Professor–Student Rapport and Perceived Autonomy Support as Predictors of Course and Student Outcomes

Professor–Student Rapport

The various interpersonal exchanges between the professor and the student in and out of the classroom are central in promoting feelings of rapport in the relationship (Frisby & Martin, 2010; Jorgenson, 1992; Ramsden, 2003). Rapport is defined as the feeling of mutual trust and understanding developed between two individuals in light of numerous and frequent interpersonal and enjoyable interactions across different settings (Carey, 1986; Frisby & Martin, 2010; Gremler & Gwinner, 2000; Lowman, 1995; Ryan et al., 2011). When specifically applied to professor–student relations, it is construed as “a perceived outcome based on students’ observed instructor communication” (Frisby, Mansson, & Kaufmann, 2014, p. 110). Recognizing the importance of perceived rapport for learning (Frisby, Berger, Burchett, Herovic, & Strawser 2014; Rogers, 2015) and the fact that students consider rapport a key component of an effective professor (Faranda & Clarke, 2004), scholars have devoted significant attention to identify behaviors and factors that promote this feeling in and outside the classroom (Benson et al., 2005; Buskist & Saville, 2004; Cashin, 2010; Davis & Buskist, 2006; Fleming, 2003; Forsyth, 2003; Lowman, 1995; Wilson, 2006; Wilson & Ryan, 2012). For instance, this line of research suggests that being approachable, encouraging, getting to know students, and maintaining frequent contact that shows concern are behaviors that promote rapport.

Recently, Wilson and Ryan (2013) have shown that rapport encompasses two dimensions: professor perception and student engagement. While the former encompasses characteristics of the professor, the latter taps into behaviors promoting a positive classroom experience. There is a substantial body of research showing that a students’ perception of rapport with their professors are a reliable and robust predictor of course and learning outcomes. For instance, students perceiving rapport with their professors report higher levels of liking and professor effectiveness (Buskist & Saville, 2004; Frisby & Martin, 2010; Perkins, Schenk, Stephan, Vrungos, & Wynants, 1995; Richmond et al., 2015; Wilson & Hackney, 2006; Wilson & Ryan, 2013; Wilson, Ryan, & Pugh, 2010). They also report higher levels of learning and engage in a variety of proacademic behaviors (attendance, participation) in class (Benson et al., 2005; Buskist & Saville, 2004; Frisby & Martin, 2010; Frisby & Myers, 2008; Frisby et al., 2014; Rogers, 2015; Ryan et al., 2011; Weaver & Qi, 2005; Wilson et al., 2010). As for dimensions of rapport, Wilson and Ryan (2013) have reported that the student engagement component of rapport predicted not only higher course ratings but also positive attitudes toward the teacher. They also showed that student engagement uniquely predicted attendance, perceived amount learned, expected and actual final grades. Although a discussion of why and how rapport yields positive outcomes is beyond the scope of this study, suffice it to state that rapport promotes various positive outcomes probably because it reduces negative interpretations of course requirements and classroom behaviors (Wilson & Hackney, 2006) and creates a context in which students feel that their basic psychological needs (e.g., competence, relatedness) are met in their relationships with their professors (Filak & Sheldon, 2008). Collectively, there is consistent evidence showing students’ perceptions of rapport with their professors are vital for a wide range of course and student outcomes.

Autonomy Support

Another key experience that could develop within faculty–student interactions pertains to the degree that one’s autonomy is supported in the classroom. Autonomy support is defined as “when an individual in a position of authority (e.g. an instructor) takes the other’s (e.g. a student’s) perspective, acknowledging the other’s feelings, and providing the other with pertinent information and opportunities for choice, while minimizing the use of pressures and demands” (Black & Deci, 2000, p. 742). Stefanou, Perencevich, DiCintio, and Turner (2004) suggest that there are three types of autonomy support: organizational, procedural, and cognitive. According to the research, organizational autonomy support refers to ways to structure the environment, such as implementing rules and due dates. Procedural autonomy support refers to forms of teaching, such as having a say in how material is presented (e.g., videos, slideshows, assignments). Cognitive autonomy support refers to means of learning, such as encouraging free discussion. These strategies can be implemented at the high school and the undergraduate level. For purposes of this study, we will focus mainly on cognitively based practices of autonomy support, as this construct is the one mainly associated with positive student outcomes (Stefanou, Perencevich, DiCintio, & Turner, 2004). Specifically, teachers can practice listening carefully, encouraging efforts, praising improvements, responding positively and constructively to questions, communicating acknowledgment of differing points of view, and allowing for freedom for students to work in the way that works best for them (Deci et al., 1982; Reeve, 2006; Reeve & Jang, 2006).

Autonomy support is a key component of self-determination theory (Deci & Ryan, 2000; Ryan & Deci, 2000). Understanding how autonomous actions and perceptions of autonomy support in different domains relate to different indices of well-being has been a main agenda of the research in this field (Deci & Ryan, 2008). According to theory and empirical research, perception of autonomy support in any given relationship will promote greater levels of well-being because the recipient of autonomy support is likely to satisfy basic psychological needs in the relationship (Deci & Ryan, 2008; Deci, La Guardia, Moller, Scheiner, & Ryan, 2006; Deci et al., 2001). Decades of research consistent with this premise have shown that perceived autonomy support is related to motivation and various indices of well-being in different relationships such as parent–child interactions (Abad & Sheldon, 2008; Clark & Ladd, 2000; Grolnick & Ryan, 1989) and friendships (Demir, Simsek, & Procsal, 2013). Attesting to the utility of this theory, this idea has been applied to the educational setting and professor–student autonomy support has been shown to promote pro-academic behaviors (e.g., persistence, self-determined motivation) and positive course outcomes (e.g., increased test performance, enhanced learning, and overall GPA; Bonneville-Roussy, Vallerand, & Bouffard, 2013; Filak & Sheldon, 2008; Overall, Deane, & Peterson, 2011; Sheldon & Krieger, 2007; Simon, Aulls, Dedic, Hubbard, & Hall, 2015; Steele & Fullagar, 2009; Stefanou et al., 2004; Vansteenkiste, Simons, Lens, Sheldon, & Deci, 2004).

The importance of autonomy support in the classroom has been shown across different samples and contexts. For example, in a study conducted with law school students, Sheldon and Krieger (2007) have shown that students who perceived their professors as supporting their autonomy support within the classroom had higher GPAs, higher American Bar Association exam results, and more motivation when finding a job upon graduation. Filak and Sheldon (2008) also showed that perceived autonomy support predicted more positive course evaluations. In another study, Black and Deci (2000) have shown that professors who implemented autonomy-supportive practices in their course have promoted their students’ success. Specifically, students’ perceptions of autonomy support from their instructors predicted improvement in course grades, increased amount of time spent studying, and more thorough competence of the course material as a whole. All in all, there is consistent evidence showing that perceived autonomy support from a professor promotes positive course evaluations, student learning, and success.

The Current Study

Although professor–student rapport and perceived autonomy support both have been shown to be related to similar course and student learning outcomes (e.g., Bonneville-Roussy et al., 2013; Overall et al., 2010; Richmond et al., 2015; Sheldon & Krieger, 2004; Simon et al., 2015; Soenens & Vansteenkiste, 2005; Williams & Deci, 1996; Wilson & Ryan, 2013), they are different. Whereas rapport involves creating a positive classroom environment through building a sense of connection that increases student engagement, autonomy support promotes choice and supports curiosity. To make the contrast even more clear, professors with high levels of rapport and low levels of autonomy support might engage in relationally oriented practices like getting to know their students on an individual basis through required one-on-one meetings (Filak & Sheldon, 2008) but may not engage in practices such as providing three final paper prompt options and allowing the students the freedom to choose which option they would like to pursue (Reeve & Jang, 2006). On the other hand, a professor who displays high levels of autonomy support but low levels of rapport may have a class with hundreds of students and is therefore perceived as unapproachable because there are too many students to be able to foster connectedness with everyone (Wilson & Ryan, 2012). However, the same professor may allow each student to pick whether they want to complete a project alone or in teams and recruits teaching assistants to provide specific and constructive feedback (Deci et al., 1982).

Professor–student rapport and autonomy support have received significant attention from scholars resulting in two separate lines of research. Yet no prior research has studied both constructs and their implications for student learning simultaneously. The current study addressed this gap in the literature and investigated the unique and combined effects of rapport and autonomy support on several course and student outcomes. In order to provide data that could be comparable with past research, we had six outcome variables. As for course outcomes, our focus was on ratings of professor effectiveness and perceptions of the course. As for student outcomes, we focused on perceived amount learned, number of classes missed, expected, and actual final grades. In light of theoretical arguments and past research, it was predicted that both overall rapport and autonomy would be positively associated with every outcome variable except the number of classes missed, for which we predicted a negative relationship. We had one research question, and it pertained to the most important variable predicting course and student outcomes: Are overall rapport and autonomy support equally important or one is more important than the other in predicting the outcome variables?

Method

Results

Prior to any analyses, the data were screened following the guidelines outlined by Tabachnick and Fidell (2007). There were missing data for the perception of the course (18%) and actual grades (28%) variables. As for the former, not every student provided a rating for this measure. As for the latter, we could not obtain actual grades for every participant. The missing data with the exception of these two variables were less than 1%. Moreover, data were missing completely at random in the entire data set, χ² (572) = 592.19, p = .27 (Little, 1988). Missing values were not estimated since actual grades represent a key objective outcome variable, and in general, our sample was large enough for analyses including perception of the course.

Investigation of the scores on the study variables revealed that they were negatively skewed (e.g., autonomy support: skew = −.94, SE = .12). The square root transformation of the variables addressed this problem such that they were all normally distributed (e.g., autonomy support: skew = −.22, SE = .12). The analyses conducted with the transformed variables were very similar to those obtained with the untransformed variables (e.g., same pattern of results and interaction). Thus, we decided to present the untransformed results since they are easier to interpret (e.g., when interpreting the unstandardized regression coefficients, a one unit increase on any given measure would correspond to a one unit change in the associated outcome).

The ratio of sample size to predictors was not an issue across the analyses. However, there were a total of 10 univariate (z = ±3.29) and multivariate outliers (Tabachnick & Fidell, 2007), and these cases were removed from the analyses. Thus, the sample size for the analyses reported ranged from 289 (actual grades) to 402 (entire sample) across the analyses. The reason for this discrepancy is that a small group of student did not provide their IDs (n = 24), and we did not receive the actual grade rosters from a few professors (n = 89). Finally, the values for tolerance, variance inflation factor (VIF), and the condition index obtained from the regression analyses indicated that multicollinearity was not an issue (e.g., all VIFs < 3.4, tolerances > .30; and condition indices < 3; Menard, 1995; Myers, 1990; Tabachnick & Fidell, 2007).

The correlations between the study variables are reported in Table 1. Since gender was not related to any of the study variables, it was not controlled for in the analyses. Consistent with our predictions, autonomy support and overall rapport were significantly associated with the course and student learning outcomes to varying degrees in the expected directions.

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Table 1.

Descriptive Statistics and Correlations Between the Study Variables.

Measures	1	2	3	4	5	6	7	8	9	10	11	M	SD
1. Gender	—
2. Autonomy support	−.07	—										5.59	1.11
3. Perception of teacher	.01	.78**	—									4.46	.60
4. Student engagement	−.03	.78**	.78**	—								4.10	.82
5. Overall rapport	−.01	.83**	.95**	.94**	—							4.32	.65
6. Professor effectiveness	−.02	.81**	.76**	.79**	.82**	—						4.34	.76
7. Perception of the course	.00	.65**	.56**	.73**	.68**	.72**	—					4.08	.84
8. Classes missed	.03	−.17**	−.17**	−.22**	−.21**	−.21**	−.20**	—				1.78	1.63
9. Perceived amount learned	.01	.47**	.24**	.53**	.51**	.63**	.66**	−.13**	—			4.20	.91
10. Expected final gradea	.01	.26**	.21**	.23**	.24**	.29**	.41**	−.26**	.34**	—		—	—
11. Actual final grade	−.03	.21**	.12	.16**	.15*	.24**	.29**	−.28**	.21**	−.76**	—	85.48	8.84

^aThe frequency of expected final letter grades were A (46.4%), B (39.7%), C (12.7%), D (1%), and F (.2). This variable was recoded in the analyses such that higher letter grades reflect higher values.

*p < .05. **p < .01.

Although the students were nested within classes, preliminary analyses suggested classes did not differ more than would be expected by chance on any outcome variables, and additionally all intraclass correlation coefficients were very small, indicating no meaningful average difference across groups on outcome variables. Thus, we conducted six hierarchical regressions, one for each outcome variable. Variables were centered before the analyses. In the regressions, overall rapport and autonomy support were entered in the first step. The second step included the interaction between the two variables. Since only one of the six interactions was significant, we only report the findings obtained in the first step (Table 2). As explained earlier, our research question aimed to identify whether one variable (autonomy or rapport) was more important than the other in predicting the outcome variables. In order to address this important question, we relied on β weights and semipartial correlations obtained in the regression analyses. In its squared form, semipartial correlation is the percent of full variance uniquely accounted for by the independent variable in the dependent variable when other variables are controlled. Squared semipartial correlation has been suggested to be the more useful measure of the importance of an independent variable (Tabachnick & Fidell, 2007). Even though there are different ways to assess importance (e.g., dominance analyses, epsilon), research has shown that these two indices are in perfect agreement with other relative importance indices (Baltes, Parker, Young, Huff, & Altmann, 2004). Thus, our reliance on β weights and squared semipartial correlations in identifying the strongest predictor is justified.

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Table 2.

Regression Analyses Predicting Course and Student Outcomes From Overall Rapport and Autonomy Support.

	R 2	Semipartial2	B	β	95% CI	t
Professor effectiveness	.72
Rapport		.07	.69	.49	[.46., 78]	10.36***
Autonomy support		.05	.34	.40	[.21, .34]	8.58***
Perception of the course	.49
Rapport		.06	.64	.44	[.43, .86]	6.01***
Autonomy support		.03	.24	.29	[.12, .37]	3.92***
Classes misseda	.04
Rapport		.02	−.54	−.21	[−.97, −.11]	−2.45*
Autonomy support		.00	.02	.01	[−.23, .27]	.15
Perceived amount learned	.27
Rapport		.05	.58	.40	[.36, .80]	5.12***
Autonomy support		.01	.12	.14	[−.01, .25]	1.82
Expected final grade	.07
Rapport		.00	.09	.06	[−.18, .35]	.65
Autonomy support		.01	.20	.22	[.04, .35]	2.52**
Actual final grade	.04
Rapport		.00	.02	.01	[−.28, .31]	.92
Autonomy support		.03	.17	.20	[.01, .34]	2.05**

^a Controlling for attendance policy that was part of the course grade. R ² value indicates change in explained variance.

*p < .05. **p < .01. ***p < .001.

Student Learning Outcomes and Number of Classes Missed

The study variables explained 27% of the variance in perceived amount learned in the course, F(2,394) = 72.69, p < .001. As seen in Table 2, rapport was more important than autonomy in predicting the amount learned. In our sample, four courses had an attendance policy that was part of the course grade (N = 118). In an attempt to examine whether this influenced classes missed, we created a dummy-coded variable (0 = no attendance policy-grade; 1 = attendance policy-grade). This variable was entered in the first step of the regression as a control variable and accounted for 2% of the variance, F (1, 399) = 6.74, p < .01. The second step including rapport and autonomy was also significant and accounted for an additional 4% of the variance in missed classes, F (3, 397) = 8.63, p < .001. Rapport was the only significant predictor of missed classes. The interaction term entered in the third step was significant, F (4,396) = 8.21, p < .001, and accounted for an additional 2% of the variance. This interaction was plotted following the guidelines of Aiken and West (1991). Consistent with the main analyses, the interaction highlighted the importance of rapport. Specifically, as seen in Figure 1, students reported higher levels of missed classes when both autonomy and rapport were low. However, students reported the lowest level of missed classes at high levels of rapport even when autonomy support was low.

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Figure 1.

Interaction between overall rapport and autonomy support in predicting classes missed.

Dimensions of Rapport

As explained earlier, professor–student rapport consists of two factors: perceptions of professor and student engagement. We conducted additional analyses to investigate whether both components of rapport were equally important or one dominated the other for variables when overall rapport was more salient than autonomy support in the main analyses. Both factors were simultaneously entered into the regression. In predicting professor effectiveness (Table 3), both components emerged as significant predictors, F (2,399) = 417.72, p < .001. However, student engagement was more important than professor perceptions. Likewise, student engagement emerged as the only significant predictor of perception of the course, F(2,325) = 183.32, p < .001, classes missed, F (2,398) = 10.59, p < .001, and perceived amount learned, F (2,394) = 78.89, p < .001.

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Table 3.

Regression Analyses Predicting Course and Learning Outcomes from Rapport Subscales.

	R 2	Semipartial2	B	β	95% CI	t
Professor effectiveness	.68
Perceptions of professor		.05	.44	.35	[.30, .58]	7.73**
Student engagement		.11	.58	.52	[.48, .68]	11.42**
Perception of the course	.53
Perceptions of professor		.00	−.04	−.03	[−.23, .15]	.41
Student engagement		.56	.88	.75	[.73, 1.02]	12.11**
Classes missed	.05
Perceptions of professor		.00	.03	.02	[−.23, .29]	.218
Student engagement		.02	−.29	−.24	[−.48, −.10]	−3.05*
Perceived amount learned	.29
Perceptions of professor		.00	.09	.06	[−.12, .30]	.85
Student engagement		.10	.56	.49	[.41, .71]	7.17**

Note. CI = confidence interval.

*p < .01. **p < .001.

Discussion

The findings of the current investigation suggest that professor–student rapport and perceived autonomy support from the professor both make meaningful contributions to the prediction of various course and student outcomes. However, rapport was more important than autonomy for ratings of professor and the course, attendance, and perceived amount learned. On the other hand, autonomy support was the only predictor for expected and actual course grades.

Although rapport and autonomy support are two constructs reflecting different theoretical perspectives and traditions (Jorgenson, 1992; Williams & Deci, 1996), they were highly correlated with each other. Perhaps this conceptual overlap is inevitable since both address the quality of the learning environment and various behaviors that could foster this goal. Nevertheless, our findings showed that even though they were similarly related to various outcomes, they had differential predictive abilities when they competed for variance. This clearly suggests these constructs, although overlapping to some extent, are different from each other such that they predict different aspects of student learning.

Rapport emerging as the salient experience and strongest predictor of ratings for professor effectiveness and perception of the course is in line with past research (Richmond et al., 2015; Rogers, 2015). Rapport was also the strongest predictor of perceived amount learned in the course, and this is consistent with previous studies (Rogers, 2015; Wilson & Ryan, 2013). Although rapport information was only collected at the end of the semester, recent research suggests rapport ratings at the beginning, middle, and end of the course all share significant, positive relationships with course grades. Notably, the trajectory of rapport across the semester is an important predictor of course outcomes such that students who report declining rapport also report lower grades (Lammers, Gillaspy, & Hancock, 2017). Additional analyses revealed that it was the student engagement component of rapport that influenced these outcomes. Clearly, professors should be cognizant of strategies they could implement that could promote a sense rapport with their students if they are striving for positive evaluations of their teaching and courses and aiming to promote student learning. Fortunately, the literature on building rapport in the classroom provides excellent tips as how to develop rapport with students (Buskist & Saville, 2004; Ryan et al., 2011; Teven, 2007). These suggestions range from positive classroom practices (e.g., clear lectures and frequent contact) to various interpersonal behaviors (e.g., being approachable and enthusiastic while creating a respectful learning atmosphere). Identifying which sets of practices and behaviors strongly promote rapport is a valuable research agenda. It is also important to note that data for this study came from medium-sized (25–70) classrooms and did not include large-sections classes typical of 100-level courses. Thus, it remains to be seen whether rapport confers similar benefits in large classrooms.

There was an interesting interaction between rapport and autonomy support in predicting the number of class days missed. Specifically, students were more likely to miss class when they experienced less support and rapport from their professors. However, they were more likely to attend class when they perceived higher levels of rapport with their professors regardless of perceived autonomy support from them. Although our measure of number of classes missed was not objective, and variables and the interaction term accounted for only 6% of the variance, we consider this an important finding. It is safe to argue that class attendance, which is a critical component of learning and success (Credé, Roch, & Kieszczynka, 2010), might be fostered to a greater extent by establishing rapport with students (Benson et al., 2005; Buskist & Saville, 2004; Wilson & Ryan, 2013). Creating an engaging classroom while displaying differing levels of compassion and enthusiasm toward the subject matter and maintaining a personal relationship with students might motivate them to attend class regularly. Although implementing these practices, especially getting to know the students at a personal level (e.g., learning one unique thing about them, their research interests), might be relatively easier in small and medium-sized classrooms, it certainly is a challenge in large classrooms.

Regardless of class size, one simple message that Legg and Wilson (2009) suggest to enhance professor–student rapport is to establish such rapport as early as possible. Namely, going beyond relying on the first week to engage in discourse, they recommend the use of a warm introductory e-mail prior to the first day of class (Legg & Wilson, 2009). Their study showed a relation between a welcome e-mail 1 week before the first day of class and increased motivation and student retention, as well as improved perception of the course and instructor (Legg & Wilson, 2009). Keeping in mind the association between rapport and attendance in the current study, it is likely that by establishing rapport as early as possible in the semester, professors may experience higher attendance thus providing increased and continuing opportunities to build professor–student rapport, which may result in continued attendance, and the cycle continues. Thus, if administrators, higher educators, and professors are voicing concerns over attendance, finding ways to promote professor–student rapport as early as possible might be valuable.

Perceived autonomy support from the professor was the only predictor of expected and actual grades. It should be noted, however, that rapport, consistent with past studies (e.g., Wilson & Ryan, 2013), was also related to expected and actual grades at the bivariate level but did not emerge as a significant predictor when it competed for variance with autonomy support in the regression analyses. It could be that professors conveying confidence in their student’s ability to do well in the course and supporting their choices while listening to them and validating their ideas and perceptions creates an environment in which students believe that they can do better in the course. Furthermore, this student-centered approach translates into greater intrinsic motivation and therefore higher student achievement. Our findings corroborate research that links perceived autonomy support and positive educational outcomes across cultural contexts (e.g., Chirkov, 2009; Chirkov & Ryan, 2001; Diseth & Samdal, 2014), subject matter (e.g., Black & Deci, 2000; Filak & Sheldon, 2008; Hagger, Sultan, Hardcastle, & Chatzisarantis, 2015; Hall & Webb, 2014), and educational levels (e.g., Martin, Sultan, Hardcastle, & Chatzisarantis, 2015). However, stage-environment fit theory (Eccles & Midgley, 1989) suggests that different levels of class structure and autonomy support may be appropriate depending upon class content, size, and level in order protect against creating a developmentally regressive educational environment (Eccles et al., 1993) and to best fit the changing needs of students. It is unlikely that the relationship between autonomy support and course grades is spurious, reflecting the impact of “easier” classes or professors, because most of the students were in challenging 200-level survey courses with considerable drop-failure-withdrawal (DFW) rates or in rigorous upper division courses taught by seasoned faculty. However, the importance of autonomy support for course grades would be furthered if it still contributes to student outcomes when taking course difficulty, student skills (e.g., writing, test taking), and pre-existing levels of student self-regulation and autonomy into account. Also, autonomy support accounted for 7% of the variance in expected grades. This could have been higher had we assessed expected grades in percentages instead of letter grades. Overall, future research is ripe with many opportunities to further delineate the importance that autonomy support plays in course grades.

How could one support the autonomy of their students? Fortunately, there is a well-documented line of research showing that it is possible to coach teachers and professors how to be more autonomy supportive in their classes and interactions with their students (Bonneville-Roussy et al., 2013; Filak & Sheldon, 2008; Niemiec & Ryan, 2009; Overall, Deane, & Peterson, 2011; Sheldon & Krieger, 2007; Simon et al., 2015; Steele & Fullagar, 2009; Stefanou et al., 2004). For example, professors can promote autonomy support when they allow students to select group members, create evaluation procedures, and formulate class guidelines (Deci et al., 1982; Reeve, 2006; Reeve & Jang, 2006; Stefanou et al., 2004). Further, they could grant students the opportunity to openly talk about their wants and needs and allow students to pick materials utilized in class projects (Deci et al., 1982; Reeve, 2006; Reeve & Jang, 2006; Stefanou et al., 2004). Professors may also promote autonomy support by allowing students to ask questions and freely debate topics, discussing multiple problem-solving strategies, and promoting goal-oriented behavior (Deci et al., 1982; Reeve, 2006; Reeve & Jang, 2006; Stefanou et al., 2004). Reeve (2006) further suggests ways that compromise autonomy support including providing solutions to problems before the student has the opportunity to complete it, emphasizing the use of directives or commands, utilizing controlling questions (e.g. “Can you do a certain problem?”), using forceful language in directions (e.g., “must,” “should,”, etc.), and monopolizing the learning materials. Since autonomy support provides various benefits and predicts actual learning outcomes, perhaps professional development programs at universities could offer brief training sessions for professors who are interested in learning about different types of autonomy support (Stefanou et al., 2004) and how these techniques could be promoted in the classroom.

Conclusion

The current investigation has shown that rapport and autonomy support have differential implications for various outcomes ranging from professor effectiveness to actual course grades. Professors who desire to receive better course evaluations and to promote the learning of their students might want to consider including practices and behaviors that would promote rapport and levels of autonomy support appropriate for the developmental needs of their students in their arsenal of already existing effective pedagogies. Future research is ripe with opportunities to develop a finer understanding of how, when, and to whom rapport and autonomy support confer academic benefits and improve student learning.