Massive Open Online Courses: History
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Subjects: Education & Educational Research | Computer Science, Information Systems
Contributor:
Olugbara O.O.

Massive open online courses (MOOCs) is generally recognized as one of the most recent developmental phases of open educational resources that have tremendously transformed higher education institutions and significantly minimized the spiraling costs of learning. It is reshaping the quality of teaching and learning experiences for students and it provides a wide diversity of high-quality courses and valuable learning materials for the diverse needs of students

  • multiple correspondences
  • online course
  • computer self-efficacy

1. Introduction

The technology of massive open online courses (MOOCs) has become a recent educational innovation for sustainable online education services that have gained widespread popularity across the world [1]. MOOCs have been described as online distance-learning courses that provide free educational resources to registered students in various disciplines [2]. MOOC is generally recognized as one of the most recent developmental phases of open educational resources that have tremendously transformed higher education institutions and significantly minimized the spiraling costs of learning. It is reshaping the quality of teaching and learning experiences for students and it provides a wide diversity of high-quality courses and valuable learning materials for the diverse needs of students [3]. It can motivate students for learning [4], allow for free sharing of learning materials [5], support interactivity with the aid of various communication tools and provide numerous opportunities for students to seamlessly collaborate [6]. The use of MOOCs can provide students with pervasive access to a diverse spectrum of learning resources, thereby promoting student-centered instructions [7]. MOOC can improve the quality of learning pedagogy, help accelerate collaboration, ensure social cohesion and promote sustainable development growth [8][9].
However, it comes naturally with some inherent challenges despite its rapid development and numerous intrinsic benefits. The challenges include lack of standardization and flexibility [10], incapacity to provide real-time feedback to students [5], inability to obtain sustainable financial revenue [6] and inadequate learning time for students [11]. In addition, it is difficult to implement a teaching process affording the specific characters of students using MOOCs because of a huge number of participants and the teacher cannot identify the characters of individual students through face-to-face interaction [12]. These challenges have contributed to a low degree of student participation after enrolment in MOOCs [1][13][14] and acceptance rates by students are universally low [15][16]. The average completion rate of MOOCs is less than ten percent and the dropout rate is generally very high [6]. The situation can lead to uncertainty regarding the efficacy, sustainability and performance of MOOC as a learning platform [7]. Moreover, scholars have ascribed the dropout rates of MOOCs to inadequate control of a learning environment, lack of background knowledge and skills, conflict in a discussion forum among students and the feeling of complete isolation [11][17]. However, there has been a contention that the success of MOOCs should not be solely evaluated by course completion and drop-out rates because students enroll for various motives. They may, for instance, enroll to satisfy their curiosity, advance their careers, plan for the future, acquire skills and connect with people to improve knowledge without intending to finish the entire course [3][5][18]. Moreover, scholars have espoused that the success of MOOCs should rather be based on the learning behaviors of students such as their acceptance [3][19][20]. This proposition makes it propitious to investigate factors influencing student acceptance of MOOCs.
Different studies have used diverse theoretical models, sample sizes and analysis methods to expose numerous significant factors influencing student acceptance of MOOCs [1][21]. MOOC was proposed as a form of sustainable higher education by exploring the theory of task-technology fit (TTF) and technology acceptance model (TAM) to create a novel paradigm of education [22]. However, no consensus has been generally reached as to which factors best influence student acceptance of MOOCs. The one reason for the lack of consensus may be the curb of the existing studies in examining other salient determinants through the exploration of associations that remains a gap in the literature. It has been demonstrated through meta-analysis research that theoretical models and sample sizes are the significant sources of heterogeneity in factors influencing student acceptance of MOOCs [23].

2. Discussions

The overarching objective was to use the data science method of MCA to explore hidden associations among factors influencing student acceptance of MOOCs and heterogeneity sources. This has led to the development of an appropriate dataset based on a comprehensive review of related studies. The dataset was processed using the MultipleCar toolbox [24] and MCORRAN2 Matlab code [25], which are general software suitable for implementing correspondence analysis. The theory, sample and factor are the core variables of the dataset that were considered for the MCA to detect hidden associations. The number of observations in a study often refers to as sample size is an important research consideration. Sample size influences the precision, robustness and validity of the findings from research. The larger the sample size, the more robust is likely to be the study result, but it should not be either too small or too large. On one hand, very small samples can undermine the validity and prevent the generalization of findings. On the other hand, very large samples tend to amplify small differences into statistically significant differences and emphasize insignificant statistical differences [26]. Theoretical models in research offer the footing to establish credibility and application of a wrong theory can lead to erroneous interpretations, judgments and weedy conclusions. However, the selection of the right theory for research can enhance robustness, relevance and impactful findings [27].
This study has used MCA for the first time to investigate hidden associations among the variables of theory, sample and factor. The MCA enables the exploration of hidden associations among variable categories and observations not observed in the literature. It has helped in this study, to detect useful insights about hidden associations among the factors influencing student acceptance of MOOCs, theoretical models applied in the previous studies for factor exploration and sample sizes of students who participated in the different studies. The work reported in this paper is an important assignment that has been overlooked in the literature as evidence from the comprehensive review of the existing studies. Previous studies have improved the understanding of the application of MCA for factor exploration. Meta-regression analysis was used to show that “theoretical model” and “sample size” were statistically significant sources of heterogeneity in factors influencing student acceptance of MOOCs [23]. However, the investigation of associations among influencing factors and sources of heterogeneity that could have yielded significant insights was not previously found.
The MCA has helped address the literature chasm and further enrich the existing studies on MOOCs in the niche area of technology acceptance. There are some important findings from this study as elucidated as follows. Complex theories that combine two or more supplementary factors are rarely applied, but blended theories are commonly used for exploring factors influencing student acceptance of MOOCs. This study has found five previous studies in the literature that have used complex theories for exploring significant factors influencing student acceptance of MOOCs [21][28][29][30][31]. Very small samples usually between 101 and 411 are widely used, but very large samples are rarely used for exploring factors influencing student acceptance of MOOCs. The studies that have used very small samples constitute about 75.93% and the examples of such studies with less than 200 samples include [15][16][19][32][33][31]. Student satisfaction [7][19][34][28][35][36][37][30], student attitude [10][11][38][39][33][40], behavioral intention [5][21][41][42][43] and perceived usefulness [1][2][13][44][45] are the widely used factors for exploring student acceptance of MOOCs. The student satisfaction factor was shown in meta-analysis research to be the main significant factor influencing student acceptance of MOOCs [23]. A very small sample of students is the most unusual under the basic assumption that none of the variables are correlated. The single theory was best represented by the five dimensions, whilst student satisfaction has the poorest representation. The five dimensions found for dimension reduction were easily interpreted and labeled as single theory factors, blended theory factors, complex theory with small sample factors, large sample factors and complex theory with very large sample factors. The salient coordinates in the first and second dimensions suggest that these two dimensions are bipolar and the other three are unipolar. The bipolar dimensions contrast the influencing factors that are on the opposite sides of the dimensions [24]. The simplified principal coordinates of variables and observations were obtained using the widely known varimax rotation algorithm as recommended by the previous authors [24][46].
The first is formed by factors identified by single theories. It is associated with social competence and a combination cloud of factors of teacher knowledge, social influence, perceived functional value, intellectual capital, engagement on platform and perceived enjoyment. The second describes the factors identified by blended theories. It is more associated with computer self-efficacy, gamification perception, intrinsic motivation and a combination cloud of factors of utility value, perceived behavioral control, functional attachment and course quality. The third is composed of factors identified by extended theories. It is associated with facilitating conditions, self-regulation, knowledge outcome and a combination cloud of factors of task technology fit, subjective norm, perceived reputation, performance expectancy, perceived ease of use and flow experience. The last is formed by factors identified by complex theories. It is associated with very small sample, small sample, large sample, very large sample, factors of perceived usefulness, student attitude and a combination cloud of factors of student satisfaction, student habit and behavioral intention that explain student behaviors toward MOOC acceptance.

This entry is adapted from the peer-reviewed paper 10.3390/su132313451

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