A Study on the Effect of Continuing Education with Digital Technology on Professional Growth and Job Satisfaction of Librarians
Kaijun Yu 1
Ruiyi Gong 1
Chunguo Jiang 2
Shanshan Hu 1
Longjie Sun 1
Yu-Zhou Luo 3  
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Library, Shanghai University of Medicine & Health Sciences, Shanghai, CHINA
Library, University of Shanghai for Science and Technology, Shanghai, CHINA
School of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, CHINA
Online publication date: 2018-05-16
Publication date: 2018-05-16
EURASIA J. Math., Sci Tech. Ed 2018;14(7):3285–3292
Digital technology and Internet have been combined with modern people’s life, and the combination of bit technology with network communication systems largely change people’s lifestyles. Along with the popularity of education, the application of digital technology also enhances the basic changes of learning methods and learning contents. To provide quality service, professional librarians are necessary for a library. Accordingly, librarians are the core management element of a library. In the changeable technology era, librarians need constant learning for self-growth through continuing education in order to cope with the changeable environment. Taking librarians of Shanghai University of Medicine & Health Sciences as the research object, the librarians are proceeded continuing education with digital technology, and the questionnaire is distributed and collected on-site after the continuing education. The research results show that 1.continuing education would significantly and positively affect professional growth, 2.professional growth would remarkably and positively affect job satisfaction, and 3.continuing education would notably and positively affect job satisfaction. With such results, suggestions are proposed, expecting to apply the professional curricula of continuing education with digital technology for the continuous growth of librarians in the changeable era, satisfying the enhancement of core competencies to cope with reader needs under the time and technology changes, and further promoting domestic librarians’ professional competence and the development of library business.
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.
Aldhafri, S., Alkharusi, H., & Al Ismaili, A. (2015). Predicting English test anxiety: How memorization and critical thinking function? Theory and Practice in Language Studies, 5(6), 1159-1165.
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.
Atenas, J., & Havemann, L. (2014). Questions of quality in repositories of open educational resources: a literature review. Research in Learning Technology, 22(1), 20889.
Bourgonjon, J., Grove, F., Smet, C., Van Looy, J., Soetaert, R., & Valcke, M. (2013).Acceptance of game-based learning by secondary school teachers. Computers & Education, 67, 21-35.
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.
Chen, C.-M., Tan, C.-C., & Lo, B.-J. (2016). Facilitating English-language learners’ oral reading fluency with digital pen technology. Interactive Learning Environments, 24(1), 96-118.
Chiu, H. C. (2014). The Role of Digital Game-Based Learning in University Student’s English Learning Strategy, Intrinsic Motivation, Self-Efficacy, Cognitive Load, and Academic Performance.
Di Serio, Á., Ibáñez, M. B., & Kloos, C. D. (2013). Impact of an augmented reality system on students’ motivation for a visual art course. Computers & Education, 68, 586-596.
Ghorbandordinejad, F., & Ahmadabad, R. M. (2016). Examination of the relationship between autonomy and English achievement as mediated by foreign language classroom anxiety. Journal of Psycholinguistic Research, 45(3), 739-752.
Hsu, H. Y. (2014). Development and Evaluation of an Instructional Role-playing Game for History Instruction that Integrates Situated Scenarios and Problem-solving Tasks: an Analysis of Acceptance, Flow, Learning Achievement, and Sense of Place.
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.
Izenman, A. J. (2013). Linear discriminant analysis. In Modern multivariate statistical techniques (pp. 237-280). Springer New York.
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.
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). IEEE.
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.
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.
Manek, A. S., Shenoy, P. D., Mohan, M. C., & Venugopal, K. R. (2017). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World wide web, 20(2), 135-154.
MichelaMortara, 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.
Molaee, Z., & Dortaj, F. (2015). Improving L2 Learning: An ARCS Instructional-motivational Approach. Procedia - Social and Behavioral Sciences, 171, 1214-1222.
Qi, Z., Tian, Y., & Shi, Y. (2013). Robust twin support vector machine for pattern classification. Pattern Recognition, 46(1), 305-316.
Racelis, A. (2018). Library Services for the Poor: Theoretical Framework for Library Social Responsibility. Pedagogical Research, 3(2), 06.
Rambli, D., Matcha, W., & Sulaiman, S. (2013). Fun Learning with AR Alphabet Book for Preschool Children. International Conference on Virtual and Augmented Reality in Education. Procedia Computer Science, 25, 211–219.
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).
Seri, A., Ibáñez, M., & Kloos, C. (2013). Impact of an augmented reality system on students’ motivation for a visual art course. Computers & Education, 68, 586-696.
Subasi, A., Alickovic, E., & Kevric, J. (2017). Diagnosis of Chronic Kidney Disease by Using Random Forest. In CMBEBIH 2017 (pp. 589-594). Springer, Singapore.
Tang, L.-Y. (2016). Formative assessment in oral English classroom and alleviation of speaking apprehension. Theory and Practice in Language Studies, 6(4), 751-756.
Uysal, A. K., & Gunal, S. (2014). The impact of preprocessing on text classification. Information Processing & Management, 50(1), 104-112.
Wang, W., & Han, M. (2015). Quantitative analysis of the speech of the teachers and students in high school English classroom-based on information technology-based interaction analysis system. Theory and Practice in Language Studies, 5(10), 2107-2111.
Woo, J. C. (2014). Digital Game-Based Learning Supports Student Motivation, Cognitive Success, and Performance Outcomes. Educational Technology & Society, 17(3), 291–307.
Young, S.-C., & Wang, Y.-H. (2014). The game embedded CALL system to facilitate English vocabulary acquisition and pronunciation. Journal of Educational Technology & Society, 17(3), 239-251.