Pub Date : 2020-11-16DOI: 10.1109/SCCC51225.2020.9281170
Elizabeth Vidal, Y. Toro
The new demands by the present century have made international institutions like UNESCO place emphasis on the impact of lifelong learning. Immanent in lifelong learning, the socalled information literacy has been considered as the basis for the development of this competence. In this article we present our experience in the creation and use of Personal Learning Environments to develop this competence in students of the first semester of Industrial Engineering. The structure of the activity and its relationship with the five information literacy standards proposed by the "Association College Research Libraries" are presented. The initial results show us the probable effectiveness of the proposal implemented in an initial stage. The students who were part of the study showed a percentage greater than 65% in the five standards in terms of their perception. We believe that the experience presented can be adapted to different contexts and disciplines.
{"title":"Information Literacy for Lifelong Learning: an experience with Personal Learning Environments","authors":"Elizabeth Vidal, Y. Toro","doi":"10.1109/SCCC51225.2020.9281170","DOIUrl":"https://doi.org/10.1109/SCCC51225.2020.9281170","url":null,"abstract":"The new demands by the present century have made international institutions like UNESCO place emphasis on the impact of lifelong learning. Immanent in lifelong learning, the socalled information literacy has been considered as the basis for the development of this competence. In this article we present our experience in the creation and use of Personal Learning Environments to develop this competence in students of the first semester of Industrial Engineering. The structure of the activity and its relationship with the five information literacy standards proposed by the \"Association College Research Libraries\" are presented. The initial results show us the probable effectiveness of the proposal implemented in an initial stage. The students who were part of the study showed a percentage greater than 65% in the five standards in terms of their perception. We believe that the experience presented can be adapted to different contexts and disciplines.","PeriodicalId":117157,"journal":{"name":"2020 39th International Conference of the Chilean Computer Science Society (SCCC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122987525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, datasets are an essential asset used to train, validate, and test stress detection systems based on machine learning. In this paper, we used two sets of FAIR metrics for evaluating five public datasets for stress detection. Results indicate that all these datasets comply to some extent with the (F)indable, (A)ccessible, and (R)eusable principles, but none with the (I)nteroperable principle. These findings contribute to raising awareness on (i) the need for the FAIRness development and improvement of stress datasets, and (ii) the importance of promoting open science in the affective computing community.
{"title":"A FAIR evaluation of public datasets for stress detection systems","authors":"Álvaro Cuno, Nelly Condori-Fernández, Alexis Mendoza, Wilber Roberto Ramos Lovón","doi":"10.1109/SCCC51225.2020.9281274","DOIUrl":"https://doi.org/10.1109/SCCC51225.2020.9281274","url":null,"abstract":"Nowadays, datasets are an essential asset used to train, validate, and test stress detection systems based on machine learning. In this paper, we used two sets of FAIR metrics for evaluating five public datasets for stress detection. Results indicate that all these datasets comply to some extent with the (F)indable, (A)ccessible, and (R)eusable principles, but none with the (I)nteroperable principle. These findings contribute to raising awareness on (i) the need for the FAIRness development and improvement of stress datasets, and (ii) the importance of promoting open science in the affective computing community.","PeriodicalId":117157,"journal":{"name":"2020 39th International Conference of the Chilean Computer Science Society (SCCC)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131371798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-16DOI: 10.1109/SCCC51225.2020.9281212
Manuel J. Ibarra, Hesmeralda Rojas, Yonatan Mamani-Coaquira, Herwin Alayn Huillcen-Baca, Flor de Luz Palomino-Valdivia, Eliana M. Ibarra-Cabrera
This project shows the implementation of a portable repository for Ardora project-based learning objects, which was designed for schools without an Internet connection. The proposal was evaluated by 16 teachers in Apurímac-Peru. First, teachers were trained in the use of Ardora software, then they created learning objects using Ardora according to the course they teach at school; then they published the learning objects in the portable repository. The focus group methodology was used to obtain the opinion of the teachers, the results show that they agree that the portable repository can be used for the design and storage of learning objects. On the other hand, load tests were performed on the Raspberry Pi server, and the portable repository could have 200 requests with 10 users concurrently.
{"title":"Portable repository for learning objects for schools without Internet connection","authors":"Manuel J. Ibarra, Hesmeralda Rojas, Yonatan Mamani-Coaquira, Herwin Alayn Huillcen-Baca, Flor de Luz Palomino-Valdivia, Eliana M. Ibarra-Cabrera","doi":"10.1109/SCCC51225.2020.9281212","DOIUrl":"https://doi.org/10.1109/SCCC51225.2020.9281212","url":null,"abstract":"This project shows the implementation of a portable repository for Ardora project-based learning objects, which was designed for schools without an Internet connection. The proposal was evaluated by 16 teachers in Apurímac-Peru. First, teachers were trained in the use of Ardora software, then they created learning objects using Ardora according to the course they teach at school; then they published the learning objects in the portable repository. The focus group methodology was used to obtain the opinion of the teachers, the results show that they agree that the portable repository can be used for the design and storage of learning objects. On the other hand, load tests were performed on the Raspberry Pi server, and the portable repository could have 200 requests with 10 users concurrently.","PeriodicalId":117157,"journal":{"name":"2020 39th International Conference of the Chilean Computer Science Society (SCCC)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116006552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-16DOI: 10.1109/SCCC51225.2020.9281283
C. Manzano, Claudio Meneses Villegas, Paul Leger
Android mobile systems are currently the main target of malware attacks. In this sense, machine learning is a suitable approach to analyze network traffic, and it generally achieves good results in the identification and detection of malware. However, an underlying problem is creating a dataset with network characteristics that accurately reflect the malwareś behavior. Characterizing adequately the dataset is a relevant process to identify malware with high precision when using traditional machine learning algorithms. This paper compares empirically three supervised machine learning algorithms, in order to identify ransomware traffic based on Android mobile network traffic features. We consider 9 features related to time properties of flows and bidirectional packets in 10 families of ransomware and different benign application Android network traffic. Empirical results show that Random Forest (RF) achieved a 96% accuracy in classifying ransomware, higher than Decision Tree (DT) and K-Nearest Neighbor (KNN) approaches. We conclude that the selected features allow us to identify ransomware traffic and differentiate it from the traffic of benign applications.
{"title":"An Empirical Comparison of Supervised Algorithms for Ransomware Identification on Network Traffic","authors":"C. Manzano, Claudio Meneses Villegas, Paul Leger","doi":"10.1109/SCCC51225.2020.9281283","DOIUrl":"https://doi.org/10.1109/SCCC51225.2020.9281283","url":null,"abstract":"Android mobile systems are currently the main target of malware attacks. In this sense, machine learning is a suitable approach to analyze network traffic, and it generally achieves good results in the identification and detection of malware. However, an underlying problem is creating a dataset with network characteristics that accurately reflect the malwareś behavior. Characterizing adequately the dataset is a relevant process to identify malware with high precision when using traditional machine learning algorithms. This paper compares empirically three supervised machine learning algorithms, in order to identify ransomware traffic based on Android mobile network traffic features. We consider 9 features related to time properties of flows and bidirectional packets in 10 families of ransomware and different benign application Android network traffic. Empirical results show that Random Forest (RF) achieved a 96% accuracy in classifying ransomware, higher than Decision Tree (DT) and K-Nearest Neighbor (KNN) approaches. We conclude that the selected features allow us to identify ransomware traffic and differentiate it from the traffic of benign applications.","PeriodicalId":117157,"journal":{"name":"2020 39th International Conference of the Chilean Computer Science Society (SCCC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132294105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-16DOI: 10.1109/SCCC51225.2020.9281277
Manuel J. Ibarra, E. Alcarraz, Olivia Tapia, Y. P. Atencio, Yonatan Mamani-Coaquira, Herwin Alayn Huillcen-Baca
The significant decrease in agricultural land and the rapid development of hydroponic system technology have brought a huge challenge to farmers. This paper describes the NFT-I (Nutrient Film Technique based on IoT) hydroponic system, and it is a variant of traditional NFT and Floating Root (RF) systems. The system measures several parameters, such as temperature, water level, and acidity (pH). The system collects the information using sensors connected to Arduino microcontroller and Raspberry PI to store the collected data. The results show that this system can reduce the electricity consumption by 91.6%; on the other hand, it helps farmers to increase the effectivity and efficiency on monitoring and controlling NFT-I Hydroponic Farm. Finally, in these times of confinement due to coronavirus disease (COVID-19), in which the economy has decreased, and the needs are multiple, this NFT-I system could help people to create their vegetable growing system quickly and cheaply.
{"title":"NFT-I technique using IoT to improve hydroponic cultivation of lettuce","authors":"Manuel J. Ibarra, E. Alcarraz, Olivia Tapia, Y. P. Atencio, Yonatan Mamani-Coaquira, Herwin Alayn Huillcen-Baca","doi":"10.1109/SCCC51225.2020.9281277","DOIUrl":"https://doi.org/10.1109/SCCC51225.2020.9281277","url":null,"abstract":"The significant decrease in agricultural land and the rapid development of hydroponic system technology have brought a huge challenge to farmers. This paper describes the NFT-I (Nutrient Film Technique based on IoT) hydroponic system, and it is a variant of traditional NFT and Floating Root (RF) systems. The system measures several parameters, such as temperature, water level, and acidity (pH). The system collects the information using sensors connected to Arduino microcontroller and Raspberry PI to store the collected data. The results show that this system can reduce the electricity consumption by 91.6%; on the other hand, it helps farmers to increase the effectivity and efficiency on monitoring and controlling NFT-I Hydroponic Farm. Finally, in these times of confinement due to coronavirus disease (COVID-19), in which the economy has decreased, and the needs are multiple, this NFT-I system could help people to create their vegetable growing system quickly and cheaply.","PeriodicalId":117157,"journal":{"name":"2020 39th International Conference of the Chilean Computer Science Society (SCCC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131713734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-16DOI: 10.1109/SCCC51225.2020.9281242
Ignacio Lincolao-Venegas, Julio Rojas-Mora
In this work, we implemented a CUDA parallelized simulated annealing algorithm to solve the student-school assignment problem in a highly segregated environment. The objective function optimized considered the average distance from the students to their assigned school, the socio-economic segregation via the dissimilarity index, and the cost of schools partially filled. Using data from the MINEDUC, the INE, and the Municipality of Temuco (Chile), we simulated the distribution of Temuco’s student population, solving its students’ assignment to the city’s schools (29853 students to 85 schools). The results obtained were better with a high number of block (simultaneous students exploring), and a low number of threads (simultaneous schools explored by these students) instantiated in the GPU. Algorithm execution time worsens with the number of blocks and the number of threads, although it remained below 1000 seconds in the worst and below 400 seconds in the best case. However, the algorithm achieves excellent results in reducing socio-economic segregation, taking it from a high level to almost making it disappear. We achieved this result, even with a reduction of the average distance from students to their assigned school.
{"title":"A centralized solution to the student-school assignment problem in segregated environments via a CUDA parallelized simulated annealing algorithm","authors":"Ignacio Lincolao-Venegas, Julio Rojas-Mora","doi":"10.1109/SCCC51225.2020.9281242","DOIUrl":"https://doi.org/10.1109/SCCC51225.2020.9281242","url":null,"abstract":"In this work, we implemented a CUDA parallelized simulated annealing algorithm to solve the student-school assignment problem in a highly segregated environment. The objective function optimized considered the average distance from the students to their assigned school, the socio-economic segregation via the dissimilarity index, and the cost of schools partially filled. Using data from the MINEDUC, the INE, and the Municipality of Temuco (Chile), we simulated the distribution of Temuco’s student population, solving its students’ assignment to the city’s schools (29853 students to 85 schools). The results obtained were better with a high number of block (simultaneous students exploring), and a low number of threads (simultaneous schools explored by these students) instantiated in the GPU. Algorithm execution time worsens with the number of blocks and the number of threads, although it remained below 1000 seconds in the worst and below 400 seconds in the best case. However, the algorithm achieves excellent results in reducing socio-economic segregation, taking it from a high level to almost making it disappear. We achieved this result, even with a reduction of the average distance from students to their assigned school.","PeriodicalId":117157,"journal":{"name":"2020 39th International Conference of the Chilean Computer Science Society (SCCC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131947542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-16DOI: 10.1109/SCCC51225.2020.9281154
A. C. Leal, Ingrid Lefiguala, Rodrigo Tralma, Sebastián González
The paper presents an experience of "Implementation of Innovation" in the Directorate of Analysis of a Regional University. The Innovation consists of a Data Lake storage system for data management and analysis. The architecture uses 3 zones to store data: the first zone, stores the raw data; the second, stores the processed data, which come from the first zone; and the third is the access zone, which has data that can be analyzed and used by data scientists and decision makers. The work describes the development of the proposal and the lessons learned.
{"title":"Data Lake architecture proposal for the Analysis Directorate of a Regional University","authors":"A. C. Leal, Ingrid Lefiguala, Rodrigo Tralma, Sebastián González","doi":"10.1109/SCCC51225.2020.9281154","DOIUrl":"https://doi.org/10.1109/SCCC51225.2020.9281154","url":null,"abstract":"The paper presents an experience of \"Implementation of Innovation\" in the Directorate of Analysis of a Regional University. The Innovation consists of a Data Lake storage system for data management and analysis. The architecture uses 3 zones to store data: the first zone, stores the raw data; the second, stores the processed data, which come from the first zone; and the third is the access zone, which has data that can be analyzed and used by data scientists and decision makers. The work describes the development of the proposal and the lessons learned.","PeriodicalId":117157,"journal":{"name":"2020 39th International Conference of the Chilean Computer Science Society (SCCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132306337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-16DOI: 10.1109/SCCC51225.2020.9281228
Guillermo Guevara Bermúdez, Adriana Tapia Barraza, C. González
This article presents a proposal and didactic experience of the use of information and communications technology in the classroom to strengthen teaching and learning processes of sixth grade geometry students in Chile. Initially, the background and reference framework that underlie the problem with current research are shown. Then the results of the diagnostics of elementary school teachers and their collaborative use of mathematics and technology are presented. All the above is presented as support to design the pedagogical proposal through dynamic worksheets and use of GeoGebra software. Finally, descriptive results of the implementation, impact and level of satisfaction are provided.
{"title":"Dynamic worksheets for learning and teaching geometry in primary school education","authors":"Guillermo Guevara Bermúdez, Adriana Tapia Barraza, C. González","doi":"10.1109/SCCC51225.2020.9281228","DOIUrl":"https://doi.org/10.1109/SCCC51225.2020.9281228","url":null,"abstract":"This article presents a proposal and didactic experience of the use of information and communications technology in the classroom to strengthen teaching and learning processes of sixth grade geometry students in Chile. Initially, the background and reference framework that underlie the problem with current research are shown. Then the results of the diagnostics of elementary school teachers and their collaborative use of mathematics and technology are presented. All the above is presented as support to design the pedagogical proposal through dynamic worksheets and use of GeoGebra software. Finally, descriptive results of the implementation, impact and level of satisfaction are provided.","PeriodicalId":117157,"journal":{"name":"2020 39th International Conference of the Chilean Computer Science Society (SCCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115800099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-16DOI: 10.1109/SCCC51225.2020.9281280
F. Robles, Jacqueline Köhler, Karen Hinrechsen, V. Araya, Luciano Hidalgo, J. Jara
Student dropout is a phenomenon that affects all higher education institutions in Chile, with costs for people, institutions and the State. The reported retention rate of first year students for all Chilean universities was of 75%. Despite the extensive research and the implementation of various models to identify dropout causes and risk groups, few of them have been carried out in the Chilean higher education context.Our work attempts to identify, using machine learning methods, the variables with highest predictive value for student dropout by the end of the first year of study, within a 6-year Informatics Engineering programme with a rather high dropout rate of 21.9% reported on 2018. In that regard, we use the data of 4 cohorts of students (2012-2016) enrolled at the programme, to feed a random forest feature selection process. We later build a decision tree using the identified relevant features, which we later test using data of the 2017-2018 cohorts of students.Despite the fact that the decision tree is over-fitted (97,21% training accuracy against 81.01% test accuracy), the process sheds light on the nature of the variables that determine whether or not a student remains at the end of their first year of study at the University. 6 of the identified factors are academic, and the remaining one is social-cultural.
{"title":"Using machine learning methods to identify significant variables for the prediction of first-year Informatics Engineering students dropout","authors":"F. Robles, Jacqueline Köhler, Karen Hinrechsen, V. Araya, Luciano Hidalgo, J. Jara","doi":"10.1109/SCCC51225.2020.9281280","DOIUrl":"https://doi.org/10.1109/SCCC51225.2020.9281280","url":null,"abstract":"Student dropout is a phenomenon that affects all higher education institutions in Chile, with costs for people, institutions and the State. The reported retention rate of first year students for all Chilean universities was of 75%. Despite the extensive research and the implementation of various models to identify dropout causes and risk groups, few of them have been carried out in the Chilean higher education context.Our work attempts to identify, using machine learning methods, the variables with highest predictive value for student dropout by the end of the first year of study, within a 6-year Informatics Engineering programme with a rather high dropout rate of 21.9% reported on 2018. In that regard, we use the data of 4 cohorts of students (2012-2016) enrolled at the programme, to feed a random forest feature selection process. We later build a decision tree using the identified relevant features, which we later test using data of the 2017-2018 cohorts of students.Despite the fact that the decision tree is over-fitted (97,21% training accuracy against 81.01% test accuracy), the process sheds light on the nature of the variables that determine whether or not a student remains at the end of their first year of study at the University. 6 of the identified factors are academic, and the remaining one is social-cultural.","PeriodicalId":117157,"journal":{"name":"2020 39th International Conference of the Chilean Computer Science Society (SCCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128122926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-16DOI: 10.1109/SCCC51225.2020.9281163
Luis A. Laurens Arredondo, Hugo Valdés Riquelme
M-learning is a pedagogical tool that involves the active and dominant presence of mobile technologies in the classroom, demonstrating its effectiveness in meaningful learning, which is why its implementation is increasingly common by university professors. The purpose of this study is to investigate the relationship between motivation and learning in university students through m-learning. The Google Science Journal mobile application was used by students to determine the magnitude and direction of the acceleration vector of a previously selected motor vehicle under the ARCS instructional model. The motivation of university students was measured with the Instructional Material Motivational Survey (IMMS) instrument, which was applied to a group of 15 students from the Civil Engineering career at the Universidad Católica del Maule. The reliability of the instrument was determined by Cronbach’s alpha, giving an overall value of 0.89. The results suggest that the implementation of m-learning in university classrooms was positively valued by the majority of the students surveyed, as well as an increase in the percentage of students who achieved the expected learning compared to previous versions of the course, where the proposed methodology was not implemented. Teachers provide a validated measurement model, as well as solid scientific references that aim to stimulate the use of m-learning, as it has been shown that its implementation favorably stimulates students, as well as their interest in learning and confidence in themselves.
移动学习是一种教学工具，它涉及到课堂上移动技术的积极和主导存在，展示了它在有意义学习中的有效性，这就是为什么它的实施越来越普遍。本研究的目的是探讨大学生在移动学习中学习动机与学习的关系。学生们使用谷歌Science Journal移动应用程序来确定在ARCS教学模型下先前选择的机动车辆的加速度矢量的大小和方向。使用教材动机调查(IMMS)工具测量大学生的动机，该工具应用于一组15名来自Católica del Maule大学土木工程专业的学生。仪器的可靠性由Cronbach 's alpha确定，总体值为0.89。结果表明，在大学课堂上实施移动学习得到了大多数被调查学生的积极评价，与之前的课程版本相比，实现预期学习的学生比例也有所增加，而之前的课程版本没有实施拟议的方法。教师提供了一个有效的测量模型，以及可靠的科学参考，旨在刺激移动学习的使用，因为已经表明，移动学习的实施有利于激发学生，以及他们对学习的兴趣和对自己的信心。
{"title":"Evaluation of University Students Motivation in Learning Kinematics Through M-Learning","authors":"Luis A. Laurens Arredondo, Hugo Valdés Riquelme","doi":"10.1109/SCCC51225.2020.9281163","DOIUrl":"https://doi.org/10.1109/SCCC51225.2020.9281163","url":null,"abstract":"M-learning is a pedagogical tool that involves the active and dominant presence of mobile technologies in the classroom, demonstrating its effectiveness in meaningful learning, which is why its implementation is increasingly common by university professors. The purpose of this study is to investigate the relationship between motivation and learning in university students through m-learning. The Google Science Journal mobile application was used by students to determine the magnitude and direction of the acceleration vector of a previously selected motor vehicle under the ARCS instructional model. The motivation of university students was measured with the Instructional Material Motivational Survey (IMMS) instrument, which was applied to a group of 15 students from the Civil Engineering career at the Universidad Católica del Maule. The reliability of the instrument was determined by Cronbach’s alpha, giving an overall value of 0.89. The results suggest that the implementation of m-learning in university classrooms was positively valued by the majority of the students surveyed, as well as an increase in the percentage of students who achieved the expected learning compared to previous versions of the course, where the proposed methodology was not implemented. Teachers provide a validated measurement model, as well as solid scientific references that aim to stimulate the use of m-learning, as it has been shown that its implementation favorably stimulates students, as well as their interest in learning and confidence in themselves.","PeriodicalId":117157,"journal":{"name":"2020 39th International Conference of the Chilean Computer Science Society (SCCC)","volume":"41 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126855480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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