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In fact, motives and related psychological factors are given much attention and frequently referred to in international research of dropouts, of which the concepts of self-efficacy and cognitive load are closely related to what Bean & Metzner classified as psychological experience. The concept of self-efficacy proposed by the American psychologist Albert Bandura in late 1970’s refers to a person’’s belief in his ability to succeed, including expectations of results and efficacy. The “result” expectation refers to the individual’’s inference as to the possible results brought about by certain actions; the “efficacy” expectation refers to the individual’’s subjective judgment of their own ability to implement a certain action. The inference and judgment are called individual self-efficacy (Zhang Wenjie, 2006). Let us imagine a scenario to analyze the question, “How does self-efficacy influence school dropout?”: Motivated by learning desire for further education, Student A visits the enrollment venue. After coming to a primary understanding of the RTVU distance learning programme, he comes to the result expectation that “I can obtain knowledge and the corresponding education certificate after my study here”; and the efficacy expectation that “if convenient learner support is available anywhere at any time, I should be able to accommodate learning while working”. Nevertheless, if Student A lacks the required personal characteristics and skills listed in Table 1, and if the enrollment staff fail to notice and point out such deficiency, we can say that he is not “ready” for distance education. Once he begins his studies, his self-efficacy will change accordingly and gradually lead to a negative psychological experience.
Another concept influencing a student’’s psychological experience is the theory of cognitive load. In analyzing our ability to process information, cognitive psychology classifies memory into different categories, including working memory and long-term memory. Working memory is used to store information temporarily, and it is here that information is initially processed. Due to its limited capacity, only a small amount of information is processed at a time. This indicates that people have a limited capacity to process outside information in a given amount of time. This is what led John Sweller, an Australian cognitive psychologist, to propose the theory of cognitive load in the 1980’s — a study on the psychological capacity needed for the processing of a fixed amount of information. Sweller points out that as the amount of information to be processed increases, cognitive load increases accordingly. Cognitive load results from the difficulty of the material and the method of presentation. The former is decided by the subject matter itself, whereas the latter is decided by the way the information is presented. The two sources of cognitive load are cumulative, and when the sum surpasses the total information capacity of the learner, learning becomes very challenging (Lin Gang et al., 2007; Miao Shiwei, 2009). Consequently, when Student A begins his studies, he will soon face great cognitive pressure when studying specialized subjects because he is not “ready” to learn and has not yet adapted to the new style of learning. He begins to doubt his own ability and must re-evaluate his ability to complete his education. This negative psychological experience will lead to the rapid decrease of his self-efficacy, and without timely effective help or learner support at this stage, he may decide to drop out. A domestic study on the pattern of dropout timing for distance education dropouts also confirms the rationality of Student A’s action inference. “Just enrolled” and “at the end of the first term” represent the two main occasions for possible dropout (Li Ying et al., 2009). The hypothetical scenario of Student A shows not only the influence of negative psychological factors in a student’s decision to drop out of school, but also the rationality of Rovai’s and Park’s special emphasis on student skills when learners begin their distance education.
A great deal of international research on adult dropouts in traditional education using Bean & Metzner’s theory has shown that when students have negative psychological experiences, they will tend to quit schooling no matter how well they are learning. Therefore, the results of psychological experience play a key role in learners’ decisions to quit studying. Over 80% of RTVU learners are working adults, and the importance of psychological factors should be highlighted when analyzing causes of dropout. As such, the “Composite Attribution Analysis Framework of Distance Learner Dropouts” based on Rovai’s and Park’s research was re-analyzed as follows:
Figure 1 provides a framework to analyze the relationships and correlations between each factor in the complicated phenomenon of distance education dropout. It warns us that when starting to learn via distance education, learners should possess a basic readiness for study, that is to say, some threshold should be set for entrance. Secondly, the decision to drop out of school is a complicated psychological process which interacts with diverse factors, such as academic literacy, information literacy, the learners’ external environment prior to admission, and satisfaction with learner support provided by schools after admission. Third, internal factors under the control of the schools exert a significant influence on the learners’ psychological experience after entrance to the school. By relying on the dropout attribution analysis framework, the data will be classified and the listed key variables of dropout attribution will be analyzed so as to obtain an overall description of factors leading to dropout, to produce a path diagram for the decision to drop out, and to explore the underlying causes.
Research Methods
Undergraduate English majors entering a provincial RTVU in the spring of 2010 were chosen as the sample group for this study. Dropouts are defined as students who did not continue studies after the autumn 2010 term, including students who suspended schooling, changed programmes, or transferred out of the RTVU system. In line with the definition, there were 34 dropouts, evenly distributed among 9 branches of the university. The investigation began in October 2010 and ended in December of the same year, lasting 90 days. The research data was obtained in two ways. The first was online questionnaires and the second was interviews. Since the subjects of the study were students who dropped out of school, there was some difficulty in tracking them. To ensure the smooth collection of data, special groups were formed. Each school branch designated persons to be responsible for finding dropouts of their own school and having them fill in the online questionnaires, interviewing dropouts, instructors and class supervisors, as well as preparing transcripts of the interviews, all within the set time frame.
The questionnaires comprised 52 questions divided into 3 sections, whose contents involved the students’ basic personal information prior to admission, the dropouts’ learning behavior in school, and the dropouts’ own observations as to the cause of their quitting. This kind of information was collected via the Internet. Interviews were conducted with the dropouts, their class supervisors and course instructors. The outline used in the interviews was prepared by the entire research group and centered on the topics: “individual background”, “evaluation of the learning environment”, “reasons for dropping out”, and “suggestions on reducing dropouts”. Those who conducted the interviews were advised to be flexible as to the structure of the interviews in an effort to tap into valuable research data. The original plan had been to follow and interview each of the dropouts. However, during the course of implementation, the number of dropouts to be interviewed was adjusted to half of the original plan, i.e., at least 17 students. There was great difficulty in getting in touch with the dropouts, and some of them had already moved to other places to seek employment. It was the same with the instructor’s interviews. Because many of the RTVU instructors were postgraduates seeking master’s or doctor’s degrees in school, they also left for other cities for work after graduation, and it was difficult to follow them. The objective was to interview as many instructors as possible. Interviews with the class supervisors went well and all 9 accepted interviews. By the end of December 2010, 24 useful questionnaires had been collected, accounting for 68% of the target subjects; 18 dropouts were interviewed, accounting for 52% of the total dropouts; 9 class supervisors were interviewed, representing a proportion of 100%; 20 instructors were interviewed, over 50% of the total.
An integration of both quantitative and qualitative methods was adopted in the study to explore the causes of dropout. The data was analyzed in the following four categories using “Composite Attribution Analysis Framework of Distance Learner Dropouts”: 1) dropouts’ personal characteristics and basic skills prior to admission; 2) appearance of external factors; 3) appearance of internal factors; 4) learning experience and inference of their psychological experience. This paper presents a statistical analysis of the questionnaires collected via the Internet, and this quantitative data will be used to analyze and prove the key variables leading to dropout. The interviews were read and the contents analyzed. The data collected from the interviews was evaluated for relevant information, which was then classified and used to draw conclusions and support discoveries about certain factors related to the decision to drop out of school.