Pub Date : 2018-05-28DOI: 10.1080/13614568.2018.1476596
Radek Pelánek, Jirí Rihák
ABSTRACT A common personalisation approach in educational systems is mastery learning. A key step in this approach is a criterion that determines whether a learner has already achieved mastery. We thoroughly analyse several mastery criteria for the basic case of a single well-specified knowledge component. For the analysis we use experiments with both simulated and real data. The results show that the choice of data sources used for mastery decision and the setting of thresholds are more important than the choice of a learner modelling technique. We argue that a simple exponential moving average method is a suitable technique for mastery criterion and discuss techniques for the choice of a mastery threshold. We also propose an extension of the exponential moving average method that takes into account practical aspects like time intensity of items and we report on a practical application of this mastery criterion in a widely used educational system.
{"title":"Analysis and design of mastery learning criteria","authors":"Radek Pelánek, Jirí Rihák","doi":"10.1080/13614568.2018.1476596","DOIUrl":"https://doi.org/10.1080/13614568.2018.1476596","url":null,"abstract":"ABSTRACT A common personalisation approach in educational systems is mastery learning. A key step in this approach is a criterion that determines whether a learner has already achieved mastery. We thoroughly analyse several mastery criteria for the basic case of a single well-specified knowledge component. For the analysis we use experiments with both simulated and real data. The results show that the choice of data sources used for mastery decision and the setting of thresholds are more important than the choice of a learner modelling technique. We argue that a simple exponential moving average method is a suitable technique for mastery criterion and discuss techniques for the choice of a mastery threshold. We also propose an extension of the exponential moving average method that takes into account practical aspects like time intensity of items and we report on a practical application of this mastery criterion in a widely used educational system.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"133 - 159"},"PeriodicalIF":1.2,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1476596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45132903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-03DOI: 10.1080/13614568.2018.1489001
Mainack Mondal, Leandro Araújo Silva, D. Correa, Fabrício Benevenuto
ABSTRACT Social media platforms provide an inexpensive communication medium that allows anyone to publish content and anyone interested in the content can obtain it. However, this same potential of social media provide space for discourses that are harmful to certain groups of people. Examples of these discourses include bullying, offensive content, and hate speech. Out of these discourses hate speech is rapidly recognized as a serious problem by authorities of many countries. In this paper, we provide the first of a kind systematic large-scale measurement and analysis study of explicit expressions of hate speech in online social media. We aim to understand the abundance of hate speech in online social media, the most common hate expressions, the effect of anonymity on hate speech, the sensitivity of hate speech and the most hated groups across regions. In order to achieve our objectives, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both of these systems. Our results identify hate speech forms and unveil a set of important patterns, providing not only a broader understanding of online hate speech, but also offering directions for detection and prevention approaches.
{"title":"Characterizing usage of explicit hate expressions in social media","authors":"Mainack Mondal, Leandro Araújo Silva, D. Correa, Fabrício Benevenuto","doi":"10.1080/13614568.2018.1489001","DOIUrl":"https://doi.org/10.1080/13614568.2018.1489001","url":null,"abstract":"ABSTRACT Social media platforms provide an inexpensive communication medium that allows anyone to publish content and anyone interested in the content can obtain it. However, this same potential of social media provide space for discourses that are harmful to certain groups of people. Examples of these discourses include bullying, offensive content, and hate speech. Out of these discourses hate speech is rapidly recognized as a serious problem by authorities of many countries. In this paper, we provide the first of a kind systematic large-scale measurement and analysis study of explicit expressions of hate speech in online social media. We aim to understand the abundance of hate speech in online social media, the most common hate expressions, the effect of anonymity on hate speech, the sensitivity of hate speech and the most hated groups across regions. In order to achieve our objectives, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both of these systems. Our results identify hate speech forms and unveil a set of important patterns, providing not only a broader understanding of online hate speech, but also offering directions for detection and prevention approaches.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"110 - 130"},"PeriodicalIF":1.2,"publicationDate":"2018-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1489001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47426260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-03DOI: 10.1080/13614568.2018.1504520
F. Bonchi, Peter Dolog, D. Helic, P. Vojtás
This Special Issue presents three invited papers, selected from among the best contributions that were presented at the 2017 ACM International Conference on Hypertext and Social Media (HT 2017) held in Prague, Czech Republic on 4–7th July 2017. Since 1987, HT has successfully brought together leading researchers and developers from the Hypertext community. It is concerned with all aspects of modern hypertext research, including social media, adaptation, personalisation, recommendations, user modelling, linked data and semantic web, dynamic and computed hypertext, and its application in digital humanities, as well as with interplay between those aspects such as linking stories with data or linking people with resources. The call for papers of HT 2017 was organised into four technical tracks: Social Networks and Digital Humanities (Linking people), Semantic Web and Linked Data (Linking data), Adaptive Hypertext and Recommendations (Linking resources), News and Storytelling (Linking stories). The Program Committee of HT 2017 accepted 19 papers (acceptance rate 27%) for regular presentation, and an additional 12 short-presentation papers. In addition, the conference featured four demonstrations and two keynotes: Kristina Lerman and Peter Mika. The three papers selected for this Special Issue cover a diverse set of topics, well representing the spectrum of topics that were discussed at HT 2017. The first paper, entitled “Implicit Negative Link Detection on Online Political Networks via Matrix Tri-Factorizations” (Ozer, Yildirim and Davulcu), deals with the prediction of negative connections between users of online political networks. Currently, the majority of social media sites do not support explicit negative links between participating users. However, the very nature of the political discourse often involves users in discussing controversial political issues, which results in a series of agreements and disagreements. The authors present a technically sound approach to extracting negative links from a variety of online political platforms by using a matrix factorisation approach. Matrix factorisation is extended in multiple ways to reflect the information that can be found in the sentiment of the written comments as well as the social balance theory known from the social sciences. The paper concludes with a range of experiments on the real datasets using the Twitter accounts of the politicians of the major UK political parties. The experiments show an improved accuracy of the community detection methods applied on the networks with the extracted negative interaction links as compared to the application of these methods on the networks having only positive links. The second paper, entitled “Hybrid Recommendations by Content-Aligned Bayesian Personalized Ranking” (Peska) focuses on recommender systems that seek to predict the "rating" or "preference" a user would give to an item and hence enabling to display items in order the user might find interest
{"title":"Invited papers from the ACM conference on hypertext and social media","authors":"F. Bonchi, Peter Dolog, D. Helic, P. Vojtás","doi":"10.1080/13614568.2018.1504520","DOIUrl":"https://doi.org/10.1080/13614568.2018.1504520","url":null,"abstract":"This Special Issue presents three invited papers, selected from among the best contributions that were presented at the 2017 ACM International Conference on Hypertext and Social Media (HT 2017) held in Prague, Czech Republic on 4–7th July 2017. Since 1987, HT has successfully brought together leading researchers and developers from the Hypertext community. It is concerned with all aspects of modern hypertext research, including social media, adaptation, personalisation, recommendations, user modelling, linked data and semantic web, dynamic and computed hypertext, and its application in digital humanities, as well as with interplay between those aspects such as linking stories with data or linking people with resources. The call for papers of HT 2017 was organised into four technical tracks: Social Networks and Digital Humanities (Linking people), Semantic Web and Linked Data (Linking data), Adaptive Hypertext and Recommendations (Linking resources), News and Storytelling (Linking stories). The Program Committee of HT 2017 accepted 19 papers (acceptance rate 27%) for regular presentation, and an additional 12 short-presentation papers. In addition, the conference featured four demonstrations and two keynotes: Kristina Lerman and Peter Mika. The three papers selected for this Special Issue cover a diverse set of topics, well representing the spectrum of topics that were discussed at HT 2017. The first paper, entitled “Implicit Negative Link Detection on Online Political Networks via Matrix Tri-Factorizations” (Ozer, Yildirim and Davulcu), deals with the prediction of negative connections between users of online political networks. Currently, the majority of social media sites do not support explicit negative links between participating users. However, the very nature of the political discourse often involves users in discussing controversial political issues, which results in a series of agreements and disagreements. The authors present a technically sound approach to extracting negative links from a variety of online political platforms by using a matrix factorisation approach. Matrix factorisation is extended in multiple ways to reflect the information that can be found in the sentiment of the written comments as well as the social balance theory known from the social sciences. The paper concludes with a range of experiments on the real datasets using the Twitter accounts of the politicians of the major UK political parties. The experiments show an improved accuracy of the community detection methods applied on the networks with the extracted negative interaction links as compared to the application of these methods on the networks having only positive links. The second paper, entitled “Hybrid Recommendations by Content-Aligned Bayesian Personalized Ranking” (Peska) focuses on recommender systems that seek to predict the \"rating\" or \"preference\" a user would give to an item and hence enabling to display items in order the user might find interest","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"61 - 62"},"PeriodicalIF":1.2,"publicationDate":"2018-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1504520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48194211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-03DOI: 10.1080/13614568.2018.1482964
M. Ozer, M. Yildirim, H. Davulcu
ABSTRACT Political conversations have become a ubiquitous part of social media. When users interact and engage in discussions, there are usually two mediums available to them; textual conversations and platform-specific interactions such as like, share (Facebook) or retweet (Twitter). Major social media platforms do not facilitate users with negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Thus, detecting implicit negative links is an important and a challenging task. In this work, we propose an unsupervised framework utilising positive interactions, sentiment cues, and socially balanced triads for detecting implicit negative links. We also present an online variant of it for streaming data tasks. We show the effectiveness of both frameworks with experiments on two annotated datasets of politician Twitter accounts. Our experiments show that the proposed frameworks outperform other well-known and proposed baselines. To illustrate the detected implicit negative links' contribution, we compare the community detection accuracies using unsigned and signed networks. Experimental results using detected negative links show superiority on the three datasets where the camps are known a priori. We also present qualitative evaluations of polarisation patterns between communities which are only possible in the presence of negative links.
{"title":"Implicit negative link detection on online political networks via matrix tri-factorizations","authors":"M. Ozer, M. Yildirim, H. Davulcu","doi":"10.1080/13614568.2018.1482964","DOIUrl":"https://doi.org/10.1080/13614568.2018.1482964","url":null,"abstract":"ABSTRACT Political conversations have become a ubiquitous part of social media. When users interact and engage in discussions, there are usually two mediums available to them; textual conversations and platform-specific interactions such as like, share (Facebook) or retweet (Twitter). Major social media platforms do not facilitate users with negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Thus, detecting implicit negative links is an important and a challenging task. In this work, we propose an unsupervised framework utilising positive interactions, sentiment cues, and socially balanced triads for detecting implicit negative links. We also present an online variant of it for streaming data tasks. We show the effectiveness of both frameworks with experiments on two annotated datasets of politician Twitter accounts. Our experiments show that the proposed frameworks outperform other well-known and proposed baselines. To illustrate the detected implicit negative links' contribution, we compare the community detection accuracies using unsigned and signed networks. Experimental results using detected negative links show superiority on the three datasets where the camps are known a priori. We also present qualitative evaluations of polarisation patterns between communities which are only possible in the presence of negative links.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"63 - 87"},"PeriodicalIF":1.2,"publicationDate":"2018-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1482964","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43920227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-03DOI: 10.1080/13614568.2018.1489002
Ladislav Peška
ABSTRACT In many application domains of recommender systems, content-based (CB) information are available for users, objects or both. CB information plays an important role in the process of recommendation, especially in cold-start scenarios, where the volume of feedback data is low. However, CB information may come from several, possibly external, sources varying in reliability, coverage or relevance to the recommending task. Therefore, each content source or attribute possess a different level of informativeness, which should be taken into consideration during the process of recommendation. In this paper, we propose a Content-Aligned Bayesian Personalized Ranking Matrix Factorization method (CABPR), extending Bayesian Personalized Ranking Matrix Factorization (BPR) by incorporating multiple sources of content information into the BPR’s optimization procedure. The working principle of CABPR is to calculate user-to-user and object-to-object similarity matrices based on the content information and penalize differences in latent factors of closely related users’ or objects’. CABPR further estimates relevance of similarity matrices as a part of the optimization procedure. CABPR method is a significant extension of a previously published BPR_MCA method, featuring additional variants of optimization criterion and improved optimization procedure. Four variants of CABPR were evaluated on two publicly available datasets: MovieLens 1M dataset, extended by data from IMDB, DBTropes and ZIP code statistics and LOD-RecSys dataset extended by the information available from DBPedia. Experiments shown that CABPR significantly improves over standard BPR as well as BPR_MCA method w.r.t. several cold-start scenarios.
{"title":"Hybrid recommendations by content-aligned Bayesian personalized ranking","authors":"Ladislav Peška","doi":"10.1080/13614568.2018.1489002","DOIUrl":"https://doi.org/10.1080/13614568.2018.1489002","url":null,"abstract":"ABSTRACT In many application domains of recommender systems, content-based (CB) information are available for users, objects or both. CB information plays an important role in the process of recommendation, especially in cold-start scenarios, where the volume of feedback data is low. However, CB information may come from several, possibly external, sources varying in reliability, coverage or relevance to the recommending task. Therefore, each content source or attribute possess a different level of informativeness, which should be taken into consideration during the process of recommendation. In this paper, we propose a Content-Aligned Bayesian Personalized Ranking Matrix Factorization method (CABPR), extending Bayesian Personalized Ranking Matrix Factorization (BPR) by incorporating multiple sources of content information into the BPR’s optimization procedure. The working principle of CABPR is to calculate user-to-user and object-to-object similarity matrices based on the content information and penalize differences in latent factors of closely related users’ or objects’. CABPR further estimates relevance of similarity matrices as a part of the optimization procedure. CABPR method is a significant extension of a previously published BPR_MCA method, featuring additional variants of optimization criterion and improved optimization procedure. Four variants of CABPR were evaluated on two publicly available datasets: MovieLens 1M dataset, extended by data from IMDB, DBTropes and ZIP code statistics and LOD-RecSys dataset extended by the information available from DBPedia. Experiments shown that CABPR significantly improves over standard BPR as well as BPR_MCA method w.r.t. several cold-start scenarios.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"109 - 88"},"PeriodicalIF":1.2,"publicationDate":"2018-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1489002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47916531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-02DOI: 10.1080/13614568.2018.1448007
M. S. Missen, Mickaël Coustaty, N. Salamat, V. B. Surya Prasath
ABSTRACT The amount of opinionated data on the web has exponentially increased especially after the emergence of online social networks. To deal with these huge deluge of data, we need to have robust mechanisms that can help identify all aspects of opinion segment and support the automatic processing of opinion data. Recently, there have been a few developments made in this direction, and different sentiment annotation schemes have been proposed such as the SentiML, OpinionMiningML, and EmotionML. In this work, we propose SentiML++, an extension of SentiML with a focus on annotating opinions, and further answering aspects of the general question “who has what opinion about whom in which context?”. A detailed comparison with SentiML and other existing annotation schemes is also presented. The data collection annotated with SentiML has been annotated with SentiML++ and is available for download for further research purposes. Experiments with data collections annotated with SentiML and SentiML++ proves that SentiML++ is a significant and valuable addition to SentiML.
{"title":"SentiML ++: an extension of the SentiML sentiment annotation scheme","authors":"M. S. Missen, Mickaël Coustaty, N. Salamat, V. B. Surya Prasath","doi":"10.1080/13614568.2018.1448007","DOIUrl":"https://doi.org/10.1080/13614568.2018.1448007","url":null,"abstract":"ABSTRACT The amount of opinionated data on the web has exponentially increased especially after the emergence of online social networks. To deal with these huge deluge of data, we need to have robust mechanisms that can help identify all aspects of opinion segment and support the automatic processing of opinion data. Recently, there have been a few developments made in this direction, and different sentiment annotation schemes have been proposed such as the SentiML, OpinionMiningML, and EmotionML. In this work, we propose SentiML++, an extension of SentiML with a focus on annotating opinions, and further answering aspects of the general question “who has what opinion about whom in which context?”. A detailed comparison with SentiML and other existing annotation schemes is also presented. The data collection annotated with SentiML has been annotated with SentiML++ and is available for download for further research purposes. Experiments with data collections annotated with SentiML and SentiML++ proves that SentiML++ is a significant and valuable addition to SentiML.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"28 - 43"},"PeriodicalIF":1.2,"publicationDate":"2018-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1448007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41873252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-02DOI: 10.1080/13614568.2018.1460403
Yuan Meng, Hongwei Wang, Lijuan Zheng
ABSTRACT Facing with thousands of online product reviews, consumers usually pay close attention to those valuable ones which provide more specific and credible evaluations on products. Whether a close association exists between product review quality and sales is thus examined in this paper. By employing text mining techniques on multiple review features, a review is measured as one of the following two levels: high-quality or low-quality. In doing so, aggregate quality level of product’s whole reviews is also identified. Then, a two-level econometrical analysis is conducted on the real datasets from Amazon.cn. The results reveal that aggregate quality level of positive reviews and negative reviews interactively influence sales. In the situation the aggregate quality level of positive reviews is high meanwhile that of negative reviews’ is low, product sale is the highest, while in the opposite situation product sale is the lowest. The results also reveal that consumers understand product’s value from weighting positive and negative reviews of high-quality level, which then positively relates to product sales and exerts a dynamic effect on sales by the moderating role of product selling stage and popularity. The paper innovatively integrates the quantitative and qualitative characteristics of reviews to estimate their economic effect.
{"title":"Impact of online word-of-mouth on sales: the moderating role of product review quality","authors":"Yuan Meng, Hongwei Wang, Lijuan Zheng","doi":"10.1080/13614568.2018.1460403","DOIUrl":"https://doi.org/10.1080/13614568.2018.1460403","url":null,"abstract":"ABSTRACT Facing with thousands of online product reviews, consumers usually pay close attention to those valuable ones which provide more specific and credible evaluations on products. Whether a close association exists between product review quality and sales is thus examined in this paper. By employing text mining techniques on multiple review features, a review is measured as one of the following two levels: high-quality or low-quality. In doing so, aggregate quality level of product’s whole reviews is also identified. Then, a two-level econometrical analysis is conducted on the real datasets from Amazon.cn. The results reveal that aggregate quality level of positive reviews and negative reviews interactively influence sales. In the situation the aggregate quality level of positive reviews is high meanwhile that of negative reviews’ is low, product sale is the highest, while in the opposite situation product sale is the lowest. The results also reveal that consumers understand product’s value from weighting positive and negative reviews of high-quality level, which then positively relates to product sales and exerts a dynamic effect on sales by the moderating role of product selling stage and popularity. The paper innovatively integrates the quantitative and qualitative characteristics of reviews to estimate their economic effect.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"1 - 27"},"PeriodicalIF":1.2,"publicationDate":"2018-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1460403","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43911767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-02DOI: 10.1080/13614568.2018.1488890
Ja-Ryoung Choi, Soon-Bum Lim
ABSTRACT A crowdsourcing environment, where there is a very large volume of diverse content resulting from the participation of a mass of unspecified individuals, has resulted in significant changes in education. This paper presents an e-learning content system to manage the inclusion of crowdsourced material on the Web within lecture materials. The e-learning content system comprises a scrape system, learning content editor, and tracing system. As Web content may change with the progress of time, teachers (and students) must check whether the Web-based materials previously used in their classes have been updated. Accordingly, we designed scrape metadata specifications for tracing the original source. These metadata include information on copyrights and tracing, rather than basic data regarding the original source, to allow users to determine whether the original source has been updated. An editor was also configured so that the scraped Web content could be immediately incorporated into the teaching materials for enhanced convenience. The change point tracing accuracy test and utility evaluation performed using this system show that the accuracy of the change point tracing was 97.1% and that this system effectively saves time as compared with checking for changes by entering each URL directly.
{"title":"Original source tracing enabled by e-learning contents system based on crowdsourcing","authors":"Ja-Ryoung Choi, Soon-Bum Lim","doi":"10.1080/13614568.2018.1488890","DOIUrl":"https://doi.org/10.1080/13614568.2018.1488890","url":null,"abstract":"ABSTRACT A crowdsourcing environment, where there is a very large volume of diverse content resulting from the participation of a mass of unspecified individuals, has resulted in significant changes in education. This paper presents an e-learning content system to manage the inclusion of crowdsourced material on the Web within lecture materials. The e-learning content system comprises a scrape system, learning content editor, and tracing system. As Web content may change with the progress of time, teachers (and students) must check whether the Web-based materials previously used in their classes have been updated. Accordingly, we designed scrape metadata specifications for tracing the original source. These metadata include information on copyrights and tracing, rather than basic data regarding the original source, to allow users to determine whether the original source has been updated. An editor was also configured so that the scraped Web content could be immediately incorporated into the teaching materials for enhanced convenience. The change point tracing accuracy test and utility evaluation performed using this system show that the accuracy of the change point tracing was 97.1% and that this system effectively saves time as compared with checking for changes by entering each URL directly.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"44 - 59"},"PeriodicalIF":1.2,"publicationDate":"2018-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1488890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43954296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-02DOI: 10.1080/13614568.2017.1416681
Le Van Thinh, Truong-Dinh Tu
ABSTRACT There is an important online role for Web service providers and users; however, the rapidly growing number of service providers and users, it can create some similar functions among web services. This is an exciting area for research, and researchers seek to to propose solutions for the best service to users. Collaborative filtering (CF) algorithms are widely used in recommendation systems, although these are less effective for cold-start users. Recently, some recommender systems have been developed based on social network models, and the results show that social network models have better performance in terms of CF, especially for cold-start users. However, most social network-based recommendations do not consider the user’s mood. This is a hidden source of information, and is very useful in improving prediction efficiency. In this paper, we introduce a new model called User-Trust Propagation (UTP). The model uses a combination of trust and the mood of users to predict the QoS value and matrix factorisation (MF), which is used to train the model. The experimental results show that the proposed model gives better accuracy than other models, especially for the cold-start problem.
{"title":"QoS prediction for web services based on user-trust propagation model","authors":"Le Van Thinh, Truong-Dinh Tu","doi":"10.1080/13614568.2017.1416681","DOIUrl":"https://doi.org/10.1080/13614568.2017.1416681","url":null,"abstract":"ABSTRACT There is an important online role for Web service providers and users; however, the rapidly growing number of service providers and users, it can create some similar functions among web services. This is an exciting area for research, and researchers seek to to propose solutions for the best service to users. Collaborative filtering (CF) algorithms are widely used in recommendation systems, although these are less effective for cold-start users. Recently, some recommender systems have been developed based on social network models, and the results show that social network models have better performance in terms of CF, especially for cold-start users. However, most social network-based recommendations do not consider the user’s mood. This is a hidden source of information, and is very useful in improving prediction efficiency. In this paper, we introduce a new model called User-Trust Propagation (UTP). The model uses a combination of trust and the mood of users to predict the QoS value and matrix factorisation (MF), which is used to train the model. The experimental results show that the proposed model gives better accuracy than other models, especially for the cold-start problem.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"23 1","pages":"277 - 291"},"PeriodicalIF":1.2,"publicationDate":"2017-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2017.1416681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42490781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-02DOI: 10.1080/13614568.2017.1421717
Piotr Konrad Leszczyński, A. Charuta, B. Kolodziejczak, M. Roszak
ABSTRACT There is scientific evidence confirming the effectiveness of e-learning within resuscitation, however, there is not enough research on modern examination techniques within the scope. The aim of the pilot research is to compare the exam results in the field of Advanced Life Support in a traditional (paper) and interactive (computer) form as well as to evaluate satisfaction of the participants. A survey was conducted which meant to evaluate satisfaction of exam participants. Statistical analysis of the collected data was conducted at a significance level of α = 0.05 using STATISTICS v. 12. Final results of the traditional exam (67.5% ± 15.8%) differed significantly (p < 0.001) from the results of the interactive exam (53.3% ± 13.7%). However, comparing the number of students who did not pass the exam (passing point at 51%), no significant differences (p = 0.13) were observed between the two types exams. The feedback accuracy as well as the presence of well-prepared interactive questions could influence the evaluation of satisfaction of taking part in the electronic test. Significant differences between the results of a traditional test and the one supported by Computer Based Learning system showed the possibility of achieving a more detailed competence verification in the field of resuscitation thanks to interactive solutions. GRAPHICAL ABSTRACT
有科学证据证实了电子学习在复苏中的有效性，然而，在这一范围内对现代检查技术的研究还不够。试点研究的目的是比较传统(纸质)和交互式(计算机)形式的高级生命支持领域的考试结果，并评估参与者的满意度。进行了一项调查，旨在评估考试参与者的满意度。采用STATISTICS v. 12对收集的数据进行统计学分析，显著性水平为α = 0.05。传统考试的最终结果(67.5%±15.8%)与交互式考试的最终结果(53.3%±13.7%)差异有统计学意义(p < 0.001)。然而，比较未通过考试的学生人数(及格点为51%)，两种类型的考试之间没有显著差异(p = 0.13)。反馈的准确性以及精心准备的互动问题的存在会影响参与电子测试满意度的评价。传统测试结果与基于计算机的学习系统支持的结果之间的显着差异表明，由于交互式解决方案，有可能在复苏领域实现更详细的能力验证。图形抽象
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