A Study on the Effectiveness of College English Blended Learning Under MOOCs Philosophy in China

A Study on the Effectiveness of College English Blended Learning Under MOOCs Philosophy in China

Zhaohui Dai
DOI: 10.4018/978-1-7998-1622-5.ch011
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This study investigated the effectiveness of college English blended education under MOOCs philosophy in China. The findings suggest that many features of MOOCs philosophy are evident in college English blended education and eight factors influence effectiveness. Relationships of the influencing factors demonstrate that interactions and evaluations are highly influencing factors in autonomous learning and motivations exert high influences on autonomous learning. However, students have low motivation in interaction and evaluation, for they are more extrinsically than intrinsically motivated. And also, collaborative learning is the least influencing factor in the study. To motivate the students, great emphasis should be laid on interactions and evaluation in student's autonomous learning. Moreover, students' negative attitude towards autonomous learning hampers their adaptability to college English blended learning, and, as attitude and motivation are highly related, this deserves equal attention.
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China’s rapid economic development and increasing trades with the world, particularly the recent implementation of “The Belt and Road Initiative” policy, has made English learning unprecedentedly important in the country. The Chinese government has made it clear in the newly-launched the “Medium and long-term National Education Reforms and Development Plan (2010-2020)” that promoting educational reform and equality has become core tasks in English education and that great efforts should be made to the effective cohesion of information technology into curriculum in higher education.

College English is a compulsory course for non-English majors in China, undergraduate and graduate. Its objective is to foster students’ English language competence in an all-round way. It also aims to provide them with effective means for communications and lifelong learning strategies so that they can learn independently, collaboratively and creatively to continuously cultivate themselves key competencies for the 21st century even after graduation.

In the year 2004 and 2007, the national Ministry of Education issued “College English Curriculum Requirements” (“Requirements” hereafter), a guideline for English education, particularly for non-English majors. And in the year 2015, with the assistance of a pool of top educational experts nationwide, the national Ministry of Education issued another “Guidelines for College English Education” (“Guidelines” hereafter). It encourages deeper innovations in college English education, in particular, the adoption of blended learning and further application of Micro lecture and Massive Open Online Courses (MOOCs hereafter). Ever since the Requirements was issued in 2004, great changes in college English education have been made in curriculum design, teaching models, evaluation and teacher development (Zhao et al., 2014; Xu, 2015).

However, innovations of college English education in China is by no means easy. It meets with challenges, and the learning environment is perhaps the biggest one. It is generally accepted that English is a foreign language instead of a second language in China. Compared with their counterparts in L2 countries, such as Canada and Singapore, Chinese college English learners have comparatively limited language input outside the classroom. Surprisingly, a lot of key universities in China have been deducting the course credits of college English, mainly because, as surveys show, students are becoming increasingly discontented with what they have learned from the course (Cai, 2011; Wang, 2009; Fen, 2010; He et al., 2012). And the rise of MOOCs constitutes another challenge for college English education. By giving college English learners brand new online and offline blended learning experiences and interactions with peers and instructors in virtual learning communities, as well as giving them access to high quality learning resources from prestigious universities, MOOCs have paved a new way for language learning and pressed for re-innovation and further cohesion of information technology into the curriculum.

The educational effectiveness and improvement research develop rapidly in recent decades. It has three research areas, and the school effectiveness research centers upon describing factors that affect the learning outcomes of the students (Chapman et al., 2015). Different kinds of e-learning as MOOCs, crowdsourcing and social networks have made it necessary the identification and isolation of critical factors for educational effectiveness. College English blended learning, with combination of both online and offline education, seeks to improve the educational effectiveness and tackles the challenges facing e-learning effectiveness, i.e. students’ motivation, sense of isolation and impersonal (Montebello, 2018).

Key Terms in this Chapter

Endogenous Variables: A factor whose value is dependent on other variables in the system.

Exogenous Variables: A factor whose value is wholly independent from other variables in the system.

SEM Modelling: Abbreviation for structural equation modeling, which includes confirmatory factor analysis, confirmatory composite analysis, path analysis, etc. It is used in the social sciences because it can impute relationships between latent variables from observable variables.

College English Education: English education for non-English majors at tertiary level in China, also labeled as College English Teaching (CET).

Ubiquitous Learning: (Also u-Learning) Learning anytime, anywhere with the help of mobile devices and wireless networks.

Semi-Structured Interviews: A method of research often used in the social sciences, in which the interviewer has a general framework to be explored and is open to allow new ideas to be brought up during the interview.

AMOS: A statistical software and an added SPSS module, used for structural equation modeling, path analysis, and confirmatory factor analysis.

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