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Yao Y, Xia J. Optimization of Ideological and Political Education Strategies in Colleges and Universities Based on Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4807169. [PMID: 35942444 PMCID: PMC9356794 DOI: 10.1155/2022/4807169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 11/18/2022]
Abstract
In the current technological world, artificially intelligent deep learning techniques are adapted in many fields. This advanced technology is also used in the field of education. In this study, people will conduct research on the optimization of ideological and political education strategies in colleges and universities based on deep learning. Deep learning is often a machine learning technique that uses artificial neural networks that allow a machine to imitate human behaviour. Ideological and political education deals with the social studies implied by the political scenario. Ideological and political education aims to teach the younger generation social, economic, and political awareness. In our proposed system, people will deploy the deep learning algorithm named brute force algorithm to optimize ideological and political education in colleges and universities. The teaching optimization is performed by automating the training of the deep learning model. The results were compared with the existing K-means algorithm, and it is observed that the proposed system has achieved a higher accuracy of 99.12% in optimizing the educational strategies.
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Affiliation(s)
- Yanxia Yao
- School of Marxism, Hunan City University, Yiyang 413000, Hunan, China
| | - Jianwen Xia
- School of Marxism, Hunan City University, Yiyang 413000, Hunan, China
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2
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Visualizing Collaboration in Teamwork: A Multimodal Learning Analytics Platform for Non-Verbal Communication. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Developing communication skills in collaborative contexts is of special interest for educational institutions, since these skills are crucial to forming competent professionals for today’s world. New and accessible technologies open a way to analyze collaborative activities in face-to-face and non-face-to-face situations, where collaboration and student attitudes are difficult to measure using traditional methods. In this context, Multimodal Learning Analytics (MMLA) appear as an alternative to complement the evaluation and feedback of core skills. We present a MMLA platform to support collaboration assessment based on the capture and classification of non-verbal communication interactions. The developed platform integrates hardware and software, including machine learning techniques, to detect spoken interactions and body postures from video and audio recordings. The captured data is presented in a set of visualizations, designed to help teachers to obtain insights about the collaboration of a team. We performed a case study to explore if the visualizations were useful to represent different behavioral indicators of collaboration in different teamwork situations: a collaborative situation and a competitive situation. We discussed the results of the case study in a focus group with three teachers, to get insights in the usefulness of our proposal. The results show that the measurements and visualizations are helpful to understand differences in collaboration, confirming the feasibility the MMLA approach for assessing and providing collaboration insights based on non-verbal communication.
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He X, Chen P, Wu J, Dong Z. Deep Learning-Based Teaching Strategies of Ideological and Political Courses Under the Background of Educational Psychology. Front Psychol 2021; 12:731166. [PMID: 34744900 PMCID: PMC8569183 DOI: 10.3389/fpsyg.2021.731166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 09/16/2021] [Indexed: 11/13/2022] Open
Abstract
At present, low teaching efficiency has been the common problem of ideological and political education in colleges and universities in China. It is essential to improve the teaching efficiency and realize the intelligent information transformation of the ideological and political courses in colleges and universities. First, the relationship between ideological and political courses and the educational psychology of college students was analyzed based on the theoretical characteristics of educational psychology and college ideological and political courses. Additionally, the teaching efficiency of ideological and political courses based on deep learning (DL) was analyzed through a literature survey. Combined with online teaching modes such as the flipped classroom and Massive Open Online Courses, a comprehensive online teaching mode of college ideological and political courses was proposed via educational psychology and the Single Shot MutiBox Detector networks of DL. Then, a total of 100 research subjects were selected randomly from the freshmen and sophomores of the Southwest University of Science and Technology, and their acceptability to the online ideological and political courses was analyzed by a questionnaire survey. The results show that the adopted questionnaire had high reliability and validity, and the proportion of respondents of different genders, grades, and majors was essentially balanced. More than half of the students had a good understanding of the comprehensive ideological and political courses and made progress in their values, ideology, morals, and knowledge reserves. More than half of the students had a positive attitude to the course, and they thought that the class atmosphere of the course was active, which was conducive to a satisfactory learning effect. This indicates that the teaching strategy of ideological and political courses in colleges and universities that integrates educational psychology, DL, and online information can attract students. The contribution of this study is that the research outcome can be applied to the concrete formulation of the teaching strategies of ideological and political courses for college students.
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Affiliation(s)
- Xiaoqing He
- School of Marxism, Chengdu Normal University, Chengdu, China
| | - Peiyao Chen
- China Aerospace Science & Industry Corp., Beijing, China
| | - Jieting Wu
- Engineering University of Armed Police Force, Urumqi, China
| | - Zhen Dong
- School of Marxism, Sichuan Tourism University, Chengdu, China
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Vieira F, Cechinel C, Ramos V, Riquelme F, Noel R, Villarroel R, Cornide-Reyes H, Munoz R. A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations. SENSORS 2021; 21:s21041525. [PMID: 33671797 PMCID: PMC7926817 DOI: 10.3390/s21041525] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/05/2021] [Accepted: 01/25/2021] [Indexed: 11/16/2022]
Abstract
Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.
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Affiliation(s)
- Felipe Vieira
- Centro de Ciências, Tecnologias e Saúde, Universidade Federal de Santa Catarina, Araranguá 88906072, Brazil; (C.C.); (V.R.)
- Correspondence:
| | - Cristian Cechinel
- Centro de Ciências, Tecnologias e Saúde, Universidade Federal de Santa Catarina, Araranguá 88906072, Brazil; (C.C.); (V.R.)
| | - Vinicius Ramos
- Centro de Ciências, Tecnologias e Saúde, Universidade Federal de Santa Catarina, Araranguá 88906072, Brazil; (C.C.); (V.R.)
| | - Fabián Riquelme
- Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362735, Chile; (F.R.); (R.N.); (R.M.)
| | - Rene Noel
- Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362735, Chile; (F.R.); (R.N.); (R.M.)
| | - Rodolfo Villarroel
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile;
| | - Hector Cornide-Reyes
- Departamento de Ingeniería Informática y Ciencias de la Computación, Universidad de Atacama, Copiapó 1531772, Chile;
| | - Roberto Munoz
- Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362735, Chile; (F.R.); (R.N.); (R.M.)
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Mu S, Cui M, Huang X. Multimodal Data Fusion in Learning Analytics: A Systematic Review. SENSORS 2020; 20:s20236856. [PMID: 33266131 PMCID: PMC7729570 DOI: 10.3390/s20236856] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 11/26/2020] [Accepted: 11/28/2020] [Indexed: 12/05/2022]
Abstract
Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper systematically surveys 346 articles on MMLA published during the past three years. For this purpose, we first present a conceptual model for reviewing these articles from three dimensions: data types, learning indicators, and data fusion. Based on this model, we then answer the following questions: 1. What types of data and learning indicators are used in MMLA, together with their relationships; and 2. What are the classifications of the data fusion methods in MMLA. Finally, we point out the key stages in data fusion and the future research direction in MMLA. Our main findings from this review are (a) The data in MMLA are classified into digital data, physical data, physiological data, psychometric data, and environment data; (b) The learning indicators are behavior, cognition, emotion, collaboration, and engagement; (c) The relationships between multimodal data and learning indicators are one-to-one, one-to-any, and many-to-one. The complex relationships between multimodal data and learning indicators are the key for data fusion; (d) The main data fusion methods in MMLA are many-to-one, many-to-many and multiple validations among multimodal data; and (e) Multimodal data fusion can be characterized by the multimodality of data, multi-dimension of indicators, and diversity of methods.
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Affiliation(s)
- Su Mu
- School of Information Technology in Education, South China Normal University, Guangzhou 510631, China;
| | - Meng Cui
- School of Information Technology in Education, South China Normal University, Guangzhou 510631, China;
- Correspondence:
| | - Xiaodi Huang
- School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia;
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Cornide-Reyes H, Riquelme F, Monsalves D, Noel R, Cechinel C, Villarroel R, Ponce F, Munoz R. A Multimodal Real-Time Feedback Platform Based on Spoken Interactions for Remote Active Learning Support. SENSORS 2020; 20:s20216337. [PMID: 33172039 PMCID: PMC7664242 DOI: 10.3390/s20216337] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/02/2020] [Accepted: 11/03/2020] [Indexed: 12/02/2022]
Abstract
While technology has helped improve process efficiency in several domains, it still has an outstanding debt to education. In this article, we introduce NAIRA, a Multimodal Learning Analytics platform that provides Real-Time Feedback to foster collaborative learning activities’ efficiency. NAIRA provides real-time visualizations for students’ verbal interactions when working in groups, allowing teachers to perform precise interventions to ensure learning activities’ correct execution. We present a case study with 24 undergraduate subjects performing a remote collaborative learning activity based on the Jigsaw learning technique within the COVID-19 pandemic context. The main goals of the study are (1) to qualitatively describe how the teacher used NAIRA’s visualizations to perform interventions and (2) to identify quantitative differences in the number and time between students’ spoken interactions among two different stages of the activity, one of them supported by NAIRA’s visualizations. The case study showed that NAIRA allowed the teacher to monitor and facilitate the learning activity’s supervised stage execution, even in a remote learning context, with students working in separate virtual classrooms with their video cameras off. The quantitative comparison of spoken interactions suggests the existence of differences in the distribution between the monitored and unmonitored stages of the activity, with a more homogeneous speaking time distribution in the NAIRA supported stage.
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Affiliation(s)
- Hector Cornide-Reyes
- Departamento de Ingeniería Informática y Ciencias de la Computación, Universidad de Atacama, Copiapó 1534146, Chile
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile;
- Correspondence:
| | - Fabián Riquelme
- Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile; (F.R.); (D.M.); (R.N.); (F.P.); (R.M.)
| | - Diego Monsalves
- Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile; (F.R.); (D.M.); (R.N.); (F.P.); (R.M.)
| | - Rene Noel
- Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile; (F.R.); (D.M.); (R.N.); (F.P.); (R.M.)
| | - Cristian Cechinel
- Centro de Ciências, Tecnologias e Saúde, Universidade Federal de Santa Catarina, Araranguá 88906-072, Brazil;
| | - Rodolfo Villarroel
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile;
| | - Francisco Ponce
- Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile; (F.R.); (D.M.); (R.N.); (F.P.); (R.M.)
| | - Roberto Munoz
- Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile; (F.R.); (D.M.); (R.N.); (F.P.); (R.M.)
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González Crespo R, Burgos D. Advanced Sensors Technology in Education. SENSORS 2019; 19:s19194155. [PMID: 31557927 PMCID: PMC6806318 DOI: 10.3390/s19194155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 09/23/2019] [Indexed: 11/24/2022]
Abstract
The topic presented will show how different kinds of sensors can help to improve our skills in learning environments. When we open the mind and let it take the control to be creative, we can think how a martial art would be improved with registered sensors, or how a person may dance with machines to improve their technique, or how you may improve your soccer kick for a penalties round. The use of sensors seems easy to imagine in these examples, but their use is not limited to these types of learning environments. Using depth cameras to detect patterns in oral presentations, or improving the assessment of agility through low cost-sensors with multimodal learning analytics, or using computing devices as sensors to measure their impact on primary and secondary students’ performances are the focus of this study as well. We hope readers will find original ideas that allow them to improve and advance in their own researches.
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Affiliation(s)
- Rubén González Crespo
- Faculty of Engineering, Universidad Internacional de La Rioja, Av. de la Paz, 137, 26006 Logroño, La Rioja, Spain.
| | - Daniel Burgos
- Research Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja (UNIR), 26006 Logroño (La Rioja), Spain.
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