Pub Date : 2019-04-03DOI: 10.1080/13614568.2019.1664645
J. Brzostek-Pawlowska, Malgorzata Rubin, A. Salamończyk
ABSTRACT The article presents methods of increasing mathematical content accessibility in educational e-publications using multimodal user interfaces (UI). Educational mathematical publications such as exercise notebooks and worksheets, require student's interactivity in problem solving. EPUB3, an open format for e-publications, has the possibilities of creating multimedia, interactive mathematics content. Among the programs that support EPUB3, only a few support the MathML format presenting formulas, and provides limited possibilities for user interactivity, insufficient in mathematical education. Our solutions in the PlatMat system increase the interactive accessibility of EPUB3 mathematical content for students with visual impairment. The solutions are based on concurrent multimodal alternative interfaces for exploring math content in EPUB3 publications. Students can read and modify formulas choosing preferred UI and device. Similarly, in different modes (visual, acoustic and touch) students can recognise function graphs and shapes of geometrical figures saved in scalable vector graphics (SVG) format. Teachers can create universal mathematical documents for all students. The system supports inclusive maths education and is designed according to the principles of universal design for learning (UDL). The article describes the system’s usefulness in relation to research conducted among maths teachers. Positive results have become the basis for the further development of the system.
{"title":"Enhancement of math content accessibility in EPUB3 educational publications","authors":"J. Brzostek-Pawlowska, Malgorzata Rubin, A. Salamończyk","doi":"10.1080/13614568.2019.1664645","DOIUrl":"https://doi.org/10.1080/13614568.2019.1664645","url":null,"abstract":"ABSTRACT The article presents methods of increasing mathematical content accessibility in educational e-publications using multimodal user interfaces (UI). Educational mathematical publications such as exercise notebooks and worksheets, require student's interactivity in problem solving. EPUB3, an open format for e-publications, has the possibilities of creating multimedia, interactive mathematics content. Among the programs that support EPUB3, only a few support the MathML format presenting formulas, and provides limited possibilities for user interactivity, insufficient in mathematical education. Our solutions in the PlatMat system increase the interactive accessibility of EPUB3 mathematical content for students with visual impairment. The solutions are based on concurrent multimodal alternative interfaces for exploring math content in EPUB3 publications. Students can read and modify formulas choosing preferred UI and device. Similarly, in different modes (visual, acoustic and touch) students can recognise function graphs and shapes of geometrical figures saved in scalable vector graphics (SVG) format. Teachers can create universal mathematical documents for all students. The system supports inclusive maths education and is designed according to the principles of universal design for learning (UDL). The article describes the system’s usefulness in relation to research conducted among maths teachers. Positive results have become the basis for the further development of the system.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"25 1","pages":"31 - 56"},"PeriodicalIF":1.2,"publicationDate":"2019-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2019.1664645","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42939868","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 : 2019-03-26DOI: 10.1080/13614568.2019.1596169
Jeya Amantha Kumar, B. Muniandy, W. Yahaya
ABSTRACT Emotions are an important aspect in learning and with the current boom in instructional technology, researchers are exploring methods to investigate how emotions may be manipulated to positively influence online learning. One such method is by adapting the theory of emotional design through multimedia elements. This theory emphasises on individuality and metacognition in exploring these learning outcomes and by this we choose to explore the effects of emotional intelligence (EI). We replicated the methodology used in previous research studies in emotional design in multimedia learning by further exploring the gaps from those studies especially the effects of negative design, EI and a new sample that primarily focusses on engineering undergraduates in Malaysia. This study was designed as a quantitative quasi-experimental using a 3 × 2 factorial design. Based on the findings, it was found that emotional design is a better predictor of cognitive outcomes, whereas EI was a better predictor of emotional outcomes such as motivation and satisfaction for multimedia-based learning. It was also found that positive and negative designs have similar effects on students’ learning outcomes, while EI affected perceived satisfaction in each design.
{"title":"Exploring the effects of emotional design and emotional intelligence in multimedia-based learning: an engineering educational perspective","authors":"Jeya Amantha Kumar, B. Muniandy, W. Yahaya","doi":"10.1080/13614568.2019.1596169","DOIUrl":"https://doi.org/10.1080/13614568.2019.1596169","url":null,"abstract":"ABSTRACT Emotions are an important aspect in learning and with the current boom in instructional technology, researchers are exploring methods to investigate how emotions may be manipulated to positively influence online learning. One such method is by adapting the theory of emotional design through multimedia elements. This theory emphasises on individuality and metacognition in exploring these learning outcomes and by this we choose to explore the effects of emotional intelligence (EI). We replicated the methodology used in previous research studies in emotional design in multimedia learning by further exploring the gaps from those studies especially the effects of negative design, EI and a new sample that primarily focusses on engineering undergraduates in Malaysia. This study was designed as a quantitative quasi-experimental using a 3 × 2 factorial design. Based on the findings, it was found that emotional design is a better predictor of cognitive outcomes, whereas EI was a better predictor of emotional outcomes such as motivation and satisfaction for multimedia-based learning. It was also found that positive and negative designs have similar effects on students’ learning outcomes, while EI affected perceived satisfaction in each design.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"25 1","pages":"57 - 86"},"PeriodicalIF":1.2,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2019.1596169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47107795","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 : 2018-10-02DOI: 10.1080/13614568.2019.1585486
N. Mirza, H. Khan, Tassawar Iqbal, Khalid Iqbal, Saqib Iqbal, Muhammad Imran
ABSTRACT The quality of user-generated content over World Wide Web media is a matter of serious concern for both creators and users. To measure the quality of content, webometric techniques are commonly used. In recent times, bibliometric techniques have been introduced to good effect for evaluation of the quality of user-generated content, which were originally used for scholarly data. However, the application of bibliometric techniques to evaluate the quality of YouTube content is limited to h-index and g-index considering only views. This paper advocates for and demonstrates the adaptation of existing Bibliometric indices including h-index, g-index and M-index exploiting both views and comments and proposes three indices hvc, gvc and mvc for YouTube video channel ranking. The empirical results prove that the proposed indices using views along with the comments outperform the existing approaches on a real-world dataset of YouTube.
{"title":"Your comments matter: incorporating viewers’ comments for ranking online video content using bibliometrics","authors":"N. Mirza, H. Khan, Tassawar Iqbal, Khalid Iqbal, Saqib Iqbal, Muhammad Imran","doi":"10.1080/13614568.2019.1585486","DOIUrl":"https://doi.org/10.1080/13614568.2019.1585486","url":null,"abstract":"ABSTRACT The quality of user-generated content over World Wide Web media is a matter of serious concern for both creators and users. To measure the quality of content, webometric techniques are commonly used. In recent times, bibliometric techniques have been introduced to good effect for evaluation of the quality of user-generated content, which were originally used for scholarly data. However, the application of bibliometric techniques to evaluate the quality of YouTube content is limited to h-index and g-index considering only views. This paper advocates for and demonstrates the adaptation of existing Bibliometric indices including h-index, g-index and M-index exploiting both views and comments and proposes three indices hvc, gvc and mvc for YouTube video channel ranking. The empirical results prove that the proposed indices using views along with the comments outperform the existing approaches on a real-world dataset of YouTube.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"335 - 345"},"PeriodicalIF":1.2,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2019.1585486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47139106","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 : 2018-10-02DOI: 10.1080/13614568.2019.1572790
Luke van Ryn, T. Apperley, J. Clemens
ABSTRACT Video game avatars have been understood as a key site of players’ “affective investment” in play and games. In this article, we extend this conversation to explore the avatar’s role in engaging players with gaming platforms. Through a case study of Team Fortress 2 (Valve Software, 2007) and the Steam platform, we demonstrate the avatar’s function beyond gameworlds as a tool for encouraging certain kinds of play. Team Fortress 2, we argue, is a crucial testing ground for Valve’s experiments with gaming economies via the Steam platform. By extension, we show the importance of video game avatars for encouraging affective investment in platforms more broadly, including Microsoft’s Xbox Live, PlayStation Network and even workplace dashboards.
{"title":"Avatar economies: affective investment from game to platform","authors":"Luke van Ryn, T. Apperley, J. Clemens","doi":"10.1080/13614568.2019.1572790","DOIUrl":"https://doi.org/10.1080/13614568.2019.1572790","url":null,"abstract":"ABSTRACT Video game avatars have been understood as a key site of players’ “affective investment” in play and games. In this article, we extend this conversation to explore the avatar’s role in engaging players with gaming platforms. Through a case study of Team Fortress 2 (Valve Software, 2007) and the Steam platform, we demonstrate the avatar’s function beyond gameworlds as a tool for encouraging certain kinds of play. Team Fortress 2, we argue, is a crucial testing ground for Valve’s experiments with gaming economies via the Steam platform. By extension, we show the importance of video game avatars for encouraging affective investment in platforms more broadly, including Microsoft’s Xbox Live, PlayStation Network and even workplace dashboards.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"291 - 306"},"PeriodicalIF":1.2,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2019.1572790","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41346548","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 : 2018-10-02DOI: 10.1080/13614568.2019.1568588
F. Orooji, F. Taghiyareh
ABSTRACT In the new “open world” of information, educational systems should involve students in constructing new knowledge of value to a community out of fragmentary information. The already proposed Knowledge Building (KB) approaches typically support only a few general-purpose activities due to the constraints of the utilised web-based environments. To organise and facilitate students’ KB during course activities, this study incorporated services provided by DoosMooc social learning environment into a knowledge transformation model. This approach is completely adapted to an educational context and allows time for iterations, helping students to both contribute to social KB processes and take collective responsibility for improving their understanding of authentic problems. The features provided by the introduced environment support and assess students’ KB activities and facilitate processes of creating, representing, organising, and reviewing different types of knowledge artefacts. The results of a semester-long experiment indicate that the approach and the corresponding instructional design thereof could successfully organise students’ KB activities and facilitate the required interactions. This study reports the impacts of parameters such as learner expertise and quality of shared knowledge on the planned KB processes, and investigates the relationships between students' KB activities and learning achievements.
{"title":"Enhancing students’ knowledge building through utilising social interactions in an online learning environment","authors":"F. Orooji, F. Taghiyareh","doi":"10.1080/13614568.2019.1568588","DOIUrl":"https://doi.org/10.1080/13614568.2019.1568588","url":null,"abstract":"ABSTRACT In the new “open world” of information, educational systems should involve students in constructing new knowledge of value to a community out of fragmentary information. The already proposed Knowledge Building (KB) approaches typically support only a few general-purpose activities due to the constraints of the utilised web-based environments. To organise and facilitate students’ KB during course activities, this study incorporated services provided by DoosMooc social learning environment into a knowledge transformation model. This approach is completely adapted to an educational context and allows time for iterations, helping students to both contribute to social KB processes and take collective responsibility for improving their understanding of authentic problems. The features provided by the introduced environment support and assess students’ KB activities and facilitate processes of creating, representing, organising, and reviewing different types of knowledge artefacts. The results of a semester-long experiment indicate that the approach and the corresponding instructional design thereof could successfully organise students’ KB activities and facilitate the required interactions. This study reports the impacts of parameters such as learner expertise and quality of shared knowledge on the planned KB processes, and investigates the relationships between students' KB activities and learning achievements.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"307 - 334"},"PeriodicalIF":1.2,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2019.1568588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47480965","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 : 2018-07-03DOI: 10.1080/13614568.2018.1524934
Moshe Unger, Bracha Shapira, L. Rokach, Amit Livne
ABSTRACT Context-aware systems enable the sensing and analysis of user context in order to provide personalised services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilised to infer users’ dynamic preferences that are learned over time. We suggest novel methods for inferring the category of the item liked in a specific contextual situation, by applying encoder-decoder learners (long short-term memory networks and auto encoders) on mobile sensor data. In these approaches, the encoder-decoder learners reduce the dimensionality of the contextual features to a latent representation which is learned over time. Given new contextual sensor data from a user, the latent patterns discovered from each deep learner is used to predict the liked item’s category in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual situations with the items’ categories. Empirical results utilising a real world data set of contextual situations derived from mobile phones sensors log show a significant improvement (up to 73% improvement) in prediction accuracy compared with state of the art classification methods.
{"title":"Inferring contextual preferences using deep encoder-decoder learners","authors":"Moshe Unger, Bracha Shapira, L. Rokach, Amit Livne","doi":"10.1080/13614568.2018.1524934","DOIUrl":"https://doi.org/10.1080/13614568.2018.1524934","url":null,"abstract":"ABSTRACT Context-aware systems enable the sensing and analysis of user context in order to provide personalised services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilised to infer users’ dynamic preferences that are learned over time. We suggest novel methods for inferring the category of the item liked in a specific contextual situation, by applying encoder-decoder learners (long short-term memory networks and auto encoders) on mobile sensor data. In these approaches, the encoder-decoder learners reduce the dimensionality of the contextual features to a latent representation which is learned over time. Given new contextual sensor data from a user, the latent patterns discovered from each deep learner is used to predict the liked item’s category in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual situations with the items’ categories. Empirical results utilising a real world data set of contextual situations derived from mobile phones sensors log show a significant improvement (up to 73% improvement) in prediction accuracy compared with state of the art classification methods.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"262 - 290"},"PeriodicalIF":1.2,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1524934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45062021","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 : 2018-07-03DOI: 10.1080/13614568.2018.1525436
Seyyed Hadi Hashemi, J. Kamps
ABSTRACT The Internet of Things (IoT) holds the promise to blend real-world and online behaviors in principled ways, yet we are only beginning to understand how to effectively exploit insights from the online realm into effective applications in smart environments. Such smart environments aim to provide an improved, personalized experience based on the trail of user interactions with smart devices, but how does recommendation in smart environments differ from the usual online recommender systems? And can we exploit similarities to truly blend behavior in both realms to address the fundamental cold-start problem? In this article, we experiment with behavioral user models based on interactions with smart devices in a museum, and investigate the personalized recommendation of what to see after visiting an initial set of Point of Interests (POIs), a key problem in personalizing museum visits or tour guides, and focus on a critical one-shot POI recommendation task—where to go next? We have logged users' onsite physical information interactions during visits in an IoT-augmented museum exhibition at scale. Furthermore, we have collected an even larger set of search logs of the online museum collection. Users in both sets are unconnected, for privacy reasons we do not have shared IDs. We study the similarities between users' online digital and onsite physical information interaction behaviors, and build new behavioral user models based on the information interaction behaviors in (i) the physical exhibition space, (ii) the online collection, or (iii) both. Specifically, we propose a deep neural multilayer perceptron (MLP) based on explicitly given users' contextual information, and set-based extracted features using users' physical information interaction behaviors and similar users' digital information interaction behaviors. Our experimental results indicate that the proposed behavioral user modeling approach, using both physical and online user information interaction behaviors, improves the onsite POI recommendation baselines' performances on all evaluation metrics. Our proposed MLP approach achieves 83% precision at rank 1 on the critical one-shot POI recommendation problem, realizing the high accuracy needed for fruitful deployment in practical situations. Furthermore, the MLP model is less sensitive to amount of real-world interactions in terms of the seen POIs set-size, by backing of to the online data, hence helps address the cold start problem in recommendation. Our general conclusion is that it is possible to fruitfully combine information interactions in the online and physical world for effective recommendation in smart environments.
{"title":"Exploiting behavioral user models for point of interest recommendation in smart museums","authors":"Seyyed Hadi Hashemi, J. Kamps","doi":"10.1080/13614568.2018.1525436","DOIUrl":"https://doi.org/10.1080/13614568.2018.1525436","url":null,"abstract":"ABSTRACT The Internet of Things (IoT) holds the promise to blend real-world and online behaviors in principled ways, yet we are only beginning to understand how to effectively exploit insights from the online realm into effective applications in smart environments. Such smart environments aim to provide an improved, personalized experience based on the trail of user interactions with smart devices, but how does recommendation in smart environments differ from the usual online recommender systems? And can we exploit similarities to truly blend behavior in both realms to address the fundamental cold-start problem? In this article, we experiment with behavioral user models based on interactions with smart devices in a museum, and investigate the personalized recommendation of what to see after visiting an initial set of Point of Interests (POIs), a key problem in personalizing museum visits or tour guides, and focus on a critical one-shot POI recommendation task—where to go next? We have logged users' onsite physical information interactions during visits in an IoT-augmented museum exhibition at scale. Furthermore, we have collected an even larger set of search logs of the online museum collection. Users in both sets are unconnected, for privacy reasons we do not have shared IDs. We study the similarities between users' online digital and onsite physical information interaction behaviors, and build new behavioral user models based on the information interaction behaviors in (i) the physical exhibition space, (ii) the online collection, or (iii) both. Specifically, we propose a deep neural multilayer perceptron (MLP) based on explicitly given users' contextual information, and set-based extracted features using users' physical information interaction behaviors and similar users' digital information interaction behaviors. Our experimental results indicate that the proposed behavioral user modeling approach, using both physical and online user information interaction behaviors, improves the onsite POI recommendation baselines' performances on all evaluation metrics. Our proposed MLP approach achieves 83% precision at rank 1 on the critical one-shot POI recommendation problem, realizing the high accuracy needed for fruitful deployment in practical situations. Furthermore, the MLP model is less sensitive to amount of real-world interactions in terms of the seen POIs set-size, by backing of to the online data, hence helps address the cold start problem in recommendation. Our general conclusion is that it is possible to fruitfully combine information interactions in the online and physical world for effective recommendation in smart environments.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"228 - 261"},"PeriodicalIF":1.2,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1525436","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45879147","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 : 2018-07-03DOI: 10.1080/13614568.2018.1527114
E. Herder, M. Bieliková, F. Cena, M. Desmarais
ACM UMAP is an annual conference on user modeling, adaptation and personalization. User modeling concerns the process of understanding the user’s needs, preferences, interests, knowledge and other aspects. This is achieved by reasoning about and extracting knowledge from user data, which includes both data that is explicitly provided by the user—such as profile data—and implicitly gathered usage data—such as browsing data. Adaptation and personalization techniques exploit the user models in order to better tailor a software system, such as a website, to the user needs. Recommender systems are the best known type of personalized systems, but the field is much wider and includes among others personalized search, adaptive user interfaces, personalized advice, and personalized technology-enhanced learning. This special issue contains extended versions of selected papers from UMAP 2017, the 25th edition of the conference series. The conference was hosted in Bratislava, Slovakia, from 9 to 12 July 2017. The conference consisted of five tracks that represent the variety of disciplines and application areas in which user modeling, adaptation and personalization play a role. User interface aspects, including adaptive presentation and navigation, were covered by the tracks Intelligent User Interfaces and Adaptive Hypermedia. As one of the most visible and largest application area of personalization is the Social Web, we received in the corresponding track submissions that both analyzed user behavior to function as input for personalization, as well as the effect of personalization on user behavior. Being the most prominent and most applied adaptive technique, Recommender Systems were given a dedicated track as well. Finally, we dedicated a track to the field of Technology-Enhanced Adaptive Learning, as this is an application area with important and tangible impacts on society. The papers in this special issue belong to the latter two areas. Three papers are situated in the field of Technology-Enhanced Learning. The first paper, “Analysis and Design of Mastery Learning Criteria” (Pelánek and Řihák), shows that, under the assumption of isolated skills, the decision over skill mastery, and whether a system should let the student move on to the next skill to learn, can rest on a simple exponential moving average rather than on the more sophisticated Bayesian and logistic approaches to learner modeling. They also show that the choice of an appropriate mastery threshold and of the source of information is more influential than the choice of the learner modeling technique. The second paper focuses on open learner models, which is an approach for making a student’s learner model explicit to the student, in order to enhance reflection, self-awareness and self-regulation of the learning process. In “Navigation Support in Complex Learner Models: Assessing Visual Design Alternatives” (Guerra, Schunn, Bull, BarríaPineda and Brusilovsky), six alternative prototypes were
{"title":"Introduction","authors":"E. Herder, M. Bieliková, F. Cena, M. Desmarais","doi":"10.1080/13614568.2018.1527114","DOIUrl":"https://doi.org/10.1080/13614568.2018.1527114","url":null,"abstract":"ACM UMAP is an annual conference on user modeling, adaptation and personalization. User modeling concerns the process of understanding the user’s needs, preferences, interests, knowledge and other aspects. This is achieved by reasoning about and extracting knowledge from user data, which includes both data that is explicitly provided by the user—such as profile data—and implicitly gathered usage data—such as browsing data. Adaptation and personalization techniques exploit the user models in order to better tailor a software system, such as a website, to the user needs. Recommender systems are the best known type of personalized systems, but the field is much wider and includes among others personalized search, adaptive user interfaces, personalized advice, and personalized technology-enhanced learning. This special issue contains extended versions of selected papers from UMAP 2017, the 25th edition of the conference series. The conference was hosted in Bratislava, Slovakia, from 9 to 12 July 2017. The conference consisted of five tracks that represent the variety of disciplines and application areas in which user modeling, adaptation and personalization play a role. User interface aspects, including adaptive presentation and navigation, were covered by the tracks Intelligent User Interfaces and Adaptive Hypermedia. As one of the most visible and largest application area of personalization is the Social Web, we received in the corresponding track submissions that both analyzed user behavior to function as input for personalization, as well as the effect of personalization on user behavior. Being the most prominent and most applied adaptive technique, Recommender Systems were given a dedicated track as well. Finally, we dedicated a track to the field of Technology-Enhanced Adaptive Learning, as this is an application area with important and tangible impacts on society. The papers in this special issue belong to the latter two areas. Three papers are situated in the field of Technology-Enhanced Learning. The first paper, “Analysis and Design of Mastery Learning Criteria” (Pelánek and Řihák), shows that, under the assumption of isolated skills, the decision over skill mastery, and whether a system should let the student move on to the next skill to learn, can rest on a simple exponential moving average rather than on the more sophisticated Bayesian and logistic approaches to learner modeling. They also show that the choice of an appropriate mastery threshold and of the source of information is more influential than the choice of the learner modeling technique. The second paper focuses on open learner models, which is an approach for making a student’s learner model explicit to the student, in order to enhance reflection, self-awareness and self-regulation of the learning process. In “Navigation Support in Complex Learner Models: Assessing Visual Design Alternatives” (Guerra, Schunn, Bull, BarríaPineda and Brusilovsky), six alternative prototypes were","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"131 - 132"},"PeriodicalIF":1.2,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1527114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45774943","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 : 2018-06-25DOI: 10.1080/13614568.2018.1482375
Julio Guerra, C. Schunn, S. Bull, Jordan Barria-Pineda, Peter Brusilovsky
ABSTRACT Open Learner Models are used in modern e-learning to show system users the content of their learner models. This approach is known to prompt reflection, facilitate planning and navigation. Open Learner Models may show different levels of detail of the underlying learner model, and may structure the information differently. However, a trade-off exists between useful information and the complexity of the information. This paper investigates whether offering richer information is assessed positively by learners and results in more effective support for learning tasks. An interview pre-study revealed which information within the complex learner model is of interest. A controlled user study examined six alternative visualisation prototypes of varying complexity and resulted in the implementation of one of the designs. A second controlled study involved students interacting with variations of the visualisation while searching for suitable learning material, and revealed the value of the design alternative and its variations. The work contributes to developing complex open learner models by stressing the need to balance complexity and support. It also suggests that the expressiveness of open learner models can be improved with visual elements that strategically summarise the complex information being displayed in detail.
{"title":"Navigation support in complex open learner models: assessing visual design alternatives","authors":"Julio Guerra, C. Schunn, S. Bull, Jordan Barria-Pineda, Peter Brusilovsky","doi":"10.1080/13614568.2018.1482375","DOIUrl":"https://doi.org/10.1080/13614568.2018.1482375","url":null,"abstract":"ABSTRACT Open Learner Models are used in modern e-learning to show system users the content of their learner models. This approach is known to prompt reflection, facilitate planning and navigation. Open Learner Models may show different levels of detail of the underlying learner model, and may structure the information differently. However, a trade-off exists between useful information and the complexity of the information. This paper investigates whether offering richer information is assessed positively by learners and results in more effective support for learning tasks. An interview pre-study revealed which information within the complex learner model is of interest. A controlled user study examined six alternative visualisation prototypes of varying complexity and resulted in the implementation of one of the designs. A second controlled study involved students interacting with variations of the visualisation while searching for suitable learning material, and revealed the value of the design alternative and its variations. The work contributes to developing complex open learner models by stressing the need to balance complexity and support. It also suggests that the expressiveness of open learner models can be improved with visual elements that strategically summarise the complex information being displayed in detail.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"160 - 192"},"PeriodicalIF":1.2,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1482375","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46379225","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 : 2018-06-21DOI: 10.1080/13614568.2018.1477999
J. Okpo, J. Masthoff, Matt Dennis, N. Beacham
ABSTRACT Adapting to learner characteristics is essential when selecting exercises for learners in an intelligent tutoring system. This paper investigates how humans adapt next exercise selection (in particular difficulty level) to learner personality, invested mental effort, and performance to inspire an adaptive exercise selection algorithm. First, the paper describes the investigations to produce validated materials for the main studies, namely the creation and validation of self-esteem personality stories, mental effort statements, and mathematical exercises with varying levels of difficulty. Next, through empirical studies, we investigate the impact on exercise selection of learner's self-esteem (low versus high self-esteem) and effort (minimal, little, moderate, much, and all possible effort). Three studies investigate this for learners who had different performances on a previous exercise: just passing, just failing, and performed well. Participants considered a fictional learner with a certain performance, self-esteem and effort, and selected the difficulty level of the next mathematical exercise. We found that self-esteem, mental effort, and performance all impacted the difficulty level of the exercises selected for learners. Finally, using the results from the studies, we propose an algorithm that selects exercises with varying difficulty levels adapted to learner characteristics.
{"title":"Adapting exercise selection to performance, effort and self-esteem","authors":"J. Okpo, J. Masthoff, Matt Dennis, N. Beacham","doi":"10.1080/13614568.2018.1477999","DOIUrl":"https://doi.org/10.1080/13614568.2018.1477999","url":null,"abstract":"ABSTRACT Adapting to learner characteristics is essential when selecting exercises for learners in an intelligent tutoring system. This paper investigates how humans adapt next exercise selection (in particular difficulty level) to learner personality, invested mental effort, and performance to inspire an adaptive exercise selection algorithm. First, the paper describes the investigations to produce validated materials for the main studies, namely the creation and validation of self-esteem personality stories, mental effort statements, and mathematical exercises with varying levels of difficulty. Next, through empirical studies, we investigate the impact on exercise selection of learner's self-esteem (low versus high self-esteem) and effort (minimal, little, moderate, much, and all possible effort). Three studies investigate this for learners who had different performances on a previous exercise: just passing, just failing, and performed well. Participants considered a fictional learner with a certain performance, self-esteem and effort, and selected the difficulty level of the next mathematical exercise. We found that self-esteem, mental effort, and performance all impacted the difficulty level of the exercises selected for learners. Finally, using the results from the studies, we propose an algorithm that selects exercises with varying difficulty levels adapted to learner characteristics.","PeriodicalId":54386,"journal":{"name":"New Review of Hypermedia and Multimedia","volume":"24 1","pages":"193 - 227"},"PeriodicalIF":1.2,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614568.2018.1477999","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47437754","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}
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