Symbolic Interactionism
As a key theory of sociology, symbolic interactionism holds that “human beings act toward things on the basis of the meaning that the things have for them; the meaning of such things is derived from, or arises out of, the social interaction that one has with one’s fellows; these meanings are handled in and modified through an interpretative process used by the person in dealing with the things he encounters” (Blumer, 1969). In this theory, a symbol is a stimulus that is abstract and arbitrary to which meaning is applied. People acquire symbols from the interaction with society or other people, which then allows them to develop a sense of self and a mind. Societies exists because people are able to interact with each other through symbols (Redmond, 2015). Examples of such symbols are languages, gestures, icons, etcetera.
Faules and Alexander (1978) pointed out that symbolic interaction was a communication theory of human behavior, which integrated traditional, behavioral and humanistic approaches to the study of communication. The core concept of this theory lies in the fact that social interaction is a process that forms human conduct instead of being a means or a setting for human conduct (Blumer, 1969). In effective communication, it is the symbolic human interaction that shapes our perception of ourselves and others and leads to further actions and behaviors, which is represented by the following diagram:
When applied in face-to-face communication, this cyclic interaction mostly occurs synchronously with a small part of it occurring asynchronously. In face-to-face communication, we constantly adjust our behaviors real time through other people’s responses to our behaviors. Occasionally, some adjustments also take place afterwards and new behavior patterns emerge in the next round of interaction hours, days, months or even years after the first round.
However, in the online environment, esp. in the case with MOOCs, as the courses are pre-recorded and then played to the students after the recording have been finished, the interaction between the teacher and the students is almost always asynchronous, except for some limited interactive elements such as pauses, fast forward and backward, and embedded test questions and answers. Even for the interactive elements, it is highly unlikely that they will act as a qualified ‘response’ leading to synchronous change of behaviors of either party of the interaction in MOOCs (Hadidi and Sung, 1998; Zhang et al., 2006). Therefore, the lack of synchronous interaction between the instructor and the viewer in MOOCs is where the communication may be hindered and the transfer of meaning can be thwarted. For example, in a traditional classroom, the teachers may scan the students’ facial expression and infer their level of understanding of a certain concept and then choose to either elaborate on it or only briefly mention it. In a pre-recorded course video, such interaction would be impossible, and the teachers’ instruction is standardized with no change for no matter how many times the video is played. But it must also be pointed out that MOOCs possess unique features which could potentially compensate the lack of synchronous interaction (Chen and Zimitat, 2004; Ladyshewsky, 2004; Van Schaik et al, 2003). For instance, the use of animation or 3D graphics together with verbal explanation improves clarity of instruction and enhances understanding. Therefore, MOOCs present features of both face-to-face communication as well as the unique features of online video-based communication. It will be interesting to find out which features from both modes of communication exert the greatest influence on MOOCs.
Face-to-Face Communication in Classroom
In traditional classroom setting, both teaching and learning take place through face-to-face communication. Effective communication in classroom has long been a traditional central topic of a myriad of education research studies, most of which recognize the importance of instruction and curriculum design. One of the most prominent theories of these studies, Universal Design for Learning (UDL), which originated from the research in cognitive science and neuroscience, has become the new gold standard for learning design (Dalton, 2017). First developed by the Center for Applied Special Technology (CAST), UDL takes into consideration the variability of learners and design the instruction and curriculum which address this variability (CAST, 2010). UDL aims to enable effective communication in classroom based on the three learning components: 1. recognition of the information to be learned; 2. application of strategies to process the information; and 3. engagement in the learning task (Vygotsky, 1962). Further development of UDL by Hall, Strangman, and Meyer (2003) set out three core principles: 1. multiple means of representation; 2. Multiple means of action and expression; 3. multiple means of engagement. To assist in the effective design of curriculum and instructor and provide operational guidance, CAST developed the following set of guidelines for UDL implementation (CAST, 2011):
Multiple Means of Representation:
2) Provide options for language, mathematical expressions, and symbols
3) Provide options for comprehension
Multiple Means of Action & Expression:
5) Provide options for expression and communication
6) Provide options for executive functions
Multiple Means of Engagement:
8) Provide options for sustaining effort and persistence
9) Provide options for self-regulation
The nine guidelines have also been recently applied to online education environment. However, considering the fact that it first originated from classroom teaching theories and practice, it is more reasonable to view them mainly as the basic principles of face-to-face interaction or communication in traditional classroom.
Cognitive Load Theory and Communication in MOOCs
In the field of instructional science, cognitive load theory (CLT) provides a way of explaining factors influencing the learning process. CLT views instruction as containing extraneous, intrinsic and germane load. Extraneous load is information presentation which contains content which is irrelevant or difficult to understand. Intrinsic load is the inherent difficulty in learners when learning new information. Germane load is the ability of learners to make association with the new knowledge and acquire the knowledge (De Jong, 2010; Sweller, 2005). High amounts of cognitive load, or high extraneous load, will hinder the learner’s ability to process information and learn (Hughes et al., 2018). From the early developmental stage of this theory, CLT has been closely connected with and applied in multimedia learning. Especially, the effect of extraneous load, which is often put into practice in the instructional and curriculum design in multimedia learning environment, has become the focus of research on the effectiveness of E-learning (Bradford, 2011; Mayer and Moreno, 2010).
As a major application mode of E-learning, MOOCs present the features of multimedia learning and the principles of effective communication in multimedia are also applicable for MOOCs (Delahay and Lovett 2018). According to Mayer and Fiorella (2014), in a multimedia learning environment, the extraneous load can be effectively reduced with the following five instructional design techniques:
1. Coherence: Eliminate extraneous material to reduce processing of extraneous material. For example, exclude interesting but irrelevant statements or graphics.
2. Signaling: Provide cues for how to process the lesson to reduce processing of extraneous material. For example, add signals that show the learner what to attend to and how to organize it.
3. Redundancy: Avoid presenting identical streams of printed and spoken words concurrently with corresponding animation. For example, present words as narration rather than as narration and on-screen text.
4. Spatial contiguity: Place printed words near corresponding parts of graphics to reduce the need for visual scanning. For example, put printed words near rather than far from corresponding parts of an illustration (on paper) or animation (on a screen).
5. Temporal contiguity: Present corresponding narration and animation at the same time to minimize the need to hold representations in memory. For example, present corresponding narration and animation simultaneously rather than successively.
Admittedly, as ramification of Mayer’s seminal work, there are multiple studies exploring the methods and techniques to reduce cognitive loads in multimedia learning environment, which results in various versions of such techniques and methods (Ayres, and Sweller, 2014; Höffler and Leutner, 2007; Ibrahim et al., 2012). However, the above five principles remain the most inclusive and over-arching techniques used to reduce extraneous cognitive load in multimedia learning environment.
Research Question and Research Framework
Research Questions
This study aims to find out the design features that exert the greatest influence on the effectiveness of MOOCs and intends to answer the following question:
Out of all the design features of MOOCs, which ones are highly correlated with a MOOC which is well-received by its viewers (successful)?
To answer this research question, we use the review score a MOOC video receives as an index to represent the extent to which it is well-received by its viewer. For the design features of MOOCs, we combine the features of both classroom instruction and online teaching.
Research Framework
As MOOCs present features of both face-to-face communication and multimedia learning, a combination of the models for analyzing both types of communication will be conducive to conducting an in-depth examination of the factors influencing the effectiveness of MOOCs. Therefore, features extracted from the method of reducing extraneous load in CLT and the five principles of UDL are combined as the theoretical framework for our analysis. For each of the 14 features, we further operationalized them and use a descriptive index to represent them as follows:
Table 1
Theoretical Framework and Operational Features for the Analysis of MOOCs
Theoretical Framework
|
Operational Features
|
1) Provide options for perception
|
Provide visual, audio, digital and hard copies to students
|
2) Provide options for language, mathematical expressions, and symbols
|
Provide visual or translation, pre-teach vocabulary and math symbols, and point out text structures, math formula as well as scaffolding in reading to students
|
3) Provide options for comprehension
|
Provide pre-context, pre-knowledge, work examples, and application of the knowledge to the students
|
4) Provide options for physical action
|
Allow students to use different media to complete assignments or use technology to express knowledge
|
5) Provide options for expression and communication
|
Give students choice of how to respond, and provide feedback when students are working.
|
6) Provide options for executive functions
|
Provide objective and rationale for assignment, give students tips and checklists to help them work through the assignment
|
7) Provide options for recruiting interest
|
Provide motivation to students and break the session into segments
|
8) Provide options for sustaining effort and persistence
|
Help students clarify a lesson's objectives, provide different challenges to students and give feedback
|
9) Provide options for self-regulation
|
Allow students to make choice, tell students the relevance of a lesson and create a classroom environment that is suitable for learning.
|
10) Coherence
|
Eliminate extraneous material to reduce processing of extraneous material.
|
11) Signaling
|
Provide cues for how to process the lesson to reduce processing of extraneous material
|
12) Redundancy
|
Avoid presenting identical streams of printed and spoken words concurrently with corresponding animation.
|
13) Spatial contiguity
|
Place printed words near corresponding parts of graphics to reduce the need for visual scanning
|
14) Temporal contiguity
|
Present corresponding narration and animation at the same time to minimize the need to hold representations in memory.
|
Research Method
For the purpose of this study, we chose the foreign language course videos offered on one of the most renowned MOOCs platforms of China. The platform contains thousands of MOOCs developed by universities across China. The disciplines of the MOOCs cover arts and sciences, humanities, law, business and management, engineering as well as vocational and continued education. As of January 13, 2021, under the category of Foreign Languages Courses, there are over 384 related courses. We chose only the courses in this category so as to ensure the control of the confounding factors to the greatest extent. For each course, an introduction to the course, a syllabus, and a series of course videos are provided by the instructors as compulsory elements. The platform also offers the functions of forum, assignments and tests, bulletin boards for instructors to upload related materials onto the platform and have interaction with the viewer. In addition, under course function, instructors may also optionally upload teaching materials in various media, including downloadable PowerPoint presentation and Word-processed notes. Registered users of the platform may enroll in most of the course free of charge and give a review score of 1–5 to each course. The final review score, which is an average of all ratings given by the users, represents the level of how well-received a course is.
Based on the research framework, we propose the following hypotheses for testing in this study:
H1. Providing visual, audio, digital and hard copies to students is positively correlated with the review score of a course video.
H2. Providing visual or translation, pre-teach vocabulary and math symbols, and pointing out text structures, math formula as well as scaffolding in reading to students is positively correlated with the review score of a course video.
H3. Providing pre-context, pre-knowledge, work examples, and application of the knowledge to the students is positively correlated with the review score of a course video.
H4. Allowing students to use different media to complete assignments or use technology to express knowledge is positively correlated with the review score of a course video.
H5. Giving students choice of how to respond, and providing feedback when students are working is positively correlated with the review score of a course video.
H6. Providing objective and rationale for assignment and giving students tips and checklists to help them work through the assignment are positively related with review score of a course video.
H7. Providing motivation to students and breaking the session into segments are positively correlated with the review score of a course video.
H8. Helping students clarify a lesson's objectives, providing different challenges to students and giving feedback are positively correlated with the review score of a course video.
H9. Allowing students to make choice, telling students the relevance of a lesson and creating a classroom environment that is suitable for learning are positively correlated with the review score of a course video.
H10. Eliminating extraneous material to reduce processing of extraneous material is positively correlated with the review score of a course video.
H11. Providing cues for how to process the lesson to reduce processing of extraneous material is positively correlated with the review score of a course video.
H12. Avoiding presenting identical streams of printed and spoken words concurrently with corresponding animation is positively correlated with the review score of a course video.
H13. Placing printed words near corresponding parts of graphics to reduce the need for visual scanning is positively correlated with the review score of a course video.
H14. Presenting corresponding narration and animation at the same time to minimize the need to hold representations in memory is positively correlated with the review score of a course video.
In this project, for every course in the category Foreign Languages Courses, we assigned two graduate students who have had extensive experience with MOOCs as independent evaluators to give a rating of 1–5 for each of the above 14 features. The rating of 1 indicates that this feature presents itself very weakly in a course, and the rating of 5 indicates that a certain feature has a very strong presence in a course. The average score of the two evaluators for each feature of each course was calculated as the final score for that feature of the course. In order to eliminate the potential bias in the ratings, we made sure that both evaluators did not see the review score before they finished providing the ratings for the features.
The following principles were followed by both evaluators when rating the MOOCs in the sample:
1. If the instructor provides downloadable files or materials in the course module of the platform besides course video, it should be regarded as providing hard copies to the students, which will result in higher score for feature 1.
2. In the assignment module of the course, if the instructor provides various types of assignments with audio, html text, and video or asks the students to upload their recorded audio files in addition to online written assignments, the evaluators will give a higher score to feature 4 for this course.
3. In the forum module, if there are extensive interaction between the instructor and the students/viewers, then the score of feature 5 for this course will be correspondingly higher. The higher score is given to those courses with not only just interaction in the forum, but also other online-interaction conducted through instant messaging or emails as well as off-line interaction.
4. In the course video, if the instructor asks many questions and provides answers afterwards or gives small test questions during the video, such courses will receive a higher score for feature 8.
5. For course videos without using any animation, its score for feature 14 will be 1.
6. For any courses taught by the same instructor from the same university, only one of them will be selected into our sample and rated.
After data collection was finished, we used multiple linear regression as the quantitative analysis method for this study. According to Saunders et al. (2012), regression analysis is used to predict the value of a dependent variable given the values of one or more independent variables by calculating a regression equation, which corresponds to the situation of this research. We collected a total of 280 samples from the dataset and those which either did not have review score or had an outlier score were excluded. After the ratings for all 14 features as well as the review scores were collected, multiple linear regression analysis was conducted with IBM SPSS Version 25 and the results are shown in the next section.