https://reunir.unir.net/handle/123456789/9798 Mon, 11 Nov 2024 01:55:34 GMT 2024-11-11T01:55:34Z https://reunir.unir.net/handle/123456789/9811 Big Data and Learning Analytics in Blended Learning Environments: Benefits and Concerns Picciano, Anthony G. The purpose of this article is to examine big data and learning analytics in blended learning environments. It will examine the nature of these concepts, provide basic definitions, and identify the benefits and concerns that apply to their development and implementation. This article draws on concepts associated with data-driven decision making, which evolved in the 1980s and 1990s, and takes a sober look at big data and analytics. It does not present them as panaceas for all of the issues and decisions faced by higher education administrators, but sees them as part of solutions, although not without significant investments of time and money to achieve worthwhile benefits. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-07T13:19:21Z No. of bitstreams: 1 ijimai20142_7_5_pdf_15146.pdf: 494487 bytes, checksum: 3164ce650970287711026731323f1586 (MD5); Made available in DSpace on 2020-02-07T13:19:21Z (GMT). No. of bitstreams: 1 ijimai20142_7_5_pdf_15146.pdf: 494487 bytes, checksum: 3164ce650970287711026731323f1586 (MD5) https://reunir.unir.net/handle/123456789/9811 https://reunir.unir.net/handle/123456789/9810 Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study Corbi, Alberto; Burgos, Daniel Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal), relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI), detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-07T13:10:52Z No. of bitstreams: 1 ijimai20142_7_6_pdf_27449.pdf: 927662 bytes, checksum: 424250d2b9c8eb81d03124ebe87cba84 (MD5); Made available in DSpace on 2020-02-07T13:10:52Z (GMT). No. of bitstreams: 1 ijimai20142_7_6_pdf_27449.pdf: 927662 bytes, checksum: 424250d2b9c8eb81d03124ebe87cba84 (MD5) https://reunir.unir.net/handle/123456789/9810 https://reunir.unir.net/handle/123456789/9809 Analysis of Gait Pattern to Recognize the Human Activities Prakash Gupta, Jay; Dixit, Pushkar; Singh, Nishant; Bhaskar Aemwal, Vijay Human activity recognition based on the computer vision is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here we propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. We contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that our method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the videos Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-07T12:28:26Z No. of bitstreams: 1 ijimai20142_7_1_pdf_23675.pdf: 691721 bytes, checksum: 7cee259713f458b360f84b87d8ac65c2 (MD5); Made available in DSpace on 2020-02-07T12:28:26Z (GMT). No. of bitstreams: 1 ijimai20142_7_1_pdf_23675.pdf: 691721 bytes, checksum: 7cee259713f458b360f84b87d8ac65c2 (MD5) https://reunir.unir.net/handle/123456789/9809 https://reunir.unir.net/handle/123456789/9808 Integration of Multiple Data Sources for predicting the Engagement of Students in Practical Activities Tobarra, Llanos; Ros, Salvador; Hernández, Roberto; Robles-Gómez, Antonio; Caminero, Agustín C.; Pastor, Rafael This work presents the integration of an automatic assessment system for virtual/remote laboratories and the institutional Learning Management System (LMS), in order to analyze the students’ progress and their collaborative learning in virtual/remote laboratories. As a result of this integration, it is feasible to extract useful information for the characterization of the students’ learning process and detecting the students’ engagement with the practical activities of our subjects. From this integration, a dashboard has been created to graphically present to lecturers the analyzed results. Thanks to this, faculty can use the analyzed information in order to guide the learning/teaching process of each student. As an example, a subject focused on the configuration of network services has been chosen to implement our proposal. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-07T11:48:04Z No. of bitstreams: 1 ijimai20142_7_7_pdf_21332.pdf: 778074 bytes, checksum: 7ff59829d8dca9b02c6e19e85b47fa6f (MD5); Made available in DSpace on 2020-02-07T11:48:04Z (GMT). No. of bitstreams: 1 ijimai20142_7_7_pdf_21332.pdf: 778074 bytes, checksum: 7ff59829d8dca9b02c6e19e85b47fa6f (MD5) https://reunir.unir.net/handle/123456789/9808 https://reunir.unir.net/handle/123456789/9803 Recognition of Emotions using Energy Based Bimodal Information Fusion and Correlation Asawa, Krishna; Manchanda, Priyanka Multi-sensor information fusion is a rapidly developing research area which forms the backbone of numerous essential technologies such as intelligent robotic control, sensor networks, video and image processing and many more. In this paper, we have developed a novel technique to analyze and correlate human emotions expressed in voice tone & facial expression. Audio and video streams captured to populate audio and video bimodal data sets to sense the expressed emotions in voice tone and facial expression respectively. An energy based mapping is being done to overcome the inherent heterogeneity of the recorded bi-modal signal. The fusion process uses sampled and mapped energy signal of both modalities’s data stream and further recognize the overall emotional component using Support Vector Machine (SVM) classifier with the accuracy 93.06%. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T13:43:01Z No. of bitstreams: 1 ijimai20142_7_2_pdf_26941.pdf: 747112 bytes, checksum: 4c4f2f247d8fcfb61e12ec6636a4889a (MD5); Made available in DSpace on 2020-02-05T13:43:01Z (GMT). No. of bitstreams: 1 ijimai20142_7_2_pdf_26941.pdf: 747112 bytes, checksum: 4c4f2f247d8fcfb61e12ec6636a4889a (MD5) https://reunir.unir.net/handle/123456789/9803 https://reunir.unir.net/handle/123456789/9802 Social Networks as Learning Environments for Higher Education Cortés, J.A.; Lozano, J.O. Learning is considered as a social activity, a student does not learn only of the teacher and the textbook or only in the classroom, learn also from many other agents related to the media, peers and society in general. And since the explosion of the Internet, the information is within the reach of everyone, is there where the main area of opportunity in new technologies applied to education, as well as taking advantage of recent socialization trends that can be leveraged to improve not only informing of their daily practices, but rather as a tool that explore different branches of education research. One can foresee the future of higher education as a social learning environment, open and collaborative, where people construct knowledge in interaction with others, in a comprehensive manner. The mobility and ubiquity that provide mobile devices enable the connection from anywhere and at any time. In modern educational environments can be expected to facilitate mobile devices in the classroom expansion in digital environments, so that students and teachers can build the teaching-learning process collectively, this partial derivative results in the development of draft research approved by the CONADI in “Universidad Cooperativa de Colombia”, "Social Networks: A teaching strategy in learning environments in higher education." Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T13:10:53Z No. of bitstreams: 1 ijimai20142_7_8_pdf_87965.pdf: 424646 bytes, checksum: 4941c27a64595243f261333c1b30c183 (MD5); Made available in DSpace on 2020-02-05T13:10:53Z (GMT). No. of bitstreams: 1 ijimai20142_7_8_pdf_87965.pdf: 424646 bytes, checksum: 4941c27a64595243f261333c1b30c183 (MD5) https://reunir.unir.net/handle/123456789/9802 https://reunir.unir.net/handle/123456789/9801 Dissemination Matters: Influences of Dissemination Activities on User Types in an Online Educational Community Yuan, Min; Recker, Mimi Emerging online educational communities provide spaces for teachers to find resources, create instructional activities, and share these activities with others. Within these online communities, individual users’ activities may vary widely, and thus different user types can be identified. In addition, users’ patterns of activities in online communities are dynamic, and further can be affected by dissemination activities. Through analyzing usage analytics in an online teacher community called the Instructional Architect, this study explores the influences of dissemination activities on the usage patterns of different user types. Results show that dissemination activities can play an important role in encouraging users’ active participation, while the absence of dissemination activities can further increase participation inequality. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T12:56:30Z No. of bitstreams: 1 ijimai20142_7_3_pdf_32458.pdf: 558405 bytes, checksum: ae2ff68a41dd9683c506ae2cfd822d14 (MD5); Made available in DSpace on 2020-02-05T12:56:30Z (GMT). No. of bitstreams: 1 ijimai20142_7_3_pdf_32458.pdf: 558405 bytes, checksum: ae2ff68a41dd9683c506ae2cfd822d14 (MD5) https://reunir.unir.net/handle/123456789/9801 https://reunir.unir.net/handle/123456789/9800 Editor’s Note de-la-Fuente-Valentín, Luis; Burgos, Daniel; Mazza, Riccardo This special issue, Special Issue on Multisensor user tracking and analytics to improve education and other application fields, concentrates on the practical and experimental use of data mining and analytics techniques, specially focusing on the educational area. The selected papers deal with the most relevant issues in the field, such as the integration of data from different sources, the identification of data suitable for the problem analysis, and the validation of the analytics techniques as support in the decision making process. The application fields of the analytics techniques presented in this paper have a clear focus on the educational area (where Learning Analytics has emerged as a buzzword in the recent years) but not restricted to it. The result is a collection of use cases, experimental validations and analytics systems with a clear contribution to the state of the art. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T12:42:45Z No. of bitstreams: 1 ijimai20142_7_0_pdf_30073.pdf: 282935 bytes, checksum: 93c3f6fb2de1861c76b164e0c42a9155 (MD5); Made available in DSpace on 2020-02-05T12:42:45Z (GMT). No. of bitstreams: 1 ijimai20142_7_0_pdf_30073.pdf: 282935 bytes, checksum: 93c3f6fb2de1861c76b164e0c42a9155 (MD5) https://reunir.unir.net/handle/123456789/9800 https://reunir.unir.net/handle/123456789/9799 Personality and Education Mining based Job Advisory System Choudhary, Rajendra S.; Kukreja, Rajul; Jain, Nitika; Jain, Shikha Every job demands an employee with some specific qualities in addition to the basic educational qualification. For example, an introvert person cannot be a good leader despite of a very good academic qualification. Thinking and logical ability is required for a person to be a successful software engineer. So, the aim of this paper is to present a novel approach for advising an ideal job to the job seeker while considering his personality trait and educational qualification both. Very well-known theories of personality like MBTI indicator and OCEAN theory, are used for personality mining. For education mining, score based system is used. The score based system captures the information from attributes like most scoring subject, dream job etc. After personality mining, the resultant values are coalesced with the information extracted from education mining. And finally, the most suited jobs, in terms of personality and educational qualification are recommended to the job seekers. The experiment is conducted on the students who have earned an engineering degree in the field of computer science, information technology and electronics. Nevertheless, the same architecture can easily be extended to other educational degrees also. To the best of the author’s knowledge, this is a first e-job advisory system that recommends the job best suited as per one’s personality using MBTI and OCEAN theory both. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T12:16:42Z No. of bitstreams: 1 ijimai20142_7_4_pdf_92105.pdf: 431640 bytes, checksum: b6bc49b7833bad0cd1be763ed0dbf0e2 (MD5); Made available in DSpace on 2020-02-05T12:16:42Z (GMT). No. of bitstreams: 1 ijimai20142_7_4_pdf_92105.pdf: 431640 bytes, checksum: b6bc49b7833bad0cd1be763ed0dbf0e2 (MD5) https://reunir.unir.net/handle/123456789/9799