Answering the Question: Student Perceptions of Personalized Education and the Construct s Relationship to Learning Outcomes

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1 Communication Education Vol. 56, No. 4, October 2007, pp Answering the Question: Student Perceptions of Personalized Education and the Construct s Relationship to Learning Outcomes Jennifer H. Waldeck The primary objective of the present investigation was to learn what college teachers say and do to create student perceptions of personalized education, and to identify any meaningful factor structure underlying these categories of personalized education characteristics. Additionally, this study was designed to identify how the personalized education construct, as it is perceived by students, may be conceptually and theoretically linked to other instructional communication variables and to establish concurrent validity of a measure of personalized education. Finally, this study examined the relationship between personalized education and learning outcomes. Results should demonstrate the role of instructional communication scholarship in institutional and faculty efforts to create systematic and effective personalized educational experiences for students. Keywords: Personalized Education; Teacher Immediacy; Extra-Class Communication; Mentoring; Cognitive Learning; Affective Learning Personalized education has become a catchphrase for all kinds of educational promises in university recruitment materials and mission statements developed by both small, private liberal arts schools and large, publicly funded research universities (Waldeck, 2006). For example, in its promotional materials, Loyola Marymount University in Southern California describes its personalized education as ensuring that students will acquire the knowledge and skills to lead (2007). Another small liberal arts institution mentions the concept in its mission statement: a personalized education of distinction that leads to inquiring, ethical, and productive lives as global citizens (2007). Larger universities such as the University of Wisconsin tout the benefits of personalized education in an effort to reduce the perception that students Jennifer H. Waldeck is at Chapman University. Jennifer H. Waldeck can be contacted at ISSN (print)/issn (online) # 2007 National Communication Association DOI: /

2 410 J. H. Waldeck become nothing more than numbers at large, state-supported schools; UW Milwaukee s Honors Program website advertises the personalized education of a small liberal arts college without sacrificing the unique opportunities available at a major research university (2007). The University of North Carolina at Asheville emphasizes a personalized education characterized by close facultystudent interactions, challenging academic programs and service-learning activities (2007). In addition to capitalizing on faculty accessibility at small universities, and promoting a small-campus feel at large universities, personalized education may represent academia s effort to overcome the common student complaint that professors are so preoccupied with their research that their teaching and advising suffer. In 1990, the Carnegie Foundation for the Advancement of Teaching noted that overall...[undergraduate] teaching is not well rewarded and faculty who spend too much time counseling and advising students may diminish their prospects for tenure and promotion (Boyer, 1990, pp. xixiii) and called for systematic structural changes in order to meet undergraduate students needs. Researchers interested in both secondary and postsecondary education argue that... although difficult and expensive...a good teacher provides a personalized education (Del Corso, Ovcin, & Morrone, 2005, p. 574). Similarly, Dunn and Griggs (2000) note that personalized education is a way to capitalize on student strengths and results in true learning. Waldeck (2006) speculates that if properly conceptualized, operationalized, and assessed over time, personalized education may predict important variables like student retention and graduation. Despite the many promises of personalized education, there appears to be little empirical work defining the concept or validating strategies for creating it (Waldeck, 2006). Professors can only guess what specific classroom and extra-class behaviors comprise effective personalized education, and whether these behaviors are even empirically linked to meaningful student learning outcomes. However, many professors are held accountable by their institutions and are evaluated on their ability to deliver on the promise of personalized education (Waldeck, 2006). They struggle to do so in the absence of an operational definition of personalized education, and with a scarcity of research indicating what teacher behaviors and teacherstudent interactions promote this type of learning. The mandate to deliver personalized education poses challenges to faculty that range from extra work for teachers in the form of numerous independent study arrangements and special projects geared toward students individualized learning needs, to confused and resentful students who misunderstand flexible course requirements. Therefore, the primary objective of this study was to derive empirically a profile of the nature and frequency of students personalized educational experiences so that a wider audience of professors might understand both the benefits and the challenges associated with personalizing students educational experiences. Furthermore, the expectations that administration, students, and parents may hold about personalized education represent yet another responsibility for faculty members pressured by research requirements, classroom performance standards, and what are sometimes heavy service demands. Anecdotal evidence indicates that faculty may be growing

3 Personalized Education 411 tired of increasing demands to create and maintain relationships with students and to personalize their curriculum when such activities are not part of the formal reward structure in U.S. colleges and universities. Research in this area should contribute to an ongoing discussion about how faculty interactions with students are formally rewarded by their institutions. Although the requisite empirical base for drawing conclusions about personalized education in the university setting is absent, anecdotal evidence of what comprises it indicates that the construct may be conceptually similar to a number of instructional communication variables already defined, tested, validated, and refined by researchers from our field. For instance, Waldeck (2006) reported that one possible dimension of personalized education involves students and teachers engaging in social exchanges and reciprocal self-disclosure*two characteristics of the extra-class communication variable elucidated by Fusani (1994) and Bippus, Kearney, Plax, and Brooks (2003). In other words, part of the answer to the question, What is personalized education? might already exist in the form of published instructional communication theory and research which could benefit administrators and faculty in their efforts to answer the question and create personalized experiences for students. Thus, a second goal of this study involved investigating the relationships and possible conceptual overlap among personalized education and other instructional communication variables, including immediacy (Rodriguez, Plax, & Kearney, 1996; Richmond, Gorham, & McCroskey, 1987), mentoring (Waldeck, Orrego, Plax, & Kearney, 1997), and extra-class communication (Bippus et al., 2003; Fusani, 1994). Finally, some literature casts doubt on the relationship between certain personalized educational practices and learning outcomes. Waldeck (2006) notes that the mandate for personalized education poses numerous costs to faculty ranging from extra work and time-intensive independent study arrangements, to students who become confused about flexible course requirements. Moreover, institutions often invest resources such as faculty and space, and reduce class sizes to cultivate student perceptions of personalization (Johnson, 2002). Given these costs, the payoff of substantial student learning outcomes is critical*and at this time, unsubstantiated by anything other than case study reports and opinion pieces in the educational administration literature. For instance, small class sizes may or may not be related to student learning (Biddle & Berliner, 2002). Research findings on class size are mixed (and focus mostly on elementary and secondary schools) (Health and Education Research Operative Services, 2003). The relationship between small class sizes and student learning appears to be dependent on the discipline, qualities of the instructor, and characteristics of students (Black, 2003). Meyer (2002) found that smaller class sizes are linked to higher learning outcomes for females in the sciences; in another study, small class sizes were predictive of higher student self-esteem*but not necessarily higher test scores or cognitive learning outcomes (Black, 2003). Is personalization just a feel-good marketing tool, or does it accomplish solid learning outcomes among students? The third objective of this study was to examine the relationship between student perceptions of personalized education and cognitive and affective learning.

4 412 J. H. Waldeck Personalized Education Most of our knowledge about personalized education is derived from case-study research conducted in elementary and secondary school settings (Jenkins & Keefe, 2002), testimonials and opinion pieces advocating particular educational policies (McElroy, 2007; Weaver, 2006), and college and university marketing materials promoting school approaches to personalized educational delivery (Waldeck, 2006). Interestingly, the most solid empirical/social scientific work on personalized education is focused on the learning medium that most consider to be the least personal*distance and online education (Apple, 2006; Del Corso et al., 2005). Keefe and Jenkins (2000) suggest several general elements of personalized education: (1) an evolving, deepening relationship between teacher and student; (2) a collegial school culture based, in part, on lowered class sizes; (3) the diagnosis of student learning characteristics; (4) an interactive learning environment; and (5) flexible scheduling and assignments. Waldeck (2006) summarized and critiqued activities that universities and their faculty appear to engage in that support Keefe and Jenkins s recommendations, noting that many suggestions made in the literature for creating personalized experiences are often vague and empirically unsubstantiated. Critique of Personalized Education Characteristics Literature In the area of creating faculty/student relationships, no research-based advice is offered which suggests the desired nature and depth of these relationships. Case studies of model personalized educational programs provide mixed examples that range from a faculty-as-friend model that eliminates all traditional hierarchical boundaries between teachers and students (Jenkins & Keefe, 2002) to simply raising student awareness of teacher accessibility (Shore, 1995). Moreover, Waldeck (2006) suggested that although small class sizes are promoted as a key to personalized education, their importance might be overgeneralized for the higher-education context. Research findings on appropriate college class sizes are mixed (Black, 2003; Johnson, 2002; Meyer, 2002). Although important to students not yet socialized to the university learning environment (Biddle & Berliner, 2002), most research suggests that smaller college class size does not significantly impact students grades. Personalized Education Plans (PEPs) address two of Keefe and Jenkins s recommendations: the diagnosis of student learning characteristics, and flexible scheduling and assignments. PEPs suggest curriculum paths, extra-curricular experiences and internships, and competencies that need to be developed (Evans, Ali, Singleton, Nolan, & Bahrami, 2002). PEPs typically are based on the results of diagnostics and assessments such as Meyers Briggs Type Inventory, learning styles inventories, and Emotional Intelligence/EQi (Jenkins & Keefe, 2002). Although PEPs are most common in Great Britain and Canada, and among continuing and online education providers, there are several examples of PEP implementations in U.S. universities.

5 Personalized Education 413 Portland (Oregon) State University has implemented the use of 360 Degree instruments (similar to those used in evaluating employee performance in organizations) with all MBA students, the results of which are used to plan curricular activities that ensure proficiency in specific competencies (Waldeck, 2006). Despite the benefits of a properly designed and implemented PEP, the traditional structure of most universities typically precludes such individualized curricular planning. As a result, some instructors experiment with pseudoscientific methods for assessing a given group of students needs and preferences for learning (Waldeck, 2006). For example, instructors sometimes offer alternative assignments so that students who prefer experiential learning, research and writing, or oral presentational work have freedom to choose. The choice itself often confuses and frustrates students who generally want structure and guidance in the classroom (Tschirhart & Wise, 2002)*and the results are potentially invalid and unreliable if each option does not measure the exact same competencies and skills to the same degree. Waldeck (2006) noted that the end result may be perceived negatively by students as a type of teacher misbehavior (Kearney, Plax, Hays, & Ivey, 1991). Kearney et al. identified a number of teacher behaviors that negatively impact student satisfaction with their learning experience, including lack of organization, unclear expectations, and deviations from the syllabus. Ironically, Personalized Education Plans are much more common in web-based education environments, which are ostensibly much less personal than on traditional college campuses. Engineers and technology researchers have developed sophisticated electronic course customization platforms that identify and provide relevant learning objectives based on individual learning needs and styles (Apple, 2006; Del Corso et al., 2005). These provide frameworks for online undergraduate and graduate learning, as well as continuing education and corporate training, yet the traditional academy has been slow to adopt formal learning management systems (Townley, 2003, p. 8) similar to those efficiently used in for-profit enterprises. Finally, Waldeck (2006) addressed the issue of interactive learning experiences as indicators of personalized education. Many instructors believe that personalized learning emerges when students work closely with one another on course-related activities (Jenkins & Keefe, 2002). Considerable evidence indicates that students learn better and have more positive affect toward the class and subject matter when working in groups than when working alone (Allen & Plax, 2002). However, when teachers dispense with whole-class instruction and their role as facilitator/leader, and put students into collaborative work groups, they give up a great deal of their control over the communication that takes place in the classroom. Moreover, Allen and Plax discuss a number of group relational tensions that may work against the personalized learning objectives of collaborative classroom activities if not properly managed by a skilled instructor/facilitator, including issues of democratic participation, trust, disclosure, and power. Unlike elementary and secondary educators, most university instructors have no extensive formal training in teaching techniques and instead rely on experience (Katz & Henry, 1988); thus, a strong potential for problems with interactive learning exists.

6 414 J. H. Waldeck Although there are common themes that appear to characterize personalized education initiatives at colleges and universities in the United States*such as faculty/student relationships, small class sizes, Personalized Education Plans, and collaborative learning arrangements*this study sought to identify patterns among college students experiences with what they perceived as personalized educational experiences, and to define the current state of personalized education based on student reports. Therefore, in an effort to identify an operational definition of personalized education, the first research question asked: RQ1: What do college teachers say and do to create student perceptions of personalized education? In order to demonstrate any effects of school attributes*class sizes, the cultural emphasis placed on personalized education through promotional and recruitment materials, mission statements, and faculty development initiatives*on student perceptions of personalized education, the following research question was posed: RQ2: Do perceptions of personalized education vary as a function of school characteristics? Within the literature on characteristics of and strategies for creating personalized education, the reader might have detected a number of suggestions that reflect knowledge claims already extant in instructional communication theory and research. For example, the nature of studentteacher relationships that are emphasized within the personalized education movement may be explained by the immediacy and extraclass communication literature. Immediacy and Extra-Class Communication The immediacy literature suggests a range of teacher behaviors that signal personal involvement with students and that may be concrete indicators of what students perceive as personalized educational experiences. Immediacy behaviors require various levels of teacher commitment, time, and interest level. For instance, simply moving around the classroom while teaching is one immediacy behavior that results in student motivation and learning (Christophel, 1990). However, the research on extra-class communication (ECC) suggests that teachers must take additional steps beyond prosocial classroom behaviors to be perceived as offering the most personalized experience for students. Specifically, Bippus et al. (2003) found that students predicted more positive outcome values of ECC when teachers were socially accessible and provided a range of mentoring functions related to the course and to students overall career goals. Jenkins and Keefe (2002) proposed that these types of teacher behaviors serve as the foundation for the dual teachercoach role that is important to the delivery of personalized education. Bippus et al. s work points to specific areas that are important in building a relationship with students such that they will feel known to their professors. In view of the empirical evidence of the importance of both teacher

7 Personalized Education 415 immediacy and ECC to the student experience, a model of personalized education must include both classroom interactions and teacherstudent communication outside the classroom which is targeted toward specific student needs and goals. (Those identified by Bippus et al. should serve as an initial list.) In this way, we should come to know what it means to know our students in meaningful ways that result in perceptions of personalized education and other important learning outcomes. H1: Student perceptions of teacher immediacy are positively related to studentreported personalized educational experiences. H2: Student perceptions of extra-class communication are positively related to student-reported personalized educational experiences. Mentoring In a case study of two model schools for personalized education, Jenkins and Keefe (2002) describe teachers as maintaining dual coach and advisor roles who act as facilitators to students engaged in their subject-area specialties both as they pertain to their education and future career development (p. 3). At one institution described in the case study, teachers serve as more than advisors to students... nurturing their intellectual, emotional, social, and ethical development (p. 5), and view themselves as friends of the students. These teacher roles seem to correspond conceptually to the functions of a mentor (Kram, 1988; Ragins & McFarlin, 1990; Waldeck et al., 1997). The first set of mentor functions, labeled psychosocial, address the interpersonal aspects of mentoring (Ragins & McFarlin) and encompass mentor behaviors that enhance a protégé s sense of competence, identity, and social effectiveness in personal and professional roles, and career functions encompass mentor behaviors that facilitate protégé learning, exposure, and skill development (Kram, 1988, p. 32). Prior research conducted in corporate settings indicates that mentors tend to provide more career functions than psychosocial functions to their protégés, whereas in their investigation of mentoring in the graduate education context, Waldeck et al. found that graduate students experience more psychosocial functions in their mentoring relationships than career functions, view them as more important, and are more satisfied with this aspect of their mentoring relationships than the career function. Thus, the following hypothesis was posed to test the relationship between mentoring functions in general and personalized education, and to determine which set of mentor functions is most frequently reported by students who have had personalized educational experiences: H3: Student perceptions of personalized education are positively related to mentoring. RQ3: Are course/career or psychosocial mentor functions reported more frequently by students who experience personalized education?

8 416 J. H. Waldeck Student Learning The final concern addressed by this study involves the relationship between studentperceived personalized education, and affective and cognitive learning outcomes. Whereas cognitive learning focuses on a student s intellectual ability to comprehend, organize, and evaluate course-related content (Bloom, Englehart, Furst, Hill, & Krathwohl, 1956), affective learning has to do with the internalization of positive attitudes toward the course content or subject matter (Kearney, Plax, & Wendt- Wasco, 1985). Instructional communication researchers have used both types of learning as important outcome variables in their studies for several decades (Waldeck, Kearney, & Plax, 2001), and Krathwohl (1964) suggests that teachers use both affective and cognitive learning goals simultaneously and interchangeably. Because teachers prioritize them both, and research indicates a possible linear relationship between affective and cognitive learning (Christophel, 1990; Frymier, 1994; Rodriguez et al., 1996), these domains appear to be relevant and important outcome measures. In order to learn whether the time and bother of providing personalized educational experiences to our students is even warranted in terms of what is ostensibly the primary mission of higher education, it is critical to understand whether these experiences are predictive of affective and cognitive learning. RQ4: RQ5: Is perceived cognitive learning a function of student perceptions of personalized education? Is affective learning a function of student perceptions of personalized education? In order to test the hypotheses and answer the research questions, two developmental phases were employed. Each phase with its corresponding method and results are reported separately. Phase 1: Method Phase 1: A Typology of Instructor Behaviors that Create Student Perceptions of Personalized Education Phase 1 of this investigation was designed to identify the widest variety of teacher behaviors that elicit student perceptions of a personalized educational experience, and to answer RQ1. Participants. Phase 1 of this project involved a sample of 187 (98 males, 89 females) college students enrolled in various general education and communication classes at two universities. Approximately half (n 99) of the participants attended a large public university, and 88 attended a small, private comprehensive liberal arts university. The mean age of the sample was 19.5 years (range 1732). A variety of academic majors were represented by participants, including health sciences, fine arts, education, social or behavioral sciences, natural sciences, business, and the humanities. The number of years in college ranged from 1 to 5. Sixty-eight percent

9 Personalized Education 417 indicated that they were Euroamerican/White; 15% Latino/a; 6% Asian American; 4% Middle Eastern American; and 7% African American. Procedures. Students completed a questionnaire that began by asking them to think of one particular professor who has helped to create a personalized educational experience for them. Students were then prompted with the following information derived from existing literature about personalized education reviewed in Waldeck (2006). For example, this professor might have devised a unique assignment for you or your class based on your interests, invited you to participate in his/her research, been especially approachable outside of the classroom, or demonstrated special concern for your personal goals and plans, and so on. There is no definition of personalized education * please think of experiences that you consider to be personalized. Instrumentation. Employing the passage above as their response referent, student participants were asked to record what the professor did or said (in or out of the classroom) that contributed to their perceptions of a personalized educational experience. Of the 187 participants, 177 provided usable responses and reported a total of 373 discrete instructor behaviors (an average of 2.11 per student). All 373 behavioral descriptions were included in the five-stage content analytic procedure. In stage 1, the researcher and a trained assistant unitized the raw data into 373 discrete instructor behaviors. In stage 2, these coders independently coded each unit and placed them into conceptually similar categories. In stage three, both of the coders reread all of the behaviors within each of the categories to check for internal consistency. Tentative labels were then assigned to each category. In stage 4, coders reread the behavioral descriptions in each category, and made adjustments and revisions. Stage 5 involved two additional coders, also trained in content-analytic procedures, who recategorized a sample of units randomly selected from each of the categories. The percent of unit-by-unit agreement between the original coders and the two additional coders ranged from 78% to 100% depending on the particular category. Intercoder agreement among all coders, assessed by unit-by-unit agreement, was.88 (Landis & Koch, 1977). Phase 1: Results RQ1 asked What do college teachers say and do to create student perceptions of personalized education? Qualitative student reports of personalized educational experiences, collected in Phase 1 of this study and analyzed according to the procedures described above, revealed six categories of personalized educational experiences. (Table 1 provides characterizations of each of the categories inductively derived, along with corresponding frequencies and percentages obtained.) The first and most frequently cited characteristic that emerged is Instructor Shares His/Her

10 418 J. H. Waldeck Time Outside of Class for Student Needs (n 132; 35%). Students reported that they perceived personalized educational experiences as occurring when their instructors are accessible outside of the classroom in three ways: during working hours, such as office hours or times during the normal work day; during instructor s personal time, such as late at night or on a weekend, and via personal channels, such as instructor s private home telephone number or address. The next most frequently cited category that emerged from the data is Instructor Provides Counsel to Student (n 82; 21%) of three primary types: future-oriented (advice that pertains to the student s future either in or after school), personal (advice that relates to students personal problems not directly relevant to school, coursework, or future plans), and course-related (instructor counsel regarding course requirements and procedures). The third category, labeled Instructor Exhibits Competent Communication (n68, 18%), comprised characterizations of instructor behavior that students perceived as interpersonally adept, approachable, and immediate in and/or out of the classroom. Reports within this category described professors who know students names, communicate warmth and empathy, are able to small talk relevant to college students, and are dynamic communicators. The fourth category, Instructor Cultivates Social/ Personal Relationship with Student (n 47; 12%), describes experiences in which an instructor makes an effort to become friends with the student, and to remove or lessen the power and status differential that traditionally exists between professors and students. The fifth category, Instructor Exhibits Flexibility with Course Requirements (n26, 7%), represents student perceptions of instructors who allow changes in the syllabus, alternate project or paper topics, and other course activities, based on student interests*and makes these accommodations available to the entire class. The final category, Instructor Provides Special Favors (n18; 5%) describes studentreported personalized educational experiences in which instructors pull strings with other faculty members to get students into closed or full classes, make special exceptions to course requirements for certain students, but not others, and so on. Phase 2: Method Phase 2: Validating the Typology and Investigating the Relationship of Personalized Education to Other Instructional Communication Constructs Participants. Participants included 626 (267 males, 359 females) students from the same two universities described in the methodology for Phase 1. These two institutions were chosen for sampling purposes for two primary reasons. First, one of the universities emphasizes personalized education in its faculty and staff development initiatives, student recruitment materials, and overall mission statement. The second university chosen does not emphasize personalized education in these ways. As a result, the data would indicate whether this aspect of the school culture influenced student perceptions of personalized education. Second, both universities offered access to large general education populations for data collection

11 Personalized Education 419 Table 1 Characteristics of Personalized Educational Experience Characteristics Reported by Students Category Frequency Percentage 1. Instructor Shared Their Time Outside of Class for Student Needs Student perceives instructor as accessible outside of the classroom, regardless of whether the student was enrolled in a class with the instructor, and via various channels. A. Professor Accessibility During Working Hours (n61) She provided lots of extra help during office hours. B. Professor Accessibility During Personal Time (n 71) He spent six hours on a Saturday reviewing for the final with our class. He stopped and talked with me in the cafeteria about work and school even though I m not taking a class with him. She trusted me with her home telephone number. 2. Instructor Provided Counsel to Student Instructor provides advice or counsel to student. A. Future-Oriented (n37) He gave me advice about graduate school opportunities. B. Personal/Emotional (n28) Gave me emotional guidance when my mother was diagnosed with cancer. C. Course-Related (n17) She told us how to do the assignments properly and correctly to get full credit; we didn t have to figure it out on our own. 3. Instructor Exhibited Competent Communication Instructor engages in behavior that students perceive as interpersonally adept, approachable, and immediate in and/or out of the classroom. She knew everyone s name. He was always approachable and personable. She was dynamic and spoke so well. 4. Instructor Cultivated Personal/Social Relationship with Student Student perceives faculty member makes an effort to become friends with student, and to remove or lessen power and status differential. He came down off his pedestal and related to students as a friend on a personal level, rather than a superior. 5. Instructor Exhibited Flexibility with Course Requirements Instructor allows changes in syllabus, project or paper topics, and other course activities based on student interests. Allowed us the freedom to choose a project that was of interest to us personally. Gave credit for activities I chose to do outside of class as a class project, because it was relevant to my life. 6. Instructor Provided Special Favors to Student Student reports that instructor pulls strings or provided special favors to help student. He knew this subject was very difficult for me, and created easier tests for me, and made me complete fewer assignments. He got me into a class that was full by calling a friend of his. Total

12 420 J. H. Waldeck that would provide a diverse sample in terms of student age, gender, and academic majors. Approximately half (n312; 181 females, 131 males) of the participants attended a large public university, and 314 (147 females, 167 males) attended a small, private comprehensive liberal arts university. The mean age of the sample was years (range 1962). A variety of academic majors was represented by participants, including health sciences, fine arts, education, social or behavioral sciences, natural sciences, business, and the humanities. The number of years in college ranged from 1 to 6. Sixty-one percent indicated that they were Euroamerican/White; 13% Latino/a; 9% Asian American; 7% Middle Eastern American; 7% African American; and 3% other. Thus, the sample for Phase 2 was qualitatively similar to that which generated the basis for the typology in Phase 1. Because the issue under investigation was related to interactions with a particular instructor whom the student participant felt had facilitated a personalized educational experience, a number of questions assessed the frequency and nature of each participant s communication with that instructor. Twenty-seven percent reported that the personalized educational experience they referred to in this study was with their faculty advisor, 41% with an instructor from their major department (other than their advisor), and 32% with an instructor in another department besides their major. Student respondents also were asked to report their perception or knowledge of the rank of the instructor who provided them with a personalized experience: Thirty-seven percent reported that they believed their experience was with an untenured (assistant) professor, 21% with a full or part-time lecturer, 17% with a tenured (associate or full) professor, and 11% with a teaching assistant (TA). Fourteen percent indicated that they were not sure of the instructor s rank. 1 Eighty-eight percent of student participants reported that the instructor to which they referred in the study kept four or more office hours per week. The average number of interactions students reported having had with the instructor outside of formal classroom instruction (i.e., face-to-face, , phone) at the point in the semester during which the data were being collected (week 12 out of 15) was 3. Notably, not all of the respondents were enrolled currently in a class with the instructor they targeted (41% reported that they were currently enrolled in a class with the professor, and the remaining 59% indicated that they did not have a class with the target professor at the time data were collected). Of the students not enrolled in a course with the target professor, 54% reported contact during office hours, in another face-to-face setting, by phone, or by . Regardless of whether they were enrolled in a course with the instructor at the time data were collected, 68% reported that most of their face-to-face conversations outside of formal instruction lasted between 5 and 15 min, 20% less than 5 min, and 2% longer than 15 minutes. Ten percent reported that they had no conversations with this instructor outside of class time during the semester the data were collected. Procedures. Unlike the procedures employed in Phase 1, which were designed to maximize student reports of the characteristics of personalized education, Phase 2

13 Personalized Education 421 was structured to partially validate the categories obtained in Phase 1, and to determine if a conceptually meaningful factor structure underlies the original six categories. Quantitative data collected in this phase of the project were utilized to address the research questions and hypotheses. In Phase 2, an instrument was generated based on the dimensions of personalized education revealed in Phase 1 of the investigation in order to partially validate the typology. Students (N 626) were asked to respond to the instrument by thinking of one particular professor who has helped to create a personalized educational experience for you and indicating on 5-point scales the frequency with which that instructor engaged in the behavioral categories derived from student reports in Phase 1. These students also completed measures of teacher immediacy, mentoring functions, and extra-class communication, using the same instructor as a response referent. Each of these measures is described below. Personalized educational characteristics. Based on the six categories of studentreported teacher behaviors that create perceptions of personalized education, a pool of items was generated and given to a pilot group of college freshmen, juniors, and seniors who collectively discussed, edited, and contributed items to the list based on the category labels and descriptions. The resulting 30-item measure provided students with a number of things that teachers say and do that result in student perceptions of personalized education. Using a 5-point response Likert-type scale (5very often and 1not at all), students were asked to indicate how often the instructor they had chosen to reference for this questionnaire demonstrated these behaviors. Responses to this scale were submitted to exploratory factor analysis. Because the resulting factor structure of this measure provided the data to further answer RQ1, the factor analysis report appears below in the Phase 2: Results section. Extra-class communication. A 26-item inventory (Bippus et al., 2003) assessed students perceptions of their instructor s accessibility outside of formal instruction. Results of a principal-components analysis revealed a two-factor structure which accounted for 65.07% of the variance, with an interfactor correlation of.70. The first factor, social accessibility, had primary loadings ranging from.76 to.91; with a mean of (SD7.47), alpha reliability was established at.94. The second factor, physical accessibility, had primary loadings ranging from.69 to.87; with a mean of (SD9.37), alpha reliability was established at.86. Mentoring functions. A modified version of Ragins and McFarlin s (1990) Mentor Role Item (MRI) scale assessed student perceptions of career and psychosocial functions provided by the instructor they perceived as offering personalized educational experiences. The wording of the original MRI was modified to assess respondent experiences in an educational setting, rather than a corporate or organizational context. The Likert-type scale included 32 items measuring six dimensions of career functions and five dimensions of psychosocial functions. Student responses were submitted to principal-components analysis, which indicated

14 422 J. H. Waldeck a two-factor solution. Thirteen items were split across the two factors and failed to meet a 50/30 criterion; therefore, these 13 items were eliminated from subsequent analyses. Responses to the remaining 19 items resulted in a two-factor solution (57% of the variance accounted for; interfactor correlation.30). These factors were consistent with Ragins and McFarlin s original two functions. Factor One, Psychosocial Functions (M 44.70, SD10.00), consisted of 10 items with an alpha reliability of.94, and Factor Two, Course/Career Functions (M 52.27, SD7.49), consisted of nine items with an alpha reliability estimate of.89. Teacher immediacy. Teacher immediacy was assessed via students responses to a list of 14 nonverbal (Richmond, Gorham, & McCroskey, 1987) teacher behaviors used previously in numerous studies. Students were asked to indicate the frequency of teachers use of each immediacy behavior on a scale of 0 (never) to 4 (very often). Consistent with previous investigations (Christophel, 1990; Frymier, 1994), alpha reliability was estimated at.86 (M 34, SD6.1). Affective learning. Affective learning was assessed using Andersen s (1979) affective learning instrument which measures students attitudes on five dimensions: (1) the course, (2) the subject matter, (3) the instructor, (4) their behavioral intentions of engaging in behaviors taught in the class, and (5) taking additional classes in the subject matter, using a series of 7-point semantic differential scales. Student responses to these scales were submitted to a factor analysis with an oblique rotation and forced five-factor solution. For the five-factor solution, items in each set of scales had their primary loadings on the intended factor (interfactor correlation.68). Given the high interfactor correlations obtained, a two-factor solution, again with oblique rotation, was computed, indicating an affective factor and a behavioral commitment factor, with an interfactor correlation of.66. The single-factor, unrotated solution provided the most parsimonious explanation of these data; item loadings ranged from.61 to.93, variance explained 64% (M 104; SD 31.87). The alpha reliability estimate for the single factor solution was.90. Although some researchers argue that evaluation of the teacher is not part of the affective learning construct (Mottet & Beebe, 2006), the items pertaining to teacher evaluation were retained for this study, given the high overall reliability of the scale. Perceived cognitive learning. Cognitive learning was measured by a two-item selfreport instrument (Richmond, McCroskey, Kearney, & Plax, 1987). The first item asked students, On a scale of zero to nine, how much did you learn in the class you had with this instructor, with zero meaning you learned nothing, and nine meaning you learned more than in any other class you ve had? The second item asked, How much do you think you could have learned in this class if you had had the ideal instructor? By subtracting the score on the first item from the score on the second item, a learning loss score was derived. This measure of perceived learning has been employed widely in communication research (Chesebro & McCroskey, 2000, Christophel, 1990; Frymier, 1994; Rodriguez et al., 1996; Witt & Wheeless, 2001).

15 Personalized Education 423 Chesebro and McCroskey established the validity of this instrument by demonstrating its association with cognitive recall of learned information. Because of its nature, no alpha reliability test can be computed for the learning-loss measure. Phase 2: Results Recall that Phase 1 of this investigation addressed RQ1, What do college teachers say and do to create student perceptions of personalized education? qualitatively. Phase 2 was designed to shed light on the validity of the typology derived in Phase 1 through the development of an instrument reflecting each of the six inductively derived categories. Responses to the 30-item Likert-type personalized education measure were submitted to exploratory factor analysis. With eigenvalues greater than 1.0, results indicated an initial three-factor solution with interfactor correlations ranging from.31 to.41. Using a 50/30 criterion, three items were dropped, and the remaining items were submitted again to factor-analytic procedures. The resulting three-factor solution accounted for 62% of the variance. Items loading on individual factors were conceptually similar and provided evidence of a meaningful underlying factor structure for the qualitative responses obtained in Phase 1. Factor 1, labeled Instructor Accessibility, consisted of 11 items. Item loadings ranged from.63 to.93, and alpha reliability was estimated at.91 (M44; SD11.7). Items represented in this factor revealed instructor efforts to be physically and socially accessible to students in a variety of locations, across several multiple communication channels, and during normal working hours as well as what would normally be considered the instructor s private time to discuss students personal and professional issues. The second factor, labeled Interpersonal Competence, consisted of 7 items. Item loadings ranged from.78 to.94, with alpha reliability estimated at.86 (M 24.5; SD4.82). This category encompassed teacher efforts to communicate friendliness, warmth, approachability, and dynamism to students, and communicate in ways that promote teacherstudent equality and friendship. Factor 3, labeled Course-Related Practices, consisted of 9 items. Item loadings ranged from.61 to.84, and alpha reliability was estimated at.89 (M 20.3; SD2.94). Items on this factor reflected instructor behaviors related to course design and management that create perceptions of personalization, including designing course activities based on student input, changing the syllabus based on student suggestions, and making exceptions to course requirements for particular students. Table 2 lists the items that were retained for each factor. An examination of item means for each factor revealed that students reported teachers engaging in Instructor Accessibility most often (item mean 4.27), followed by Interpersonal Competence (item mean3.77), and lastly Course-Related Practices (item mean2.10). RQ2 asked, Do perceptions of personalized education vary as a function of school characteristics? A one-way analysis of variance was computed, revealing no significant difference on perceptions of personalized education between students enrolled in the large public university with no explicit drive for personalized learning

16 424 J. H. Waldeck Table 2 Personalized Education Scale Items Loading Factor 1: Instructor Accessibility This instructor: Has an adequate number of office hours to provide extra help for.86 students. Is available when I have personal issues bothering me..72 Is willing to offer extra help in his or her office, outside of class. Takes time to advise me on particular courses and instructors I.78 should take. Socializes with me..61 Spends time talking with me about non-professional issues..73 Meets with me at various places on campus during the week..63 Takes time to give me advice about my future plans and goals..93 Meets with me to give advice when I am upset..71 Factor 2: Course-Related Practices This instructor: Designs course activities based on students special interests..86 Understands that I have requirements that are different than the.78 rest of the class. Changes the syllabus based on student suggestions..84 Would pull strings to help me through if I needed him/her to..61 Provides class activities that are unique or different from my other.84 professors. Promotes a lot of interaction in his/her classroom..81 Makes exceptions to course requirements that aren t available to.80 the rest of the class. Encourages or requires students to work together during the.84 course. Does special favors to help me with his/her class..80 Factor 3: Instructor Interpersonal Competence This instructor: Is a competent communicator..94 Is warm and friendly..91 Is approachable..86 Doesn t act like a superior to me..79 Refers to me by name..87 Relates to me well, without pulling rank..78 Is a dynamic communicator..82 and the smaller, private university that includes personalized education in its mission statement, F(1, , p.05). Pearson product-moment correlations were computed to test H1 and H2, that student perceptions of teacher immediacy and extra class communication are positively related to student perceptions of the personalized education construct. Indeed, teacher nonverbal immediacy was positively related to personalized education (r.57, pb.0001), as was extra-class communication (r.64, pb.0001). Thus, H1 and H2 were supported. H3 and RQ3 pertained to the relationship between students perceptions of personalized education and instructor mentoring. H3 predicted that student

17 Personalized Education 425 perceptions of personalized education are positively related to mentoring. Results of a Pearson product-moment correlation indicated that scores on the personalized education measure and the overall mentoring function scale are positively related (r.58, p B.05). RQ3 asked whether course/career or psychosocial mentor functions are reported more frequently by students who experience personalized education. A paired samples t test indicated that the difference between the means reported for course/career (52.27, SD 7.49) and psychosocial functions (44.70, SD10.00) are significant (t(625) 15.14, p B.0001). RQ4 and RQ5 explored the relationships between student perceptions of personalized education and cognitive and affective learning. The obtained Pearson correlation between perceived personalized education and learning loss was.42 (p B.0001). The negative correlation indicates that increased perceptions of personalized education are associated with more cognitive learning (less loss). A similar Pearson correlation indicated a substantial positive relationship (r.66, p B.0001) between student perceptions of personalized education and affective learning. Table 3 provides the means, standard deviations, and alpha reliability estimates, and Table 4 provides the simple correlations among all the variables examined in this study. Discussion and Implications RQ1 asked, What do college teachers say and do to create student perceptions of personalized education? To answer this question, both qualitative and quantitative reports were obtained. First, a sample of students from two universities provided open-ended accounts of an experience that they perceived as highly personalized. From these data, a typology was inductively derived (see Table 1) that revealed six Table 3 Means, Standard Deviations, and Reliabilities for All Variables Variable M SD a Personalized Education 1. Instructor accessibility Instructor interpersonal competence Instructor course-related practices Extra-Class Communication 4. Social accessibility Physical accessibility Mentoring 6. Psychosocial mentoring Career mentoring Teacher Immediacy 8. Nonverbal immediacy Student Perceived Learning 9. Affective learning Learning loss na

18 426 J. H. Waldeck Table 4 Intercorrelations Among Variables Variable PE/Accessibility PE/Comm. competence PE/Course practices ECC/Social accessibility ECC/Physical accessibility Psychosocial mentoring Career mentoring Nonverbal immediacy Affective learning Learning loss Note. All correlations reported are significant at p B.05. categories of instructor behaviors that collectively represent students perceptions of personalized education. The most frequently cited category was Instructor Shares His/Her Time Outside of Class, followed by Instructor Provides Counsel to Student. Taken together, these two categories are striking because they reveal the large amount of time that the instructors reported on in this study are spending with their students, and the depth of the counsel they are providing. One student reported that their instructor spent 6 hr on a weekend reviewing for an exam with the class. Other students reported lengthy conversations with their instructors of up 23 hours, during which they disclosed highly personal information about family crises, health conditions, and relationship troubles to the instructor. Instructors represented by their students in this sample provide home phone numbers and cell-phone numbers, and students report using them. One student commented that the instructor told the class that anyone could call him at any hour. So, instructors perceived by students as creating a personalized education are offering students a great deal of advice on a range of topics, and spending a lot of time doing so, including what would normally be considered private time (weekends and evenings). The third most frequently cited category was Instructor Exhibits Competent Communication. Teachers who cultivate student perceptions of personalized education practice personalized communication in and out of the classroom*engaging in immediate behavior, demonstrating excellent public speaking and facilitation skills, and making their communication relevant to students. In other words, in order to create a personalized experience for students, teachers must practice the skills that instructional communication research has emphasized for years. Fourth, students reported that their most personalized teachers cultivate social and personal relationships with students, often by deemphasizing the traditional power imbalance between instructors and students. Instructors create personalized education by exhibiting flexibility with course requirements, as well. Although no instances of Personalized Education Plans or diagnostic assessment were reported by the students in this sample, a small percentage reported that their personalized educational experience involved an instructor who allowed changes in the syllabus based on student interest,

19 Personalized Education 427 or elicited student input on course planning. Finally, a very small percentage of students indicated that their personalized experience involved an instructor doing a special favor for them. For instance, one student reported, He knew that math was really challenging for me, and let me take fewer tests than the rest of the class. The implications of these student-reported forms of personalized education are numerous. First, teachers who wish to create personalized experiences for students should be aware of student expectations, and be willing to reshape these expectations if they are inconsistent with what the instructor is willing to do. Students expect their instructors to be on call, highly accessible, and committed to nurturing the student through personal and professional/educational challenges. This typology reveals that personalized education, as students define it, can be incredibly time-consuming and involves relationship building with students that goes beyond the superficial friendliness most teachers enjoy with students. From both an administrative and instructional perspective, this aspect of the study illustrates a major concern. Students appear to expect that their instructors have and are willing to share enormous amounts of their time to deal with students personal problems and course issues that go above and beyond the traditional realm of teacher concern. The investment of this proportion of a teacher s time in activities for which they are not formally rewarded is a compelling concern for many tenure-track faculty and should concern university administrators, as well. Going above and beyond the call of duty, as students appear to expect us to do, takes away from our time to do research, prepare class materials, and participate in university governance. If university officials agree that student expectations for personalized education, as they are defined in this study, are realistic and aligned with their institution s mission and goals, some changes in the faculty reward structure are in order. Perhaps the teaching requirement of most faculty contracts should be redefined to address the huge scope of this professorial responsibility. Second, these data reinforce the critical importance of instructional communication competence. Not only are teachers who practice behaviors like immediacy, warmth, and empathy rated as better teachers who facilitate better student learning, but they are perceived by students as creating personalized learning experiences. Interestingly, the student reports did not focus on how teachers manage their classrooms or package course material, but spotlight the interpersonal nature of teacherstudent interaction. Although the literature indicates that personalized education involves creating collaborative learning experiences and other unique classroom activities, these were reported minimally by the students in this sample. Only 12% of these student reports involved instructors designing curriculum based on individual student needs or interests*and of this percentage, 5% are somewhat disturbing. These accounts illustrate instructors doing special favors for one student that are not offered to the entire class, making special exceptions to rules like course prerequisites, number of assignments, and so forth. Instructors should be cautious about favoritism and well-meaning attempts to aid individual students. The outcomes of such arrangements may compromise student learning outcomes for the student involved as well as the rest of the class. Further, such special treatment

20 428 J. H. Waldeck may contribute to the narcissistic I m special syndrome that psychology researchers report is characteristic of college students in 2007 (Twenge, 2006) and make the jobs of other instructors who endeavor to create a fair and equitable classroom more difficult. Along the same lines, it should be noted that the student responses in this study for the most part reflect the attitude that personalized education is a product that a teacher delivers *not the idealized cocreated outcome of teacherstudent and studentstudent interactions that is written about in much of the education theory literature on this construct. Next, a measure of personalized education was created to indicate the partial validity of the typology derived in Phase 1 of the investigation and to determine what underlying factor structure existed for these data. After the data were reduced, three dimensions emerged, reported here in order of frequency: Instructor Accessibility, Interpersonal Competence, and Course-Related Practices. As expected, after analyzing the first sample s qualitative accounts of what their teachers do to create perceptions of personalized education, instructor accessibility emerged as the leading factor in students recollections of personalized experiences, and instructor interpersonal competence was extremely important, as well. The message to instructors is clear: provide plenty of opportunities for extra-class interaction with students, and practice competent communication that demonstrates warmth, goodwill, friendliness, and sincerity. Similar to the open-ended accounts, students appear to be less impressed with the course-related practices of their instructors as examples of personalized education than the quality of the interactions they have with those instructors. RQ2 examined whether or not student perceptions of personalized education are a function of the school culture in which they work and learn. Recall that one university represented in this sample promotes personalized education in its mission statement, student recruitment, and faculty development initiatives, while the other does not. Surprisingly, there were no statistical differences between student responses from both schools. Students were asked to report specifically on a teacher who had created a personalized experience, so these data are not a fair representation of how much personalized education takes place at either school; however, they reveal that the two groups of participants characterize the nature of personalized education the same way. Policies, public relations, and mission statements do not appear to influence the behaviors of teachers toward the goal of personalized education; nor do they influence the way students define the nascent construct. H1 was designed to test the relationship between teacher immediacy and personalized education. The substantial relationship obtained indicates that a great deal of what educational theorists and university marketing specialists refer to as personalized education is really what instructional communication researchers have emphasized for two decades. Moreover, a test of H2 examined the relationship between extra-class communication and personalized education, and similarly demonstrated a strong relationship and considerable conceptual overlap. These results suggest, again, that students appreciate and respond to teachers with strong interpersonal skills and who are accessible. For instance, one respondent indicated that the time (my instructor) took during scheduled and unscheduled office hours

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