https://reunir.unir.net/handle/123456789/12368 Fri, 08 Nov 2024 23:04:14 GMT 2024-11-08T23:04:14Z https://reunir.unir.net/handle/123456789/12384 IJIMAI Editor's Note - Vol. 5 Issue 2 Burgos, Daniel; Nikolov, Roumen; Stracke, Christian M. The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) provides an interdisciplinary forum in which scientists and professionals share their research results and report new advances on tools that use AI with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T10:31:16Z No. of bitstreams: 1 ijimai_5_2_0_pdf_14729.pdf: 117631 bytes, checksum: 906383d4577baa3b12802d1a7857a2dd (MD5); Made available in DSpace on 2022-01-31T10:31:16Z (GMT). No. of bitstreams: 1 ijimai_5_2_0_pdf_14729.pdf: 117631 bytes, checksum: 906383d4577baa3b12802d1a7857a2dd (MD5) https://reunir.unir.net/handle/123456789/12384 https://reunir.unir.net/handle/123456789/12382 Exploring the Benefits of Using Gamification and Videogames for Physical Exercise: a Review of State of Art González-González, Carina; Gómez del Río, Nazaret; Navarro-Adelantado, Vicente There is a lack of motivation in children and adolescents to do physical exercise and at the same time a worldwide obesity epidemic. Gamification and active videogames can be used to increase the motivation of young people, promoting healthy habits. In this work we explore different studies on active videogames, eSports and gamification applied to physical exercise and health promotion. Main findings include positive effects in a reduction in body weight and in the promotion to continue performing of physical exercise. It also contributes to increase the motivation in children and adolescents to practice exercise. The personalization of user experience and emerging technologies (big data, wearables, smart technologies, etc.) are presented as promising opportunities to keep the engagement in game-based program and gamification of physical exercise. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T10:26:29Z No. of bitstreams: 1 ijimai_5_2_6_pdf_43532.pdf: 701313 bytes, checksum: d9387fa0d6151500d16c462f5347898a (MD5); Made available in DSpace on 2022-01-31T10:26:29Z (GMT). No. of bitstreams: 1 ijimai_5_2_6_pdf_43532.pdf: 701313 bytes, checksum: d9387fa0d6151500d16c462f5347898a (MD5) https://reunir.unir.net/handle/123456789/12382 https://reunir.unir.net/handle/123456789/12380 StuA: An Intelligent Student Assistant Jain, Shikha; Lodhi, Pooja; Mishra, Omji; Bajaj, Vasvi With advanced innovation in digital technology, demand for virtual assistants is arising which can assist a person and at the same time, minimize the need for interaction with the human. Acknowledging the requirement, we propose an interactive and intelligent student assistant, StuA, which can help new-comer in a college who are hesitant in interacting with the seniors as they fear of being ragged. StuA is capable of answering all types of queries of a new-comer related to academics, examinations, library, hostel and extra curriculum activities. The model is designed using CLIPS which allows inferring using forward chaining. Nevertheless, a generalized algorithm for backward chaining for CLIPS is also implemented. Validation of the proposed model is presented in five steps which show that the model is complete and consistent with 99.16% accuracy of the knowledge model. Moreover, the backward chaining algorithm is found to be 100% accurate. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T09:58:22Z No. of bitstreams: 1 ijimai_5_2_2_pdf_57769.pdf: 864490 bytes, checksum: e4a1813aac9bb2873fb6c4b8266d9c92 (MD5); Made available in DSpace on 2022-01-31T09:58:22Z (GMT). No. of bitstreams: 1 ijimai_5_2_2_pdf_57769.pdf: 864490 bytes, checksum: e4a1813aac9bb2873fb6c4b8266d9c92 (MD5) https://reunir.unir.net/handle/123456789/12380 https://reunir.unir.net/handle/123456789/12379 UC@MOOC's Effectiveness by Producing Open Educational Resources Margoum, Sofia; Bendaoud, Rachid; Berrada, Khalid; Idrissi Jouicha, Abdellah Open education is one of the most important settings in every society. It grants everyone the right to learn freely. Today, technology is helping to make learning even more open by providing an environment of online education, which plays a remarkable role in shortening distances and encouraging students to learn. At Cadi Ayyad University (UCA) the new-enrolled students are facing linguistics barriers as well as overcrowding in classrooms, in particular for those in open access institutions. Subsequently, they cannot have an easy access to their face-to-face courses. To help students to overcome these problems, the university has decided to design an online environment for all courses and programmes. The most innovative project adopted at UCA to face massification was inspired from the massive open online courses and was designed as an open Educational platform entitled UC@MOOC. More than 120 scripted courses have been posted online so far. In this paper we will describe and discuss an analytics research on geometrical optics course designed for around 2000 students at UCA. Through out this research we will explain how this initiative has been considered as a source of producing open educational resources. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T09:51:19Z No. of bitstreams: 1 ijimai_5_2_8_pdf_53982.pdf: 613254 bytes, checksum: 760bb9fd0ca870c755b574203a8a8995 (MD5); Made available in DSpace on 2022-01-31T09:51:19Z (GMT). No. of bitstreams: 1 ijimai_5_2_8_pdf_53982.pdf: 613254 bytes, checksum: 760bb9fd0ca870c755b574203a8a8995 (MD5) https://reunir.unir.net/handle/123456789/12379 https://reunir.unir.net/handle/123456789/12378 An Integrated Learning Analytics Approach for Virtual Vocational Training Centers Klamma, Ralf; Lange, de Peter; Neumann, Alexander Tobias; Nicolaescu, Petru Virtual training centers are hosted solutions for the implementation of training courses in the form of e.g. Webinars. Many existing centers neglect the informal and social dimension of vocational training as well as the legitimate business interests of training providers and companies sending their employees. In this paper, we present the virtual training center platform V3C that blends formal, certified virtual training courses with self-regulated and social learning in synchronous and asynchronous learning phases. We have developed an integrated learning analytics approach to collect, store, analyze and visualize data for different purposes like certification, interventions and gradual improvement of the platform. The results given here demonstrate the ability of the platform to deliver data for key performance indicators like learning outcomes and drop-out rates as well as the interplay between synchronous and asynchronous learning phases on a very large scale. Since the platform implementation is open source, results can be easily transferred and exploited in many contexts. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T09:28:21Z No. of bitstreams: 1 ijimai_5_2_4_pdf_97157.pdf: 1099837 bytes, checksum: 8104a29d3244961644a8cf16486c8dba (MD5); Made available in DSpace on 2022-01-31T09:28:22Z (GMT). No. of bitstreams: 1 ijimai_5_2_4_pdf_97157.pdf: 1099837 bytes, checksum: 8104a29d3244961644a8cf16486c8dba (MD5) https://reunir.unir.net/handle/123456789/12378 https://reunir.unir.net/handle/123456789/12377 Planning and Allocation of Digital Learning Objects with Augmented Reality to Higher Education Students According to the VARK Model Medina, Mireles; García, Carrillo; Olguín, Montes In the present research, the authors propose the planning, assignment and use of digital learning objects with augmented reality according to the learning style of students in higher education, according to the VARK Model. It is found that students with treatment have had better results in their final grades than students who have not undergone the treatment of having used digital learning objects with augmented reality. The digital objects of learning (DLO’s) with augmented reality designed according to the learning style of the students are an attractive and adequate option so that the teachers who are the main responsible for the didactic planning can spread the knowledge in the students. So that traditional forms of education are put aside and as a result of taking advantage of Information and Communication Technologies that have come to break with the paradigms that have prevailed for years in the teaching - learning process. On the other hand, education based on e-learning platforms facilitates the training of students at a distance allowing them to build and self-manage learning, as well as facilitate the dissemination of digital learning objects with augmented reality according to the learning style according to the VARK Model. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T08:45:17Z No. of bitstreams: 1 ijimai_5_2_7_pdf_10900.pdf: 533229 bytes, checksum: 6c883c7cda3cd3549a2b8a1923402006 (MD5); Made available in DSpace on 2022-01-31T08:45:17Z (GMT). No. of bitstreams: 1 ijimai_5_2_7_pdf_10900.pdf: 533229 bytes, checksum: 6c883c7cda3cd3549a2b8a1923402006 (MD5) https://reunir.unir.net/handle/123456789/12377 https://reunir.unir.net/handle/123456789/12376 Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis Hamoud, Alaa Khalaf; Hashim, Ali Salah; Awadh, Wid Aqeel The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T08:29:11Z No. of bitstreams: 1 ijimai_5_2_3_pdf_30196.pdf: 397209 bytes, checksum: 5691b1fcdb7dbf433b3fa10c20227739 (MD5); Made available in DSpace on 2022-01-31T08:29:11Z (GMT). No. of bitstreams: 1 ijimai_5_2_3_pdf_30196.pdf: 397209 bytes, checksum: 5691b1fcdb7dbf433b3fa10c20227739 (MD5) https://reunir.unir.net/handle/123456789/12376 https://reunir.unir.net/handle/123456789/12370 Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets Martínez Navarro, Álvaro; Moreno-Ger, Pablo Learning Analytics is becoming a key tool for the analysis and improvement of digital education processes, and its potential benefit grows with the size of the student cohorts generating data. In the context of Open Education, the potentially massive student cohorts and the global audience represent a great opportunity for significant analyses and breakthroughs in the field of learning analytics. However, these potentially huge datasets require proper analysis techniques, and different algorithms, tools and approaches may perform better in this specific context. In this work, we compare different clustering algorithms using an educational dataset. We start by identifying the most relevant algorithms in Learning Analytics and benchmark them to determine, according to internal validation and stability measurements, which algorithms perform better. We analyzed seven algorithms, and determined that K-means and PAM were the best performers among partition algorithms, and DIANA was the best performer among hierarchical algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T09:45:52Z No. of bitstreams: 1 ijimai_5_2_1_pdf_84728.pdf: 1574330 bytes, checksum: 54be7ed138da258c12b58a9453092bec (MD5); Made available in DSpace on 2022-01-27T09:45:52Z (GMT). No. of bitstreams: 1 ijimai_5_2_1_pdf_84728.pdf: 1574330 bytes, checksum: 54be7ed138da258c12b58a9453092bec (MD5) https://reunir.unir.net/handle/123456789/12370 https://reunir.unir.net/handle/123456789/12369 Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors García-Peñalvo, Francisco; Cruz-Benito, Juan; Martín-González, Martín; Vázquez-Ingelmo, Andrea; Sánchez-Prieto, José Carlos; Therón, Roberto This paper presents an original study with the aim of propose and test a machine learning approach to research about employability and employment. To understand how the graduates get employed, researchers propose to build predictive models using machine learning algorithms, extracting after that the most relevant factors that describe the model and employing further analysis techniques like clustering to get deeper insights. To test the proposal, is presented a case study that involves data from the Spanish Observatory for Employability and Employment (OEEU). Using data from this project (information about 3000 students), has been built predictive models that define how these students get a job after finalizing their degrees. The results obtained in this case study are very promising, and encourage authors to refine the process and validate it in further research. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T09:35:30Z No. of bitstreams: 1 ijimai_5_2_5_pdf_12552.pdf: 445862 bytes, checksum: 0db264e24b64c55e8111aeb77e3a7cf6 (MD5); Made available in DSpace on 2022-01-27T09:35:30Z (GMT). No. of bitstreams: 1 ijimai_5_2_5_pdf_12552.pdf: 445862 bytes, checksum: 0db264e24b64c55e8111aeb77e3a7cf6 (MD5) https://reunir.unir.net/handle/123456789/12369