefore suggest, at each decision step, a set of viable recommendations (attractions). In these scenarios the order and the context of the past user choices is a valuable source of data, and the recommender has to effectively exploit this information for understanding the user preferences in order to recommend compelling items. For addressing these scenarios, this paper proposes a novel preference learning model that takes into account the sequential nature of item consumption. The model is based on Inverse Reinforcement Learning, which enables to exploit observations of users' behaviours, when they are making decisions and taking actions, i.e., choosing the items to consume. The results of a proof of concept experiment show that the proposed model can effectively capture the user preferences, the rationale of users decision making process when consuming items in a sequential manner, and can replicate the observed user behaviours.","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":"128679437","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}
Formative feedback is essential for learning computer programming but is also a challenge to automate because of the many solutions a programming exercise can have. Whilst programming tutoring systems can easily generate automated feedback on how correct a program is, they less often provide some personalised guidance on how to improve or fix the code. In this paper, we present an approach for generating hints using previous student data. Utilising a range of techniques such as filtering, clustering and pattern mining, four different types of data-driven hints are generated: input suggestion, code-based, concept and pre-emptive hints. We evaluated our approach with data from 5529 students using the Grok Learning platform for teaching programming in Python. The results show that we can generate various types of hints for over 90% of students with data from only 10 students, and hence, reduce the cold-start problem.
{"title":"Automated Data-Driven Hints for Computer Programming Students","authors":"S. Chow, K. Yacef, I. Koprinska, J. Curran","doi":"10.1145/3099023.3099065","DOIUrl":"https://doi.org/10.1145/3099023.3099065","url":null,"abstract":"Formative feedback is essential for learning computer programming but is also a challenge to automate because of the many solutions a programming exercise can have. Whilst programming tutoring systems can easily generate automated feedback on how correct a program is, they less often provide some personalised guidance on how to improve or fix the code. In this paper, we present an approach for generating hints using previous student data. Utilising a range of techniques such as filtering, clustering and pattern mining, four different types of data-driven hints are generated: input suggestion, code-based, concept and pre-emptive hints. We evaluated our approach with data from 5529 students using the Grok Learning platform for teaching programming in Python. The results show that we can generate various types of hints for over 90% of students with data from only 10 students, and hence, reduce the cold-start problem.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"50 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113981947","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 presents a novel, graph embedding based recommendation technique. The method operates on the knowledge graph, an information representation technique alloying content-based and collaborative information. To generate recommendations, a two dimensional embedding is developed for the knowledge graph. As the embedding maps the users and the items to the same vector space, the recommendations are then calculated on a spatial basis. Regarding to the number of cold start cases, precision, recall, normalized Cumulative Discounted Gain and computational resource need, the evaluation shows that the introduced technique delivers a higher performance compared to collaborative filtering on top-n recommendation lists. Our further finding is that graph embedding based methods show a more stable performance in the case of an increasing amount of user preference information compared to the benchmark method.
{"title":"Graph Embedding Based Recommendation Techniques on the Knowledge Graph","authors":"László Grad-Gyenge, A. Kiss, P. Filzmoser","doi":"10.1145/3099023.3099096","DOIUrl":"https://doi.org/10.1145/3099023.3099096","url":null,"abstract":"This paper presents a novel, graph embedding based recommendation technique. The method operates on the knowledge graph, an information representation technique alloying content-based and collaborative information. To generate recommendations, a two dimensional embedding is developed for the knowledge graph. As the embedding maps the users and the items to the same vector space, the recommendations are then calculated on a spatial basis. Regarding to the number of cold start cases, precision, recall, normalized Cumulative Discounted Gain and computational resource need, the evaluation shows that the introduced technique delivers a higher performance compared to collaborative filtering on top-n recommendation lists. Our further finding is that graph embedding based methods show a more stable performance in the case of an increasing amount of user preference information compared to the benchmark method.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"33 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":"115722848","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":"A Corpus for Modeling Personalities of Web Forum Users","authors":"William R. Wright","doi":"10.1145/3099023.3099045","DOIUrl":"https://doi.org/10.1145/3099023.3099045","url":null,"abstract":"","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"4 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":"114325505","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}
François Bouchet, Hugues Labarthe, K. Yacef, R. Bachelet
Lack of social relationship has been shown to be an important contribution factor for attrition in Massive Open Online Courses (MOOCs). Helping students to connect with other students is therefore a promising solution to alleviate this phenomenon. Following up on our previous research showing that embedding a peer recommender in a MOOC had a positive impact on students' engagement in the MOOC, we compare in this paper the impact of three different peer recommenders: one based on socio-demographic criteria, one based on current progress made in the MOOC, and the last one providing random recommendations. We report our results and analysis (N = 2025 students), suggesting that the socio-demographic-based recommender had a slightly better impact than the random one.
{"title":"Comparing Peer Recommendation Strategies in a MOOC","authors":"François Bouchet, Hugues Labarthe, K. Yacef, R. Bachelet","doi":"10.1145/3099023.3099036","DOIUrl":"https://doi.org/10.1145/3099023.3099036","url":null,"abstract":"Lack of social relationship has been shown to be an important contribution factor for attrition in Massive Open Online Courses (MOOCs). Helping students to connect with other students is therefore a promising solution to alleviate this phenomenon. Following up on our previous research showing that embedding a peer recommender in a MOOC had a positive impact on students' engagement in the MOOC, we compare in this paper the impact of three different peer recommenders: one based on socio-demographic criteria, one based on current progress made in the MOOC, and the last one providing random recommendations. We report our results and analysis (N = 2025 students), suggesting that the socio-demographic-based recommender had a slightly better impact than the random one.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"70 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":"116973871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：