RESEARCH PAPER
A Study of the Effect of Implementing Intellectual Property Education with Digital Teaching on Learning Motivation and Achievements
More details
Hide details
1
Law School, Chongqing University, Chongqing, CHINA
2
Director of Intellectual Property Research Center for Collaborative Innovation of Chongqing, Chongqing, CHINA
Online publication date: 2018-04-10
Publication date: 2018-04-10
EURASIA J. Math., Sci Tech. Ed 2018;14(6):2445-2452
KEYWORDS
TOPICS
ABSTRACT
The emergence of e-learning created new education and diverse environment, conforming to the rapid change in modern society. The high acquisition characteristic breaks through the restrictions to time and space of traditional teaching, and the international emphasis on the problem and development of intellectual property is thoroughly presented on various international conferences and international conventions. The practice on education promotion could enhance the understanding of intellectual property and present the mission to practice intellectual property law, i.e. effectively transforming learners to further enhance the concept of intellectual property. Taking a university in Guangxi as the research object, total 198 students in four classes are proceeded the 16-week (3 hours per week for total 48 hours) experimental teaching study. The research results conclude the effects of 1.Digital Teaching on motivation to learn, 2.Digital Teaching on learning outcome, 3.motivation to learn on learning effect in learning outcome, and 4.motivation to learn on learning gain in learning outcome. According to the research results, suggestions are proposed, expecting to cultivate students understanding the full chain of intellectual property and realizing the property and legal norms behind intellectual property problems and the applicable approaches.
REFERENCES (27)
1.
Agarwal, B., & Mittal, N. (2014). Text classification using machine learning methods-a survey. In Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012 (pp. 701-709). Springer, New Delhi.
https://doi.org/10.1007/978-81....
2.
Alickovic, E., & Subasi, A. (2016). Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier. Journal of medical systems, 40(4), 1.
https://doi.org/10.1007/s10916....
3.
Atenas, J., & Havemann, L. (2013). Quality assurance in the open: an evaluation of OER repositories. INNOQUAL-International Journal for Innovation and Quality in Learning, 1(2), 22-34.
4.
Atenas, J., & Havemann, L. (2014). Questions of quality in repositories of open educational resources: a literature review. Research in Learning Technology, 22(1), 20889.
https://doi.org/10.3402/rlt.v2....
5.
Bartholomew, S. (2015). My journey with self-directed learning. Techniques: Connecting Education & Careers, 90(2), 46-50.
6.
Cai, S., Wang, X., & Chiang, F. K. (2014). A case study of Augmented Reality simulation system application in a chemistry course. Computers in Human Behavior, 37, 31–40.
https://doi.org/10.1016/j.chb.....
7.
Clark, R. C., & Mayer, R. E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. John Wiley & Sons.
https://doi.org/10.1002/978111....
8.
Conejeros, A. L., & Mansilla, C. B. (2014). Evaluation of a rural self-learning English program in Chile. Enjoy Teaching Journal, 2(2).
10.
Huang, Y. H., & Chuang, T. Y. (2016). Technology-assisted sheltered instruction: instructional streaming video in an EFL multi-purpose computer course. Computer Assisted Language Learning, 29(3), 618-637.
https://doi.org/10.1080/095882....
11.
Ibáñez, M., Serio, Á. D., Villarán, D., & Kloos, C. D. (2014). Experimenting with electromagnetism using augmented reality: Impact on flow student experience and educational effectiveness. Computers & Education, 71, 1–13.
https://doi.org/10.1016/j.comp....
12.
Jin, X., Zhao, M., Chow, T. W., & Pecht, M. (2014). Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE Transactions on Industrial Electronics, 61(5), 2441-2451.
https://doi.org/10.1109/TIE.20....
13.
Jude, L. T., Kajura, M. A., & Birevu, M. P. (2014). Adoption of the SAMR Model to Asses ICT Pedagogical Adoption: A Case of Makerere University. International Journal of e-Education, e-Business, e-Management and e-Learning, 4(2), 106-115.
https://doi.org/10.7763/IJEEEE....
14.
Khalid, S., Khalil, T., & Nasreen, S. (2014). A survey of feature selection and feature extraction techniques in machine learning. In Science and Information Conference (SAI), 2014 (pp. 372-378).
https://doi.org/10.1109/SAI.20....
15.
Lee, L. C., & Hao, K. C. (2015). Designing and Evaluating Digital Game-Based Learning with the ARCS Motivation Model, Humor, and Animation. International Journal of Technology and Human Interaction, 11(2), 80-95.
https://doi.org/10.4018/ijthi.....
16.
Maeng, U., & Lee S. M. (2015). EFL teachers’ behavior of using motivational strategies: The case of teaching in the Korean context. Teaching and Teacher Education, 46, 25–36.
https://doi.org/10.1016/j.tate....
17.
Molaee, Z., & Dortaj, F. (2015). Improving L2 Learning: An ARCS Instructional-motivational Approach. Procedia - Social and Behavioral Sciences, 171, 1214-1222.
https://doi.org/10.1016/j.sbsp....
18.
Mortara, M., Catalanoa, C. E., Bellotti, F., Fiucci, G., Houry-Panchetti, M., & Panagiotis, P. (2014). Learning cultural heritage by serious games. Journal of Cultural Heritage, 15(3), 318–325.
https://doi.org/10.1016/j.culh....
19.
Niknejad, S., & Rahbar, B. (2015). Enhancing EFL learners’ reading comprehension ability through multimedia-based visualization. Journal of Applied Linguistics and Language Research, 2(6), 119-127.
20.
Rawson, C. H., & McCool, M. A. (2014). Just Like All the Other Humans? Analyzing Images of Scientists in Children’s Trade Books. School Science and Mathematics, 114, 10-18.
https://doi.org/10.1111/ssm.12....
21.
Saelao, S., Tubsree, C., & Markwardt, R. A. (2016). The effect of online language learning on the English achievement of first-year undergraduate students. HRD JOURNAL, 6(2), 104-116.
22.
Sanjay, G. (2016). A Comparative Study on Face Recognition using Subspace Analysis. In International Conference on Computer Science and Technology Allies in Research-March (p. 82).
23.
Subasi, A., Alickovic, E., & Kevric, J. (2017). Diagnosis of Chronic Kidney Disease by Using Random Forest. In CMBEBIH 2017 (pp. 589-594). Springer, Singapore.
https://doi.org/10.1007/978-98....
24.
Surjono, H. D. (2015). The Effects of Multimedia and Learning Style on Student Achievement in Online Electronics Course. Turkish Online Journal of Educational Technology - TOJET, 14(1), 116-122.
25.
Uysal, A. K., & Gunal, S. (2014). The impact of preprocessing on text classification. Information Processing & Management, 50(1), 104-112.
https://doi.org/10.1016/j.ipm.....
26.
Valerie, C. B. (2015). Self-Directed Learning and Technology. Education Digest, 80(6), 42-44.
27.
Woo, J. C. (2014). Digital Game-Based Learning Supports Student Motivation, Cognitive Success, and Performance Outcomes. Journal of Educational Technology & Society, 17(3), 291–307.