A Research on Practical Teaching and Influencing Factors of College Student’s Performance in Chengdu, China

Main Article Content

Xu Teng

Abstract

Purpose: This study investigates the factors that influence the students’ performance of Chengdu higher vocational college students, which are determined by perceived usefulness, perceived ease of use, attitude, behavioral intention, social influence, students’ performance, and use behavior. Research design, data, and methodology: A 3-step sampling method was used to select 500 juniors from Sichuan Vocational College of Finance and Economics, Chengdu Polytechnic, and Chengdu Textile College. A questionnaire adapted from previous studies was used, which was tested for validity and reliability. Hypotheses were tested using confirmatory factor analysis and structural equation modeling. Results: The results show that perceived usefulness significantly influences the attitude of students to participate in practical teaching. Behavior intention and use behavior are influenced by perceived ease of use, usefulness, attitude, social influence, and students’ performance. Furthermore, perceived ease of use, perceived usefulness, attitude, social influence, behavior intention, use behavior significantly influence students’ performance. Conclusions: More active participation attitude, a higher sense of identity in practical teaching, a better understanding of the usefulness and ease of use of practical instruction, a higher social impact, and better student performance are all related to use behavior of students to participate in practical teaching.

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Teng, X. (2024). A Research on Practical Teaching and Influencing Factors of College Student’s Performance in Chengdu, China. AU-GSB E-JOURNAL, 17(1), 181-190. https://doi.org/10.14456/augsbejr.2024.18
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Articles
Author Biography

Xu Teng

Sichuan Vocational College of Finance and Economics China.

References

Abbasi, M. S., Chandio, F. H., Soomro, A. F., & Shah, F. (2011). Social influence, voluntariness, experience, and the internet acceptance: an extension of technology acceptance model within a south-Asian country context. Journal of Enterprise Information Management, 24(1), 30-52.

Abeer, B. E. D. A., & Elaraby, I. S. (2014). Data Mining: A prediction for Student's Performance Using Classification Method. World Journal of Computer Application and Technology, 2(2), 43-47. https://doi.org/10.13189/wjcat.2014.020203

Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior (1st ed.). Prentice-Hall.

Alkhadim, M., Gidado, K., & Painting, N. (2019). Perceived crowd safety in large space buildings: The confirmatory factor analysis of perceived risk variables. Journal of Engineering, Project, and Production Management, 8(1), 22-39. https://doi.org/10.32738/jeppm.201801.0004

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of The Technology Acceptance Model in Context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 268-273. https://doi.org/10.5901/mjss.2015.v6n4s1p268

Amerstorfer, C. M., & Freiin von Münster-Kistner, C. (2021). Student Perceptions of Academic Engagement and Student-Teacher Relationships in Problem-Based Learning. Frontiers in Psychology, 12, 713057. https://doi.org/10.3389/fpsyg.2021.713057

Amos, C., Holmes, G., & Strutton, D. (2008). Exploring the relationship between celebrity endorser effects and advertising effectiveness: a quantitative synthesis of effect size. International Journal of Advertising, Routledge, 27(2), 209-234. https://doi.org/10.1080/02650487.2008.11073052

Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion. MIS Quarterly, 33(2), 339-370.

Awang, Z. (2012). Structural equation modelling using AMOS graphic (5th ed.). Penerbit Universiti Teknologi MARA.

Bagchi, A. K. P. (2005). Growth and Structural Change in the Economy of Gujarat (1970-71 to 2000-01). Economic and Political Weekly, 40(28), 3039-3047.

Bashir, I., & Madhavaiah, C. (2015). Consumer Attitude and Behavioral Intention Towards Internet Banking Adoption in India. Journal of Indian Business Research, 7(1), 67-102. https://doi.org/10.1108/jibr-02-2014-0013

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238.

Bhattacharjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: an elaboration likelihood model. MIS Quarterly, 3(4), 805-825. https://doi.org/10.2307/25148755

Celik, H. (2008). What determines Turkish consumers’ acceptance of Internet banking? International Journal of Bank Marketing, 26(5), 353-370.

Chau, V. S., & Ngai, L. W. L. C. (2010). The youth market for Internet banking services: perceptions, attitude, and behavior. Journal of Services Marketing, 24(1), 42-60. https://doi.org/10.1108/08876041011017880

Cheng, T. C. E., Lam, D. Y. C., & Yeung, A. C. L. (2006). Adoption of Internet banking: an empirical study in Hong Kong. Decision Support Systems, 42(3), 1558-1572. https://doi.org/10.1016/j.dss.2006.01.002

Chiou, J.-S., & Shen, C.-C. (2012). The antecedents of online financial service acceptance: the impact of physical banking services on Internet banking acceptance. Behavior and Information Technology, 31(9), 859-871. https://doi.org/10.1080/0144929x.2010.549509

Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265-284. https://doi.org/10.1016/j.elerap.2015.07.006

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Davis, F. D. (1993). User accepta https://doi.org/10.2307/249008nce of information technology: system characteristics, perceptions, and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487. https://doi.org/10.1006/imms.1993.1022

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22(14), 111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104

Guo, L., Huang, J., & Zhang, Y. (2019). Education Development in China: Education Return, Quality, and Equity. Sustainability, 11(13), 3750. from http://dx.doi.org/10.3390/su11133750

Haubl, G. (1996). A cross-national investigation of the effects of country of origin and brand name on the evaluation of a new car. International Marketing Review, 13(5), 76-97.

Hill, N. E., & Tyson, D. F. (2009). Parental Involvement in Middle School: A Meta-Analytic Assessment of the Strategies That Promote Achievement. Developmental Psychology, 45, 740-763. http://dx.doi.org/10.1037/a0015362

Hong, W., Thong, J. Y. L., Wong, W. M., & Tam, K.-Y. (2002). Determinants of user acceptance of digital libraries: an empirical examination of individual differences and system characteristics. Journal of Management Information Systems, 18(3), 97-124. https://doi.org/10.1080/07421222.2002.11045692

Hu, T., Zhang, D., & Wang, J. (2015). A Meta-Analysis of the Trait Resilience and Mental Health. Personality and Individual Differences, 76, 18-27. https://doi.org/10.1016/j.paid.2014.11.039

Hua, G., & Haughton, D. (2009). Virtual worlds adoption: a research framework and empirical study. Online Information Review, 33(5), 889-900. https://doi.org/10.1108/14684520911001891

Hung, M. C., Hwang, H. G., & Hsieh, T. C. (2007). An exploratory study on the continuance of mobile commerce: an extended expectation-confirmation model of information system use. International Journal of Mobile Communications, 5(4), 409-422. https://doi.org/10.1504/ijmc.2007.012788

Igbaria, M., Iivari, J., & Maragah, H. (1995). Why do individuals use computer technology? A Finnish case studies. Information & Management, 29(5), 227-238. https://doi.org/10.1016/0378-7206(95)00031-0

Kelman, H. (1958). Compliance, identification, and internalization: three processes of attitude Change. Journal of Conflict Resolution, 1(1), 51-60. https://doi.org/10.1177/002200275800200106

Kim, S., Kim, D., Son, C., & Kim, K.-S. (2015). A full engine cycle analysis of a turbofan engine for optimum scheduling of variable guide vanes. Aerospace Science and Technology, 47, 21-30.

Klobas, J. E. (1995). Beyond information quality: fitness for purpose and electronic information resource use. Journal of Information Science, 21(2), 95-114. https://doi.org/10.1177/016555159502100204

Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29, 269-282. https://doi.org/10.1016/s0167-9236(00)00076-2

Lee, M. C. (2009). Factors influencing the adoption of Internet banking: an integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130-141. https://doi.org/10.1016/j.elerap.2008.11.006

Lin, H. (2013). The effect of absorptive capacity perceptions on the context-aware ubiquitous learning acceptance. Campus-Wide Information Systems, 30(4), 249-265. https://doi.org/10.1108/cwis-09-2012-0031

Lin, H. F. (2009). Examination of cognitive absorption influencing the intention to use a virtual Community. Behavior & Information Technology, 28(5), 421-431. https://doi.org/10.1080/01449290701662169

Lin, Y. M., & Shih, D. H. (2008). Deconstructing mobile commerce service with continuance Intention. International Journal of Mobile Communications, 6(1), 67-87. https://doi.org/10.1504/ijmc.2008.016000

Loke, Y. K. (2008). Meta-analysis: gastrointestinal bleeding due to interaction between selective serotonin uptake inhibitors and non-steroidal anti-inflammatory drugs. Alimentary Pharmacology & Therapeutics, 27(1), 31-40.

Mallat, N., Rossi, M., Tuunainen, V. K., & Öörni, A. (2008). An empirical investigation of mobile ticketing service adoption in public transportation. Pers Ubiquit Comput, 12, 57-65. https://doi.org/10.1007/s00779-006-0126-z

Moghavvemi, S., & Salleh, N. A. M. (2014). Effect of precipitating events on information system adoption and use behaviour. European Journal of Marketing, 27(5), 599-622. https://doi.org/10.1108/jeim-11-2012-0079

Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230. https://doi.org/10.1016/S0378- 7206(00)00061-6.

Moradi, K., & Sabeti, G. (2014). A Comparison of EFL Teachers and EFL Students’ Understandings of ‘Highly Effective Teaching. Procedia - Social and Behavioral Sciences, 98, 1204-1213. https://doi.org/10.1016/j.sbspro.2014.03.535

Mtebe, J., & Raisamo, R. (2014). Challenges And Instructors’ Intention to Adopt and Use Open Educational Resources in Higher Education in Tanzania. International Review of Research in Open and Distance Learning, 15(1). 249-271. https://doi.org/10.19173/irrodl.v15i1.1687

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.), McGraw-Hill.

Parker, R., Thomsen, B. S., & Berry, A. (2022). Learning Through Play at School – A Framework for Policy and Practice. Frontiers in Education, 7, 751801. https://doi.org/10.3389/feduc.2022.751801

Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081

Perea, T., Benedict, M., Dellaert, G. C., & de Ruyter, K. (2004). What drives consumers to shop online? A literature reviews. International Journal of Service Industry Management, 5(1), 102-121. https://doi.org/10.1108/09564230410523358

Sarmento, R., & Costa, V. (2016). Comparative Approaches to Using R and Python for Statistical Data Analysis (1st ed.). IGI Global Press.

Shapiro, L., & Stolz, S. A. (2019). Embodied cognition and its significance for education. Theory and Research in Education, 17(1), 19–39. https://doi.org/10.1177/1477878518822149

Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modelling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282–286. https://doi.org/10.1016/j.jfoodeng.2005.02.010

Shu, W., & Chuang, Y. (2011). The behaviour of Wiki users. Social Behaviour and Personality, 39(6), 851-864.

Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading-Edge Psychological Tests and Testing Research (pp. 27-50). New York: Nova.

Silvera, D. H., & Austad, B. (2004). Factors predicting the effectiveness of celebrity endorsement Advertisements. European Journal of Marketing, Emerald Group Publishing, 38(11/12), 1509-1526. https://doi.org/10.1108/03090560410560218

Stoian, C. E., Fărcașiu, M. A., Dragomir, G.-M., & Gherheș, V. (2022). Transition from Online to Face-to-Face Education after COVID-19: The Benefits of Online Education from Students’ Perspective. Sustainability, 14(19), 12812. https://doi.org/10.3390/su141912812

Straub, E. (2009). Understanding Technology Adoption: Theory and Future Directions for Informal Learning. Review of Educational Research, 79, 625-649.https://doi.org/10.3102/0034654308325896

Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in Internet Usage. Omega, 27(1), 25-37. https://doi.org/10.1016/s0305-0483(98)00028-0

Teo, T. S. H., & Pok, S. H. (2003). Adoption of WAP-enabled mobile phones among internet users. Omega, 31(6), 483-498. https://doi.org/10.1016/j.omega.2003.08.005

Ukut, I., & Krairit, D. (2019). Justifying students’ performance, A comparative study of ICT students’ and instructors’ perspectives. Interactive Technology and Smart Education, 16(1), 18-35.

Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704. https://doi.org/10.2307/25148660

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540

Vululleh, P. (2018). Determinants Of Students’ E-Learning Acceptance in Developing Countries: An Approach Based on Structural Equation Modeling (SEM). International Journal of Education and Development Using Information and Communication Technology, 14(1), 141-151.

Wang, Y.-S., Wang, Y.-M., Lin, H.-H., & Tang, T.-I. (2003). Determinants of user acceptance of Internet banking: an empirical study. International Journal of Service Industry Management, 14(5), 501-519. https://doi.org/10.1108/09564230310500192

Wu, J.-H., & Wang, Y.-M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728–739. https://doi.org/10.1016/j.im.2006.05.002