Transcription
CONTEMPORARY EDUCATIONAL TECHNOLOGYISSN: 1309-517X (Online)2021, 13(1), ep286, https://doi.org/10.30935/cedtech/8711Research ArticleOPEN ACCESSUse of the Collaborative Wall to Improve the Teaching-LearningConditions in the Bachelor of Visual ArtsRicardo-Adán Salas-RuedaInstitute of Applied Sciences and Technology, National Autonomous University of Mexico, MexicoORCID: 0000-0002-4188-4610Jesús Ramírez-OrtegaInstitute of Applied Sciences and Technology, National Autonomous University of Mexico, MexicoORCID: 0000-0002-4538-9203Ana-Libia Eslava-CervantesInstitute of Applied Sciences and Technology, National Autonomous University of Mexico, MexicoORCID: 0000-0002-7420-3412Received: 25 Jul 2020Accepted: 4 Sep 2020AbstractThis mixed research analyzes the use of the Collaborative Wall to improve the teaching-learningconditions in the Bachelor of Visual Arts considering data science and machine learning (linear regression).The sample is made up of 46 students who took the Geometric Representation Systems course at theNational Autonomous University of Mexico (UNAM) during the 2019 school year. The Collaborative Wallis a web application that facilitates the organization and dissemination of ideas through the use of imagesand text. In the classroom, the students formed teams and used mobile devices to access this webapplication. The results of machine learning indicate that the organization of ideas in the CollaborativeWall positively influences the participation of students, motivation and learning process. Data scienceidentifies 3 predictive models about the use of this web application in the educational field. Also, theCollaborative Wall facilitates the learning process in the classroom through the comparison and discussionof information. Finally, technological advances allow organizing creative activities that favor the activerole of students.Keywords: collaborative wall, bachelor, technology, learning, data science, teachingINTRODUCTIONTechnological advances are causing the creation of new school activities inside and outside the classroom(Costantino, 2018; Mannathoko & Mamvuto, 2018; Pavlou, 2020). As a result, teachers use technologicaltools, educational platforms and web applications to facilitate the participation of students at any time(Marshalsey & Sclater, 2019; Tadayonifar & Entezari, 2020; Wilks, Cutcher, & Wilks, 2015).During the 21st century, teachers are transforming the educational context with the support of technology(Salas-Rueda, 2020; Wargo, 2020; Wet, 2017). These changes are causing that students acquire a leading roleduring the learning process (Gonzalez et al., 2020; Kite et al., 2020; Strycker, 2020).Today, the roles of students and teachers are changing due to the emergence of new technological tools(Gopalan, Fentem, & Rever, 2020; Kerr & Lawson, 2020; Marshalsey & Sclater, 2019). Therefore, studentsCopyright 2021 by the authors; licensee CEDTECH by Bastas. This articles is published under the terms of theCreative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
Salas-Rueda et al. / Contemporary Educational Technology, 2021, 13(1), ep286have the possibility to consult the information, use the educational platforms and review the course contentsfrom anywhere (Brown & Fridman, 2020; Chen et al., 2020; Rathner & Schier, 2020).Information and Communication Technology (ICT) has improved the teaching-learning conditions in theFaculties of Arts and Design through the use of mobile devices (Souleles et al., 2017), Learning ManagementSystem (Strycker, 2020), reality augmented (Kerr & Lawson, 2020), technology applications (Brown &Fridman, 2020; Marshalsey & Sclater, 2019) and social networks (Marshalsey & Sclater, 2019).This mixed research proposes the use of the Collaborative Wall to improve the teaching-learning conditionsin the Bachelor of Visual Arts. The Collaborative Wall is a web application that facilitates the organization anddissemination of ideas through the use of images and text. Therefore, the research questions are: What is the impact of the Collaborative Wall in the participation of the students, motivation and learningprocess? What are the predictive models about the use of the Collaborative Wall in the educational fieldconsidering the characteristics of the students? What is the students’ perception about the use of the Collaborative Wall in the Geometric RepresentationSystems course?USE OF TECHNOLOGY IN THE FACULTIES OF ARTS AND DESIGNToday, educational institutions are using pedagogical models and technology to transform the teachinglearning process in the Faculties of Arts and Design (Costantino, 2018; Kerr & Lawson, 2020). For example,the use of Blended Learning (BL) and Augmented Reality (AR) allowed the construction of new educationalspaces that facilitate the teaching-learning process in the field of Design (Kerr & Lawson, 2020). In particular,the Master of Time application facilitated the active participation of the students inside and outside theclassroom, improved the academic performance about the issues of earth architecture and increased themotivation (Kerr & Lawson, 2020).At the College of Arts and Design, the incorporation of technology caused that students actively participateinside and outside the classroom (Souleles et al., 2017). In particular, the use of iPads facilitated theconsultation of information and allowed the access to educational platforms at any time (Souleles et al.,2017). Furthermore, the teachers of the Faculty of Arts and Design organized various student-centeredactivities such as discussion forums through these mobile devices (Souleles et al., 2017).In the Design course, the GFV application facilitated the collaborative work and developed the skills of thestudents (Brown & Fridman, 2020). Even this application favored the critical thinking and realization of thediscussion forums during the face-to-face sessions (Brown & Fridman, 2020). Technological advances arecausing substantial changes in the Faculty of Arts and Design (Costantino, 2018). For example, Science,Technology, Engineering, Arts and Mathematics (STEAM) are improving the teaching-learning conditionsthrough the active participation of the students before, during and after the face-to-face sessions(Costantino, 2018).In the Visual Arts course, the use of technology facilitated the assimilation of knowledge and developmentof skills (Wilks, Cutcher, & Wilks, 2015). In fact, the use of web applications and pedagogical modelstransformed the teaching-learning process on the Visual Arts (Wilks, Cutcher, & Wilks, 2015). In the field ofArts and Design, teachers use the Learning Management System (LMS) such as Moodle and Blackboard tofacilitate the assimilation of knowledge and develop the skills of the students (Strycker, 2020). Likewise, theincorporation of LMS in the school activities allows acquiring the knowledge at home in order to carry outthe collaborative activities during the face-to-face sessions (Strycker, 2020).In the Contemporary Communication Design course, the students acquired a leading role during therealization of the school activities through the use of social media and technology applications (Marshalsey& Sclater, 2019). In particular, Snapchat facilitated the communication and interaction between the2 / 10
Salas-Rueda et al. / Contemporary Educational Technology, 2021, 13(1), ep286Figure 1. Collaborative Wallparticipants of the educational process (Marshalsey & Sclater, 2019). Furthermore, the GoPro applicationfacilitated the active role of students through the creation of videos (Marshalsey & Sclater, 2019).Technology is transforming the school activities, functions and attitudes of students and teachers at theFaculties of Arts and Design (Marshalsey & Sclater, 2019; Strycker, 2020). Consequently, institutions have theopportunity to build new educational spaces with the support of digital tools (Brown & Fridman, 2020; Kerr& Lawson, 2020; Salas-Rueda, Salas-Rueda, & Salas-Rueda, 2020).METHODThis mixed research analyzes the use of the Collaborative Wall to improve the teaching-learning conditionsin the Bachelor of Visual Arts considering data science and machine learning (linear regression). The particularobjectives are (1) analyze the impact of the Collaborative Wall in the participation of the students, motivationand learning process, (2) establish the predictive models on the use of the Collaborative Wall in theGeometric Representation Systems course and (3) analyze the students’ perception about the use of theCollaborative Wall.ParticipantsThe sample is made up of 46 students (10 men and 36 women) who took the Geometric RepresentationSystems course at the National Autonomous University of Mexico (UNAM) during the 2019 school year. Theaverage age of the participants is 18.89 years.ProcedureThe teacher of the Geometric Representation Systems course took the “Classroom of the Future 2019”Diploma in order to improve the teaching-learning conditions through pedagogy and technology. Thisdiploma offers the use of the Collaborative Wall to promote the active role of students during the learningprocess. The Collaborative Wall is a web application that facilitates the organization and dissemination ofideas through the use of images and text (See Figure 1).In the Geometric Representation Systems course, the students searched for and selected the images aboutSpirals in Nature (fossils, plants and animals) before the face-to-face sessions. The use of technology in theclassroom was carried out during 3 sessions (Total hours: 6). In the face-to-face sessions, the students of theBachelor of Visual Arts formed teams (3 students) and used the SketchUp application to manipulate theimages about Spirals in Nature. Subsequently, the students used mobile devices to share and discuss theresults of the school activity in the Collaborative Wall.The research hypotheses on the use of the Collaborative Wall in the educational field are: Hypothesis 1 (H1): The organization of ideas in the Collaborative Wall positively influences theparticipation of the students3 / 10
Salas-Rueda et al. / Contemporary Educational Technology, 2021, 13(1), ep286Table 1. Questionnaire about the use of Collaborative WallNo.1VariableProfile of thestudentsDimensionSexAge2Collaborative WallOrganization ofideasParticipation ofthe studentsQuestion1. Indicate your sexAnswern%ManWoman103621.74%78.26%17 years18 years19 years20 years21 years22 years23 %Too much (1)Much (2)Little (3)Too little %28.26%19.57%13.04%2. Indicate your age3. The Collaborative Wallfacilitates the organization ofideas4. The use of the CollaborativeWall improves the participation Too much (1)of the studentsMuch (2)Little (3)Too little (4)Motivation of the 5. The use of the CollaborativestudentsWall increases the motivation of Too much (1)the studentsMuch (2)Little (3)Too little (4)Learning process 6. The use of the CollaborativeWall improves the learningToo much (1)processMuch (2)Little (3)Too little (4) Hypothesis 2 (H2): The organization of ideas in the Collaborative Wall positively influences the motivationof the students Hypothesis 3 (H3): The organization of ideas in the Collaborative Wall positively influences the learningprocessThe predictive models on the use of the Collaborative Wall in the Geometric Representation Systems courseare: Predictive Model 1 (PM1) on the organization of ideas in the Collaborative Wall and participation of thestudents Predictive Model 2 (PM2) on the organization of ideas in the Collaborative Wall and motivation of thestudents Predictive Model 3 (PM3) on the organization of ideas in the Collaborative Wall and learning processData CollectionTable 1 shows the questionnaire about the use of the Collaborative Wall in the Geometric RepresentationSystems course. Data collection was carried out at the National Autonomous University of Mexico (Facultyof Arts and Design) during the 2019 school year.Data AnalysisThis mixed research uses the Word Cloud app during the qualitative approach and the Rapidminer tool duringthe quantitative approach in order to calculate machine learning and data science. Machine learning allows4 / 10
Salas-Rueda et al. / Contemporary Educational Technology, 2021, 13(1), ep286Table 2. Results of machine learningHypothesisH1: Organization of ideas in theCollaborative Wall participation of the studentsH2: Organization of ideas in theCollaborative Wall motivation of the studentsH3: Organization of ideas in theCollaborative Wall Linear regressiony 0.124x 1.000y 0.208x 0.941y 0.218x 0.898y 0.337x 1.283y 0.292x 1.360y 0.343x 1.179y 0.405x 1.207y 0.489x 1.072y 0.531x 0.914ConclusionAccepted: 0.124Accepted: 0.208Accepted: 0.218Accepted: 0.337Accepted: 0.292Accepted: 0.343Accepted: 0.405Accepted: 0.489Accepted: 0.531Squared 002Table 3. Conditions of the PM1No.12345678Collaborative Wall organization of ideasToo muchMuchMuchLittleLittleToo littleToo littleToo littleSexManWomanManWoman-Age 19.5 years 19.5 years 18.5 years 18.5 years 18.5 yearsUse of the Collaborative Wall participationToo muchMuchToo muchToo littleToo muchMuchToo littleToo muchevaluating the research hypotheses about the use of the Collaborative Wall through the training section(50%, 60% and 70% of the sample). On the other hand, the evaluation section (50%, 40% and 30% of thesample) allows identifying the accuracy of the linear regressions. Likewise, data science allows building thepredictive models on the use of the Collaborative Wall in the educational field through the decision treetechnique.RESULTSThe Collaborative Wall facilitates too much (n 20, 43.48%), much (n 15, 32.61%), little (n 7, 15.22%) andtoo little (n 4, 8.70%) the organization of ideas (See Table 1). The results of machine learning indicate thatthe organization of ideas in the Collaborative Wall positively influences the participation of the students,motivation and learning process (See Table 2).According to the results of machine learning with 50% (0.405), 60% (0.489) and 70% (0.531) of training, thevariable that most influences the organization of ideas in the Collaborative Wall is the learning process.Participation of the StudentsThe use of the Collaborative Wall improves too much (n 31, 67.39%), much (n 6, 13.04%), little (n 4,8.70%) and too little (n 5, 10.87%) the participation of the students (See Table 1). The results of machinelearning with 50% (0.124), 60% (0.208) and 70% (0.218) of training indicate that H1 is accepted (See Table 2).Therefore, the organization of ideas in the Collaborative Wall positively influences the participation of thestudents.Table 3 shows the PM1 on the use of the Collaborative Wall. For example, if the student thinks that theCollaborative Wall facilitates much the organization of ideas and has an age 19.5 years then the use of theCollaborative Wall improves much the participation of the students.Also, Table 3 shows the 8 conditions of the PM1 with the accuracy of 78.26%. For example, if the studentthinks that the Collaborative Wall facilitates too much the organization of ideas then the use of theCollaborative Wall improves too much the participation of the students.Motivation of the StudentsThe use of the Collaborative Wall increases too much (n 24, 52.17%), much (n 11, 23.91%), little (n 4,8.70%) and too little (n 7, 15.22%) the motivation of the students (See Table 1). The results of machine5 / 10
Salas-Rueda et al. / Contemporary Educational Technology, 2021, 13(1), ep286Table 4. Conditions of the PM2No.1234567891011Collaborative Wall organization of ideasToo muchToo muchToo muchMuchMuchMuchLittleLittleLittleToo littleToo littleSexManWomanManWomanManWomanWoman-Age 17.5 years 17.5 years 17.5 years 19.5 years 19.5 years 19.5 years 19.5 years 19.5 years 18.5 years 18.5 yearsUse of the Collaborative Wall motivationToo muchToo littleMuchMuchToo littleMuchLittleToo littleMuchToo littleToo muchNo. Collaborative Wall organization of ideas1Too much2Too muchSex-Use of the Collaborative Wall learning processToo littleToo much345678Too Too littleToo little-Age 20 years 17.5 and 20years 17.5 years 17.5 years 17.5 years 19.5 years 19.5 years 19.5 and 18.5 years 18.5 years 18.5 years 18.5 yearsTable 5. Conditions of the PM3MuchMuchToo muchLittleToo littleMuchLittleMuchToo littlelearning with 50% (0.337), 60% (0.292) and 70% (0.343) of training indicate that H2 is accepted (See Table 2).Therefore, the organization of ideas in the Collaborative Wall positively influences the motivation of thestudents.Table 4 shows the PM2 on the use of the collaborative wall. For example, if the student thinks that theCollaborative Wall facilitates much the organization of ideas and has an age 19.5 years then the use of theCollaborative Wall increases much the motivation of the students.Also, Table 4 shows the 11 conditions of PM2 with an accuracy of 71.74%. For example, if the student thinksthat the Collaborative Wall facilitates too much the organization of ideas and has an age 17.5 years thenthe use of the Collaborative Wall increases too much the motivation of the students.Learning ProcessThe use of the Collaborative Wall improves too much (n 18, 39.13%), much (n 13, 28.26%), little (n 9,19.57%) and too little (n 6, 13.04%) the learning process (See Table 1). The results of machine learning with50% (0.405), 60% (0.489) and 70% (0.531) of training indicate that H3 is accepted (See Table 2). Therefore,the organization of ideas in the Collaborative Wall positively influences the learning process.Table 5 shows the PM3 about the use of the Collaborative Wall. For example, if the student thinks that theCollaborative Wall facilitates much the organization of ideas and has an age 17.5 years then the use of theCollaborative Wall improves much the learning process.Also, Table 5 shows the 11 conditions of the PM3 with the accuracy of 71.64%. For example, if the studentthinks that the Collaborative Wall facilitates much the organization of ideas and has an age 17.5 years thenthe use of the Collaborative Wall improves too much the learning process.6 / 10
Salas-Rueda et al. / Contemporary Educational Technology, 2021, 13(1), ep286Figure 2. Word cloud about the use of the Collaborative WallPerception of the StudentsThe use of the Collaborative Wall in the Geometric Representation Systems course improved the teachinglearning conditions. In fact, this web application allows the construction of new educational spaces:“It makes the class more entertaining” (Student 4, woman, 20 years).“It facilitates the work in the classroom” (Student 10, woman, 18 years).In the Bachelor of Visual Arts, the students mention that the use of the Collaborative Wall facilitates thelearning process in the classroom through the comparison and discussion of information.“Compare the ideas to reach a conclusion” (Student 8, woman, 18 years).“Compare the ideas and points of view” (Student 28, man, 18 years).Technological advances allow the creation of the school activities that favor the learning process. Accordingto the students, the use of the Collaborative Wall in the classroom was interesting and fun.“It is very interesting and fun. I like” (Student 32, woman, 18 years).“We reached agreements as teams, it was fun and useful” (Student 39, man, 22 years).Technology facilitates the organization and realization of student-centered activities. In particular, theincorporation of the Collaborative Wall in the school activities allows the active participation of students.“We collaborate in real time” (Student 5, man, 18 years).“Team participation” (Student 9, woman, 17 years).On the other hand, the word cloud about the use of the Collaborative Wall indicates that fun, learning, class,ideas, topic, dynamic and learn are the words that have the highest frequency (See Figure 2).DISCUSSIONThe incorporation of technology in the school activities allows improving the teaching-learning conditions. Inparticular, most of the students (n 20, 43.48%) think that the Collaborative Wall facilitates too much theorganization of ideas.7 / 10
Salas-Rueda et al. / Contemporary Educational Technology, 2021, 13(1), ep286Participation of the StudentsMost of the students (n 31, 67.39%) think that the use of the Collaborative Wall improves too much theparticipation of the students. The results of machine learning on H1 are greater than 0.120, therefore, theorganization of ideas in the Collaborative Wall positively influences the participation of students. On theother hand, data science identifies 8 conditions of the PM1 with an accuracy of 78.26%.Motivation of the StudentsMost of the students (n 24, 52.17%) think that the use of the Collaborative Wall increases too much themotivation of the students. The results of machine learning on H2 are greater than 0.290, therefore, theorganization of ideas in the Collaborative Wall positively influences the motivation of students. On the otherhand, data science identifies 11 conditions of the PM2 with an accuracy of 71.74%.Learning ProcessMost of the students (n 18, 39.13%) think that the use of the Collaborative Wall improves too much thelearning process. The results of machine learning on H3 are greater than 0.400, therefore, the organizationof ideas in the Collaborative Wall positively influences the learning process. On the other hand, data scienceidentifies 11 conditions of the PM3 with an accuracy of 71.64%.Perception of the StudentsThe incorporation of the Collaborative Wall in the Geometric Representation Systems course improved theteaching-learning conditions and allowed the construction of new educational spaces. In particular, this webapplication facilitated the comparison and discussion of information in the classroom. According to thestudents, the use of the Collaborative Wall in the classroom was interesting and fun. Also, this webapplication allows the active role of the participants during the learning process.CONCLUSIONThe incorporation of technology in the school activities is causing the realization of creative school activities.In particular, the Collaborative Wall transformed the roles of the participants during the educational processand facilitated the active role of the students in the Geometric Representation Systems course. The resultsof machine learning indicate that the organization of ideas in the Collaborative Wall positively influences theparticipation of the students, motivation and learning process. Also, data science identifies 3 predictivemodels of the use of this web application in the educational field.The limitations of this research are the use of the Collaborative Wall in the Bachelor of Visual Arts and size ofthe sample. Therefore, future research can analyze the impact on the use of this web application in variousdegrees such as Computer Science, Marketing, Medicine and Administration.Finally, teachers can organize and carry out creative school activities through the Collaborative Wall. In fact,this web application allows the construction of new educational spaces where the student has the main roleduring the learning process.ACKNOWLEDGEMENTSThe participation of the following academics is appreciated: PhD Clara Alvarado Zamorano, PhD Gustavo Dela Cruz Martínez, Master Ricardo Castañeda Martínez and Master Antonio M. Garcés Madrigal. Likewise, thesupport provided by the Faculty of Arts and Design is appreciated.8 / 10
Salas-Rueda et al. / Contemporary Educational Technology, 2021, 13(1), ep286REFERENCESBrown, K., & Fridman, I. (2020). Transforming Feedback: An Application Framework for Group FeedbackVideos in Design. International Journal of Art & Design Education, 39, 139-152.https://doi.org/10.1111/jade.12230Chen, J., Zhou, J.,Wang, Y., Qi, G., Xia, C., Mo, G., & Zhang, Z. (2020). Blended learning in basic medicallaboratory courses improves medical students’ abilities in self-learning, understanding, and problemsolving. Advances in Physiology Education, 44(1), 9-14. , T. (2018). STEAM by another name: Transdisciplinary practice in art and design education. ArtsEducation Policy Review, 119(2), 100-106. lez, A. A., Lizana, P. A., Pino, S., Miller, B. G., & Merino, C. (2020). Augmented reality-based learning forthe comprehension of cardiac physiology in undergraduate biomedical students. Advances inPhysiology Education, 44(3), 314-322. https://doi.org/10.1152/advan.00137.2019Gopalan, C., Fentem, A., & Rever, A. L. (2020). The refinement of flipped teaching implementation to 0143.2019Kerr, J., & Lawson, G. (2020). Augmented Reality in Design Education: Landscape Architecture Studies as e, J., Schlub, T. E., Zhang, Y., Choi, S., Craske, S., & Dickson, M. (2020). Exploring lecturer and studentperceptions and use of a learning management system in a postgraduate public health environment.E-Learning and Digital Media, 17(3), 183-198. , M. C., & Mamvuto, A. (2018). Learner involvement in art and design education assessment:The missing matrix in Botswana’s primary schools. Arts Education Policy Review, 119(3), 081Marshalsey, L., & Sclater, M. (2019). Arts‐Based Educational Research: The Challenges of Social Media andVideo‐Based Research Methods in Communication Design Education. International Journal of Art &Design Education, 38, 723-739. https://doi.org/10.1111/jade.12252Pavlou, V. (2020). Art Technology Integration: Digital Storytellying as a Transformative Pedagogy in 2254Rathner, J. A., & Schier, M. A. (2020). The impact of flipped classroom andragogy on student assessmentperformance and perception of learning experience in two advanced physiology subjects. Advances inPhysiology Education, 44(1), 80-92. a, R. A. (2020). Impact of the WampServer application in Blended learning considering datascience, machine learning, and neural networks. E-Learning and Digital Media, 17(3), las-Rueda, R. A., Salas-Rueda, E. P., & Salas-Rueda, R. D. (2020). Analysis and design of the web game ondescriptive statistics through the ADDIE model, data science and machine learning. InternationalJournal of Education in Mathematics, Science and Technology, 8(3), uleles, N., Savva, S., Watters, H. & Annesley, A. (2017). Perceptions of art and design faculty on theinstructional value of iPads. Technology, Pedagogy and Education, 26(2), 3909 / 10
Salas-Rueda et al. / Contemporary Educational Technology, 2021, 13(1), ep286Strycker, J. (2020). K-12 art teacher technology use and preparation. Heliyon, 6(7), Tadayonifar, M., & Entezari, M. (2020). Does flipped learning affect language skills and learning rgo, J. M. (2020). Sounding out synthesis: Investigating how educators in a teaching with technologycourse use sonic composition to remix reflection. E-Learning and Digital Media, 17(3), t, A. J. (2017). An educational tool to encourage higher level thinking skills in the selection of images forfashion design mood boards: an action research approach. International Journal of Fashion Design,Technology and Education, 10(1), 16-25. , J., Cutcher, A., & Wilks, S. (2015). Digital Technology in the Visual Arts Classroom: An [Un]EasyPartnership. Studies in Art Education, 54(1), 54-65. espondence: Ricardo-Adán Salas-Rueda, Institute of Applied Sciences and Technology, NationalAutonomous University of Mexico, Mexico. E-mail: [email protected] / 10
Faculties of Arts and Design through the use of mobile devices (Souleles et al., 2017), Learning Management System (Strycker, 2020), reality augmented (Kerr & Lawson, 2020), technology applications (Brown & Fridman, 2020; Marshalsey & Sclater, 20
