ºÝºÝߣshows by User: bflorian / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: bflorian / Mon, 22 Sep 2014 15:24:42 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: bflorian A Recommender System for Students Based on Social Knowledge and Assessment Data of Competences� /slideshow/2014-0919-ectelpresentation/39393483 2014-09-19ec-telpresentation-140922152442-phpapp02
TEL Recommender systems have been used to improve experiences of students or teachers. Many such systems use information about students, such as interests, preferences, and demographic data. They also use resource metadata and ratings. The authors of this paper think that recommender systems are also valuable when implemented in online or blended courses using competence?based assessment since these systems can take advantage of social knowledge about competence development, and students? performance. By using collaborative filtering and knowledge?based techniques, it is possible to obtain recommendations from social knowledge and adapt the former to each student?s performance. In this paper, the authors propose a system to recommend activities and resources that help students in achieving competence levels throughout an online or blended course. This recommender system takes into consideration experiences previously stored and ranked by former students. In order to offer successful learning advice, this recommender system analyzes the student?s current competence levels against similar former students? performances. Functional test results indicate that the proposed technical approach is accurate. Moreover, these results seem to reflect that social knowledge and students' qualifications are sources of valuable recommendations for online and blended courses.]]>
TEL Recommender systems have been used to improve experiences of students or teachers. Many such systems use information about students, such as interests, preferences, and demographic data. They also use resource metadata and ratings. The authors of this paper think that recommender systems are also valuable when implemented in online or blended courses using competence?based assessment since these systems can take advantage of social knowledge about competence development, and students? performance. By using collaborative filtering and knowledge?based techniques, it is possible to obtain recommendations from social knowledge and adapt the former to each student?s performance. In this paper, the authors propose a system to recommend activities and resources that help students in achieving competence levels throughout an online or blended course. This recommender system takes into consideration experiences previously stored and ranked by former students. In order to offer successful learning advice, this recommender system analyzes the student?s current competence levels against similar former students? performances. Functional test results indicate that the proposed technical approach is accurate. Moreover, these results seem to reflect that social knowledge and students' qualifications are sources of valuable recommendations for online and blended courses.]]> Mon, 22 Sep 2014 15:24:42 GMT /slideshow/2014-0919-ectelpresentation/39393483 bflorian@slideshare.net(bflorian) A Recommender System for Students Based on Social Knowledge and Assessment Data of Competences� bflorian TEL Recommender systems have been used to improve experiences of students or teachers. Many such systems use information about students, such as interests, preferences, and demographic data. They also use resource metadata and ratings. The authors of this paper think that recommender systems are also valuable when implemented in online or blended courses using competence?based assessment since these systems can take advantage of social knowledge about competence development, and students? performance. By using collaborative filtering and knowledge?based techniques, it is possible to obtain recommendations from social knowledge and adapt the former to each student?s performance. In this paper, the authors propose a system to recommend activities and resources that help students in achieving competence levels throughout an online or blended course. This recommender system takes into consideration experiences previously stored and ranked by former students. In order to offer successful learning advice, this recommender system analyzes the student?s current competence levels against similar former students? performances. Functional test results indicate that the proposed technical approach is accurate. Moreover, these results seem to reflect that social knowledge and students' qualifications are sources of valuable recommendations for online and blended courses. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2014-09-19ec-telpresentation-140922152442-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> TEL Recommender systems have been used to improve experiences of students or teachers. Many such systems use information about students, such as interests, preferences, and demographic data. They also use resource metadata and ratings. The authors of this paper think that recommender systems are also valuable when implemented in online or blended courses using competence?based assessment since these systems can take advantage of social knowledge about competence development, and students? performance. By using collaborative filtering and knowledge?based techniques, it is possible to obtain recommendations from social knowledge and adapt the former to each student?s performance. In this paper, the authors propose a system to recommend activities and resources that help students in achieving competence levels throughout an online or blended course. This recommender system takes into consideration experiences previously stored and ranked by former students. In order to offer successful learning advice, this recommender system analyzes the student?s current competence levels against similar former students? performances. Functional test results indicate that the proposed technical approach is accurate. Moreover, these results seem to reflect that social knowledge and students&#39; qualifications are sources of valuable recommendations for online and blended courses.
A Recommender System for Students Based on Social Knowledge and Assessment Data of Competences from Beatriz Eugenia Florian-Gaviria
]]> 746 3 https://cdn.slidesharecdn.com/ss_thumbnails/2014-09-19ec-telpresentation-140922152442-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Technology-Enhaced Support for Lifelong Competence Development in Higher Education /slideshow/technologyenhaced-support-for-lifelong-competence-development-in-higher-education/17379823 2013-03-14phddefensebflorian-130319152719-phpapp01
ºÝºÝߣs from my Ph.D. Defense ]]>
ºÝºÝߣs from my Ph.D. Defense ]]> Tue, 19 Mar 2013 15:27:19 GMT /slideshow/technologyenhaced-support-for-lifelong-competence-development-in-higher-education/17379823 bflorian@slideshare.net(bflorian) Technology-Enhaced Support for Lifelong Competence Development in Higher Education bflorian ºÝºÝߣs from my Ph.D. Defense <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2013-03-14phddefensebflorian-130319152719-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ºÝºÝߣs from my Ph.D. Defense
Technology-Enhaced Support for Lifelong Competence Development in Higher Education from Beatriz Eugenia Florian-Gaviria
]]> 842 4 https://cdn.slidesharecdn.com/ss_thumbnails/2013-03-14phddefensebflorian-130319152719-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Activity-Based Learner-Models for Learner Monitoring and Recommendations in Moodle /slideshow/activitybased-learnermodels-for-learner-monitoring-and-recommendations-in-moodle-9446062/9446062 2011-09presentationec-telv5-110927115944-phpapp01
In technology-enhanced learning, activity-based learner models can provide evidence for competence assessment. Such models are the foundation for learning and teaching support, such as: adaptation, assessment, and competence analytics, recommendations, and so on. This paper analyses how to construct activity-based learner models based on existing data in the Moodle learning management system. Based on the activity theory model and the actuator-indicator model, aggregators of learner activities for different activity types were implemented in Moodle. This requires the consideration of the social roles in a course, in order to enable adaptive views for learners and instructors on the stored activity information. The implementation showed that Moodle stores information about course activities that requires filtering before it can get used for higher level processing. The social planes in Moodle reveal a higher complexity than it has been previously described by theories of classroom orchestration, such as actors who are no longer present in a course.]]>
In technology-enhanced learning, activity-based learner models can provide evidence for competence assessment. Such models are the foundation for learning and teaching support, such as: adaptation, assessment, and competence analytics, recommendations, and so on. This paper analyses how to construct activity-based learner models based on existing data in the Moodle learning management system. Based on the activity theory model and the actuator-indicator model, aggregators of learner activities for different activity types were implemented in Moodle. This requires the consideration of the social roles in a course, in order to enable adaptive views for learners and instructors on the stored activity information. The implementation showed that Moodle stores information about course activities that requires filtering before it can get used for higher level processing. The social planes in Moodle reveal a higher complexity than it has been previously described by theories of classroom orchestration, such as actors who are no longer present in a course.]]> Tue, 27 Sep 2011 11:59:40 GMT /slideshow/activitybased-learnermodels-for-learner-monitoring-and-recommendations-in-moodle-9446062/9446062 bflorian@slideshare.net(bflorian) Activity-Based Learner-Models for Learner Monitoring and Recommendations in Moodle bflorian In technology-enhanced learning, activity-based learner models can provide evidence for competence assessment. Such models are the foundation for learning and teaching support, such as: adaptation, assessment, and competence analytics, recommendations, and so on. This paper analyses how to construct activity-based learner models based on existing data in the Moodle learning management system. Based on the activity theory model and the actuator-indicator model, aggregators of learner activities for different activity types were implemented in Moodle. This requires the consideration of the social roles in a course, in order to enable adaptive views for learners and instructors on the stored activity information. The implementation showed that Moodle stores information about course activities that requires filtering before it can get used for higher level processing. The social planes in Moodle reveal a higher complexity than it has been previously described by theories of classroom orchestration, such as actors who are no longer present in a course. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2011-09presentationec-telv5-110927115944-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In technology-enhanced learning, activity-based learner models can provide evidence for competence assessment. Such models are the foundation for learning and teaching support, such as: adaptation, assessment, and competence analytics, recommendations, and so on. This paper analyses how to construct activity-based learner models based on existing data in the Moodle learning management system. Based on the activity theory model and the actuator-indicator model, aggregators of learner activities for different activity types were implemented in Moodle. This requires the consideration of the social roles in a course, in order to enable adaptive views for learners and instructors on the stored activity information. The implementation showed that Moodle stores information about course activities that requires filtering before it can get used for higher level processing. The social planes in Moodle reveal a higher complexity than it has been previously described by theories of classroom orchestration, such as actors who are no longer present in a course.
Activity-Based Learner-Models for Learner Monitoring and Recommendations in Moodle from Beatriz Eugenia Florian-Gaviria
]]> 1195 1 https://cdn.slidesharecdn.com/ss_thumbnails/2011-09presentationec-telv5-110927115944-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-bflorian-48x48.jpg?cb=1522902246 Beatriz E. Florián Gaviria Assistant Professor EISC University of Valle - Colombia beatriz.florian@correounivalle.edu.co https://cdn.slidesharecdn.com/ss_thumbnails/2014-09-19ec-telpresentation-140922152442-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/2014-0919-ectelpresentation/39393483 A Recommender System f... https://cdn.slidesharecdn.com/ss_thumbnails/2013-03-14phddefensebflorian-130319152719-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/technologyenhaced-support-for-lifelong-competence-development-in-higher-education/17379823 Technology-Enhaced Sup... https://cdn.slidesharecdn.com/ss_thumbnails/2011-09presentationec-telv5-110927115944-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/activitybased-learnermodels-for-learner-monitoring-and-recommendations-in-moodle-9446062/9446062 Activity-Based Learner...