The study of learning has been undertaken since the beginning of the twentieth century and has been heavily influenced by psychology. Although multiple definitions exist, learning has been generally defined as a persistent change in behavior or performance as a result of some stimulus. This definition encompasses both behavioral and cognitive aspects of learning. Behaviorism, which postulates that the environment has a direct impact on behavior, dominated roughly the first half of the twentieth century. In the latter half of the twentieth century, cognitive theories challenged behaviorism by asserting that learners process information obtained from the environment and that behavior is the result of how the information is processed. Learning theorists focused on understanding the process of learning, whereas other scholars sought to develop methods for measuring and assessing learning.
The Conceptualization of Learning
One of the first efforts to assess learning systematically was led by Benjamin Bloom (1956), who put forth a taxonomy of cognitive learning. This taxonomy was intended to classify “the changes produced in individuals as a result of educational experiences” (Bloom 1956, 12), or in other words, learning. The taxonomy is hierarchical in nature, with each successive level subsuming the preceding one. The six major classes of learning are knowledge (lowest), comprehension, application, analysis, synthesis, and evaluation (highest). A primary purpose of the taxonomy was to facilitate the assessment of learning by creating common terminology and definitions for specific student behaviors.
In 1964, Krathwohl et al. extended the taxonomy of cognitive learning to include the affective and psychomotor domains. The affective domain emphasizes “a feeling, an emotion, or a degree of acceptance or rejection” (Krathwohl et al. 1964, 6) and includes receiving (attending), responding, valuing, organization, and characterization by a value or value complex. Similar to the taxonomy of cognitive learning, the taxonomy of affective learning is also hierarchical. Krathwohl et al. noted that affective objectives are often assumed to have been accomplished if cognitive objectives have been obtained. However, they argued that, similar to higher levels of cognitive learning, affective learning develops when appropriate learning experiences are provided to students, and, therefore, affective learning – like cognitive learning – must be facilitated.
In 2001, Anderson and Krathwohl, with their colleagues, revised Bloom’s taxonomy, creating a two-dimensional taxonomy consisting of the cognitive process and knowledge dimensions. They identified four organizing questions in education: (1) what is important for students to learn? (2) how should instruction be delivered? (3) how should learning be assessed? and (4) how can learning objectives, instruction, and assessment be aligned? The second question – how instruction should be delivered – has been the focus of the communication discipline.
The Operationalization of Learning
Learning has been measured using performance and self-report methods. Performance methods usually involve some type of test of ability to recall information. A problem with this method is that students may score well on a test of knowledge not because they have learned the content, but because they already knew the information. Writing ability, anxiety, and other factors can also impact students’ scores, reducing the validity of socalled objective tests. The self-report method involves asking individuals how much they think they have learned. Psychology and education scholars have favored performance methods because of their reliance on the experimental method, whereas communications scholars have relied more heavily on self-report methods.
Communication scholars have focused on how teacher communication behavior influences student learning, and have sought to make generalizations about teacher behavior across disciplines and age groups. In order to make generalizations, students’ perceptions of their own learning have frequently been used. The use of self-reported learning is based on the assumption that adult students are aware of their learning and can self-report the general quantity they have learned from a class. The measurement of learning by self-report measures has been a contentious issue in the communication discipline, particularly the measurement of cognitive learning. However, at this point, the desire to generalize findings has overridden concerns about the self-report method. The two most commonly used self-report measures of cognitive learning are the cognitive learning measure (McCroskey et al. 1985) and the learning indicators scale (Frymier & Houser 1999). The McCroskey et al. measure consists of two questions. The first asks students to estimate how much they have learned on a scale from 0 to 9, and the second asks them to estimate how much they could have learned with an ideal teacher on the same scale. The answer to the first question is subtracted from that to the second to address students’ lack of confidence in their ability to learn the content. The learning indicators scale consists of seven questions that ask how frequently students engage in behaviors believed to be a part of learning, such as relating content to other courses.
The measurement of affective learning also utilizes a self-report methodology. The construct of affective learning has been embraced in communication, and students’ self-reported attitudes toward the content and the teacher have been the most common way of measuring learning in communication. Affective learning was first measured in the late 1970s. In 1998, Mottet and Richmond examined both the conceptualization of affective learning and its measurement, and expanded the measurement of affective learning from six to eight constructs. Their instrument contains six constructs that measure affect toward the content and course and two that measure affect toward the teacher. The measurement of affective learning has been considered a reliable and valid measure and has been used in numerous studies.
The Impact of Communication On Learning
A great deal of instructional communication research has focused on how teacher communication behavior impacts student learning. Learning is the most commonly used outcome variable in instructional communication research. However, other student outcomes, such as motivation and satisfaction, have also been studied. Because of the interest in generalizing across disciplines, instructional communication scholars have focused on general teacher communication behaviors rather than on discipline-specific teaching strategies. Six teacher communication behaviors (immediacy, affinity seeking, power, clarity, relevance, and humor) and one student characteristic (communication apprehension) have received significant research attention and are associated with student learning, particularly affective learning.
Teacher immediacy has received the most research attention and has consistently been related to positive student outcomes. Immediacy is defined as a perception of physical and/or psychological closeness between people (Richmond et al. 1987). Jan Andersen (1979), the first communication scholar to examine teacher immediacy, found teacher use of nonverbal immediacy behaviors to be associated with affective (but not cognitive) learning, and this finding has been replicated consistently over time. The correlation between nonverbal immediacy and affective learning has typically been moderate to moderately large, indicating a consistent and substantial relationship between nonverbal immediacy and affective learning. The relationship between immediacy and cognitive learning has been less certain. Immediacy has consistently been positively related to self-reported cognitive learning, and has also been associated with a higher rate of recall in a handful of experimental studies, negating Andersen’s (1979) finding to the contrary. Immediacy appears to impact primarily students’ attitudes and motivation, which, in turn, has some impact on cognitive learning. For a thorough review of the construct of teacher immediacy, see Richmond et al. (2006).
A second teacher variable, affinity seeking, was first discussed by McCroskey and Wheeless (1976), who defined it as a positive attitude toward another person. Later, Bell and Daly (1984) expanded and refined the construct of affinity seeking and identified 25 affinity-seeking strategies. The affinity-seeking typology was applied to the education setting, both to see if teachers used affinity seeking in the classroom and to examine the impact of affinity seeking on student learning and motivation. Although the body of research is not large, multiple studies have concluded that both K-12 and college teachers use affinity seeking to gain student liking, and that some affinity-seeking strategies are associated with student affect and motivation. Although conclusions vary somewhat on which strategies are the most appropriate and effective in the classroom, some – facilitate enjoyment, optimism, assume equality, conversational rule-keeping, comfortable self, dynamism, elicit other’s disclosure, altruism, listening, and sensitivity – have consistently been identified as useful for teachers. For a more thorough review of affinity seeking in the instructional context, see Frymier and Wanzer (2006).
How teachers go about influencing students to do specific tasks is referred to as compliance gaining in general, which is a concept that is rooted in the construct of power. McCroskey and Richmond (1983) were the first to examine teachers’ use of power in the classroom. Subsequent research has found that students’ perceptions of teacher use of referent and expert power are positively associated, while those of teacher use of coercive and legitimate power are negatively associated, with affective learning. How teachers enact power in the classroom was examined by Kearney et al. (1985), who identified 22 compliance-gaining strategies used by teachers, which they refer to as behavioral alteration techniques (BATs). Most of the BATs can be classified as enacting one of the five power bases (reward, coercive, legitimate, referent, and expert). Consistent with the research on power, the BATs based in referent, expert, and reward power are associated with affective learning, and those based in coercive and legitimate power are negatively associated with affective learning. Plax et al. (1986) replicated these relationships and found that nonverbal immediacy moderated the effect of BAT use on students’ affective learning. For a more thorough review of power and learning, see Roach et al. (2006) and Richmond & McCroskey (1992).
Immediacy, affinity seeking, and power are primarily relationship variables that influence the teacher–student relationship. How a teacher presents content and structures messages also influence student learning. One such variable is clarity. Clarity refers to teacher behaviors that contribute to the fidelity of instructional messages (Chesebro & Wanzer 2006). When teachers are clear they do things such as use examples, stress important points, and use organizational cues to facilitate student note taking. Clarity can also include having a clear syllabus, clear assignments, checking for understanding, repeating important points, and using nonverbal behaviors to facilitate student understanding. Clarity has been operationalized in numerous ways, making it difficult to draw specific conclusions about its impact on learning. However, clarity, in its many operationalizations, has been consistently associated with positive outcomes for students and has been linked to both cognitive and affective learning.
Another aspect of how a teacher presents content is how relevant the message is to students. When students perceive the instructor’s message as relevant, they see the content as satisfying personal needs, personal goals, and/or career goals (Keller 1983). There has been a limited amount of research on relevance as a communication phenomenon. Relevance strategies have been associated with on-task behavior, and perceived relevance has been associated with self-reported learning (affective and cognitive). However, research on relevance in communication has been stymied by difficulties in manipulating relevance. The existing research indicates that when students perceive a teacher’s message as relevant to their interests or needs, their learning is enhanced. However, the specific behaviors that lead students to perceive a teacher’s message as relevant are not well understood. For a thorough review of the research on relevance, see Chesebro and Wanzer (2006).
Humor is a communication variable that teachers can use to enhance their relationship with students, as well as being a strategy for presenting content. Humor has been studied extensively both in and out of the classroom. The first way in which humor has been studied is in experimental designs where humorous messages are manipulated. A problem with this approach is that there are so many different forms of humor and only one or two of them can be used in any one experiment, making generalizations difficult. A second method has focused on describing what types of humor messages are used by teachers. This line of research has been useful in describing how humor is used, and has provided an understanding of the vast array of humor messages used by teachers. A third method has been to focus on humor as a personality characteristic, or more specifically, humor orientation. Humor orientation refers to a predisposition to use humor in a variety of situations (Booth-Butterfield & BoothButterfield 1991).
The results of these different approaches have revealed that the use of humor is associated with learning, that teachers use a variety of humorous messages (some of which are inappropriate), and that students report learning more from teachers who are perceived as humor oriented. Research has just begun to compare the impact of a humorous message in relation to how the humor is delivered by a teacher. Humor is one of the more complex communication variables studied in the classroom because of the number of forms a humorous message can take, the role of delivery by the teacher, and individual student differences in perceiving the humorous message. At this point, research indicates that humor, when used appropriately, is a useful teaching tool. However, it should be used to enhance other teaching strategies such as immediacy, clarity, and prosocial use of power, and not as a standalone technique. For a thorough review of humor in relation to learning, see Chesebro and Wanzer (2006).
Although teachers have a major impact on the classroom environment and on how students behave in the classroom, students also impact the learning environment. How students impact the classroom and their own learning has received little attention, with the exception of the communication apprehension (CA) construct. CA is the fear or anxiety associated with real or anticipated communication (McCroskey 1978). CA has consistently been negatively associated with student learning and other student outcomes. Highly apprehensive students learn less, receive lower grades, and have lower ACT scores. Many teachers in the United States expect students to interact in the classroom, and they base grades and other evaluations on students’ oral communication in class. While there are treatment options available to students with high levels of CA, skills training alone (e.g., requiring students to take communication-intensive courses) is not an effective treatment. For a thorough review of the impact of CA on students, see McCroskey & Richmond (2006).
Beginning in the 1970s, communication scholars have examined the role of several communication variables in learning environments. This research has been based on the premise that teaching is communicating and to be an effective teacher, one must be an effective communicator. Scholars in this area have purposely avoided studying the presentation of specific content. Rather they have focused on identifying effective communication behaviors that go across disciplines. Verbal and nonverbal communication behaviors that communicate approach, liking, caring, and interest in students have consistently been found to be positively associated with affective learning and motivation, and to a lesser degree, cognitive learning.
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