ArticlePDF Available

How Instructional Methods Influence Skill Development in Management Education

July 2015
Academy of Management Learning and Education 15(3)

DOI:10.5465/amle.2013.0354

Authors:

Juanita Cajiao

Icesi University

Michael Burke

Tulane University

Research concerning why and how to promote social interaction and learner reflection in management education and training is somewhat underdeveloped. In this investigation, we used a predictive, quasi-experimental design with 246 students from a business school in Colombia who were enrolled in 10 sections of a leadership course to examine expected effects of instructional methods that promoted different levels of social interaction and reflection on self-reported learning behaviors (dialogue and reflection activities), self-efficacy for class performance, and instructors' assessments of students' skill demonstration (team work, communication, influence, and work proficiency and effort). In comparisons to students participating in instructional conditions with less social interaction and fewer reflective activities, students participating in an instructional condition that promoted higher levels of these activities exhibited considerably greater student-student dialogue, instructor-student dialogue, and reflection. These learning behaviors in turn led to enhanced self-efficacy for class performance and skilled activity. In addition, students' perceptions of psychological safety partially mediated relationships between instructional method and dialogical and reflective activities. The implications of these findings for coupling action, dialogue and reflective activities in management education and training as well as avenues for future research are discussed.

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Descriptive Statistics for Learning Behaviors by Instructional Method

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Regression Results for the Effects of Instructional Engagement on Learning Behaviors

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Standardized Effects of Instructional Method on Learning Behaviors

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HOW INSTRUCTIONAL MET

HODS INFLUENCE SKILL

DEVELOPMENT IN MANAGEMENT EDUCATION

Journal:

Academy of Management Learning & Education

Manuscript ID:

AMLE-2013-0354-RES.R2

Manuscript Type:

Research & Reviews

Submission Keywords:

Management education, Experiential learning, Undergraduate business

education

Academy of Management Learning & Education

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Abstract

Research concerning why and how to promote social interaction and learner reflection in

management education and training is somewhat underdeveloped. In this investigation, we

used a predictive, quasi-experimental design with 246 students from a business school in

Colombia who were enrolled in 10 sections of a leadership course to examine expected

effects of instructional methods that promoted different levels of social interaction and

reflection on self-reported learning behaviors (dialogue and reflection activities), self-

efficacy for class performance, and instructors’ assessments of students’ skill

demonstration (team work, communication, influence, and work proficiency and effort). In

comparisons to students participating in instructional conditions with less social interaction

and fewer reflective activities, students participating in an instructional condition that

promoted higher levels of these activities exhibited considerably greater student-student

dialogue, instructor-student dialogue, and reflection. These learning behaviors in turn led

to enhanced self-efficacy for class performance and skilled activity. In addition, students’

perceptions of psychological safety partially mediated relationships between instructional

method and dialogical and reflective activities. The implications of these findings for

coupling action, dialogue and reflective activities in management education and training as

well as avenues for future research are discussed.

Keywords: Management Education, Experiential Learning, Psychological Safety.

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INTRODUCTION

Given the interpersonal nature of many managerial skills (Schippman, 1999;

Whetten & Cameron, 2005), learning from personal and social experience are crucial

aspects of managerial skill development (Holman, 2000; Mintzberg, 2004). Furthermore,

instructional methods that promote social interaction to greater degrees have been shown to

increase knowledge and skill development in business student (e.g., see Siegel, Khursheed,

& Agrawal, 1997; Specht, 1991) and worker samples (e.g., see Burke, Salvador, Smith-

Crowe, Chan-Serafin, Smith, & Sonesh, 2011; Taylor, Russ-Eft, & Chan, 2005). Studying

why and how different types of instructional methods influence knowledge development

and skill acquisition would be important for advancing theory and practice in relation to

management education and training. To date, both the empirical managerial education

literature and the organizational training literature are relatively silent on why and how

different instructional methods affect learning itself (e.g., learners’ dialogical and reflective

activities) and skill acquisition. A primary objective of this investigation is to employ a

predictive, quasi-experimental study design to shed light on why and how different

instructional methods, which vary in the degree to which they promote social interaction

and guided reflection in a course, affect learning itself and skill acquisition in relation to

management education.

In the following sections, the conceptual framework, the model in Figure 1, guiding

this research is developed through a review of the relevant literature. Initially, our

discussion focuses on learning as an on-going process and the need to study learning

behaviors to more completely understand the outcomes of learning. This discussion will

emphasize dialogue, reflection and action as critical learning behaviors. Subsequently, we

develop conceptual arguments for why and how instructional methods that vary in the

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degree to which they promote dialogue, reflection and action are expected to not only

produce differences in these learning behaviors but also are expected to influence

psychological safety, self-efficacy for class performance, and the demonstration of personal

and interpersonal skills within a management education course.

__________________________

Insert Figure 1 about here

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Learning and Key Learning Behaviors

Learning constitutes a vast domain broadly studied by scholars in disciplines ranging

from philosophy, sociology, neuroscience, education, business to psychology. Despite such

a myriad of disciplines and approaches to the study of learning, general agreement exists

among scholars that learning is a complex, multi-determined, ongoing process (Bandura,

1986, 2001; Dewey, 1938; Glaser, 1990; Kolb, 1984; Mezirow, 1997). Many scholars also

agree that dialogue and reflection play central roles in the learning process (Holman, 2000;

Kolb & Kolb, 2005; Lave & Wenger, 1991; Weil & McGill, 1989). In particular, dialogue

and reflection have been recognized as critical in regard to the effectiveness of instructional

practices in educational settings (Baker, Jensen, & Kolb, 2005; Gorsky & Caspi, 2005;

Gorsky, Caspi, & Trumper, 2006; Skidmore, 2006). Furthermore, a number of authors

have argued that learning behaviors themselves are central criteria for evaluating the

effectiveness of educational interventions at the individual (Glaser, 1990; Klein, Noe, &

Wang, 2006; Weiss, 1990) and group levels of analysis (Druskat & Kayes, 2000;

Edmondson, 1999; Edmondson, Dillon, & Roloff, 2007; Edmondson & Nembhard, 2009).

A number of scholars have also argued that greater attention be given to the forms of

social interaction and the context in which learning takes place (Boud & Walker, 1991;

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Cunliffe, 2002; Holman, Pavlica, & Thorpe, 1997; Pavlica, Holman, & Thorpe, 1998;

Vince, 1998). In this sense, researchers are extending notions of experiential learning

theory as originally articulated by Kolb (1984) or presenting alternative perspectives on

experiential learning theory, advocating for more dialogue- and situation-based approaches

to experiential learning. One important issue that has emerged from this literature is that

individuals likely differ in their ability to engage in dialogue or their ability to reflect upon

experiences. Hence, dialogue and reflection, as critical aspects of the learning process, may

need to be carefully guided and fostered within particular contexts in order to promote

knowledge development (Armstrong & Anis, 2008; Burke, Holman, & Birdi, 2006; Gray,

2007; Schein, 1996).

In regard to managerial skill development, recent theoretical and empirical advances

provide insights into means for enhancing learning behaviors such as dialogue and

reflection and, thus, offer promise for improving the development of managerial skills.

One development concerns Edmondson and colleagues (Edmondson, 1999, 2008;

Edmondson & Mogelof, 2006) work on a climate for psychological safety, where

psychological safety refers to how individuals perceive the learning context in terms of its

conduciveness to expressing one’s ideas and being able to reflect upon one’s experiences.

Developing pedagogical strategies and establishing learning conditions that provide

individuals with the opportunity to actively participate in the learning process to facilitate

learning outcomes is an under-researched area and focus of this investigation. Next, we

discuss in more detail the nature of learning behaviors and how different pedagogical

strategies or means of instructional delivery would be expected to promote not only such

behaviors and efficacy for demonstrating such actions, but also a psychologically safe

learning environment.

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Consistent with Gorsky et al. (2006), dialogue is defined as a discursive relationship

between two or more individuals and is characterized by thought-provoking activities such

as questioning, explaining, and evaluating issues at hand. In the context of a learning

environment, such behaviors distinguish dialogue from an ordinary conversation. On the

other hand, reflection is often defined as a systematic cognitive process in which the

individual actively attempts to increase his or her understanding of personal experiences,

and thus enhance his or her ability to learn from such experience (Anseel, Lievens, &

Schollaert, 2009; Argyris & Schon, 1974; Boud, Keogh, & Walker, 1985; Mezirow, 1998;

Raelin, 2001). Furthermore, Schon (1987) distinguishes between reflection-in-action which

refers to cognitive processes in the middle of an activity, and reflection-on-action that

implies stepping back from experience with the aim of carefully reviewing and drawing

lessons from it. Others have proposed reflective activities that relate more to the

development of technical knowledge. For instance, Mezirow’s (1991) critical reflection

considers three kinds of reflection: on content or problem identification, process referring

to the method for solving the problem, and premises related to the basic assumptions on

which the individual devises the problem.

Due to the present focus on developing management-related skills, reflection is

considered in this investigation as a process of re-examining experiential learning activities

that often involve social interaction, and the concomitant thought processes enacted by

learners. In other words, reflection is action-focused, concerned with simplifying

experience, and may focus on possibilities related to future actions. As such, our theorizing

on the learning process is largely grounded in Kolb’s (1984) experiential learning cycle,

which posits learning as a process of the learner – experiencing, reflecting, thinking, and

acting – in a recursive manner.

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Learners’ active participation in the learning process is also expected to directly

impact the quality of their educational experience (Colquitt, LePine, & Noe, 2000; Noe &

Colquitt, 2002). Previous research findings offer robust support for learner engagement,

often referenced in an affective sense, as an important factor in college students’ learning

(Astin, 1993; Astin & Lee, 2003; Pascarella & Terenzini, 2005). Other investigators (e.g.,

Burke, Scheurer, & Meredith, 2007; Svinicki & Dixon, 1987) have discussed why

particular instructional activities can promote active learning without invoking notions of

individuals’ affective engagement. Importantly, Svinicki & Dixon (1987) presented a

comprehensive instructional model that incorporates a broad range of classroom activities

that can lead learners through Kolb’s (1984) full learning cycle. Svinicki and Dixon’s work

is particularly relevant to this investigation as they pointed to the critical role of discussion

being focused on “examining an idea” in the reflective observation phase; while discussion

in the abstract conceptualization phase would ideally be conducted to allow students to

develop an “explanation of a concept” (p. 144).

The Expected Effects of Instructional Methods that Promote Active Learning

The question of how to optimally facilitate and enhance learning behaviors and

especially in relation to the development of managerial skills is somewhat open to debate.

For instance, among a number of possible reflective activities are individual written

reflective activities and group debriefing processes which vary in the nature of reflection. A

written reflection focuses on prior experience and mainly on the respondent’s experience;

whereas, debriefing typically takes place in a group context and often involves considerable

dialogue for making sense of personal and collective experiences (Pearson & Smith, 1985;

Raelin, 2001). Notably, in a typical lecture based instructional method, reflective-

dialogical interactions have limited chances of occurring due to the dominant role of the

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instructor and the passive information-based structure of the instructional delivery method.

However, advocates of instructional methods that promote learner involvement, view

reflection as not solely an individual cognitive process, but a socially constructed one.

Cunliffe (2002) refers to the latter practice as reflexive dialogue highlighting the

intertwined relationship of these two types of learning behaviors. As pointed out by Peltier,

Hay and Drago (2005b) this reflexive dialogue is likely between students as well as

between students and instructors and may be qualitatively different.

The above conceptual arguments support the notion that instructional methods that are

designed to promote social interactions, encourage high levels of critical thinking, and

grounded in action will lead to greater student-student and instructor-student dialogue as

well as action-focused reflection on the behalf of learners. Importantly, the literature on

experiential learning, as discussed above, supports an argument for a holistic approach to

learning; whereby learning behaviors and knowledge acquisition are only optimized when

all three types of learning behavior are at the highest levels. As a result, we hypothesize:

Hypothesis 1

As the instructional method includes greater social interaction and

reflective activities, students’ reflective and dialogical activities will increase.

Importantly, to the extent that the instructional method promotes the involvement of

the learner as an “actor” and not just a “receiver,” learners should perceive the learning

environment as psychologically safe (Edmondson, 1999). That is, in terms of dialogue, in

a psychologically safe environment, individuals would be expected to more openly discuss

their thoughts and opinions in comparison to an environment that is not viewed as

conducive to permitting the expression of one’s ideas. When instructional delivery is

designed to promote an open expression of ideas and opinions, then learners will likely

come to view the climate as a less restrictive, more open environment for learning and

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further express such learning behaviors. This point would hold for not only dialogue, but

also with respect to reflection that can be in-action and of a public nature. In this sense,

psychological safety is expected to partially mediate the relationship between instructional

method and learning behaviors. As a result, we hypothesize that:

Hypothesis 2: Students’ perceptions of psychological safety will partially mediate

the relationships between instructional method and students’ reflective and

dialogical activities.

We note that we expect perceptions of psychological safety to be well-formed prior to the

conclusion of an educational experience and to, in part, serve as a check on the degree to

which the instructional method is perceived to involve students in the learning process.

Furthermore, students’ purposeful deliberation on class activities in an open

atmosphere that promotes effective classroom dynamics (e.g., discussions with students and

instructors) and reflective activities may provide valuable performance information for self-

efficacy development. This point is consistent with the theoretical arguments that

experience and self-referent thoughts are at the core of efficacy formation (Bandura, 1986,

1997, 2006). In addition, the ability of students to more openly participate in the learning

process should contribute in not only an actual way, but also in a vicarious manner (by

observing others) to efficacy formation. Self-efficacy is, in turn, expected to directly

impact academic performance (Multon, Brown, & Lent, 1991; Pajares, 1996; Schunk,

1984; Zimmerman, Bandura, & Martinez-Pons, 1992). In large part, students’ self-

efficacious beliefs are expected to influence academic performance levels by regulating

their effort and persistence with respect to mastery of course material (Pajares, 2002;

Zimmerman et al., 1992).

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At the same time, we recognize that self-efficacy may be negatively related to

demonstrated effort in academic contexts when students believe that they know the material

and success is readily achievable (Vancouver & Kendall, 2006). However, we do not

expect such a negative effect in the present academic context due to the rigor of the present

course as described below and the graduated nature of requested student involvement

across educational conditions. This expectation is in line with the arguments and findings of

Beck and Schmidt (2013), who reported that individuals facing a challenging goal or

situation are not likely to conclude that their capabilities greatly exceed the requirements of

the task. Thus, in such situations, one should expect a positive relationship between self-

efficacy and resource allocation or effort. Together, these arguments underlie the

hypothesis:

Hypothesis 3: Students’ self-efficacy for class performance will mediate the

relationship between psychological safety and skill demonstration.

For the purposes of this investigation, skill demonstration will be defined relative to a

leadership course and discussed below.

Learning behavior development is intended to properly prepare students to perform

in a classroom setting and to achieve performance standards. The underlying premise is that

learning behaviors (i.e., dialogue and reflective activities) promote a deeper understanding

of material including eventual skill acquisition or progress toward expertise. This effect is

expected, in part, from dialogue and action-focused reflection forcing learners to infer

causal and conditional relations between events and activities. This experience should not

only enhance learners’ self-efficacy for class performance, but it should also contribute to

skill development. Thus, we hypothesize:

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Hypothesis 4: Students’ self-efficacy for class performance will partially mediate

the relationship between learning behaviors and skill demonstration.

Finally, while we expect the demonstration of skills in a course to be positively

related to course grades, course grades are determined by multiple inputs (e.g., reading

assignments, tests) that are not the focus of this investigation. Also, course grades in a

leadership development course often include a component, class participation, which would

overlap with an assessment of skill demonstration obtained for research purposes.

Therefore, given this potential measurement confound, we do not formally hypothesize a

relationship between skilled activity and grades in a management education course.

METHODS

Participants, Leadership Course, and Procedures

Participants were a sample of 246 students enrolled in 10 sections of a leadership

course within a private Colombian university. The average age was 20.5 (SD = 1.64) years

and 59% of the sample was female. Relative to the age of our sample, we note that there

are no neat boundaries such as age to classify individuals as adult learners. Furthermore,

our student sample does not fit typical conceptualizations of adult learners as working full-

time and participating in a largely voluntary, job-related learning activity (Johnstone &

Rivera, 1965; Kim, Collins Hagedorn, Williamson, & Chapman, 2004). That said, we view

our sample as young adult learners in the sense of being self-directed and responsible for

preparing themselves for professional work (Knowles, Holton, & Swanson, 2005). Given

that these individuals would have little or no exposure to situations where skills taught in

the course could be displayed, the educational activities, as discussed in this section, were

designed to scaffold learning and guide practice (Coe, 2011; Gray, 2007; Hedberg, 2009).

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The leadership course was offered to students of different majors over a 16-week

period with the largest percentage being from the School of Business and Economics

(45%). Others included students of the School of Engineering (38%) and the School of

Sciences (17%). This situation implies that most of the students did not know each other

and the course sections were comprised of individuals from applied disciplines. All

sections of the course had the same syllabi and used the same textbook. A number of

efforts were taken, as discussed below, to not convey the specific purposes of this

investigation to students and teachers.

During the first week, students completed measures of dispositional learning goal

orientation and self-efficacy for class performance. These measures were gathered to

determine if there were pre-existing differences in students’ motivation to learn from

different types of learning experiences across the educational conditions. Information

pertaining to student demographic variables (i.e., major, gender, age), incoming grade point

average (GPA), and ICFES (Instituto Colombiano para el Fomento de la Educacion

Superior, “Colombian Institute for the Promotion of Higher Education) scores were also

collected to evaluate possible differences in students across the experimental conditions.

ICFES is the Colombian national standardized test of competencies taken for entry into

undergraduate studies. This test is similar to the American SAT. Responses to a

psychological safety questionnaire were gathered in week 10 to evaluate students’

perceptions of the learning environment and serve as an experimental manipulation check.

Measures of students’ self-reported dialogical and reflective activities as well as their self-

efficacy for class performance were gathered in week 15. Instructors’ evaluations of

student skill demonstration were obtained in week 16.

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Instructors and Manipulation of Instructional Method

The manipulated variable was instructional method with three conditions that varied

with respect to the promotion of dialogue (student-student and student-instructor),

reflection, and action. These educational conditions were lecture only, lecture along with

dialogue and guided (group) reflection, and lecture along with dialogue, guided (individual

and group) reflection and experiential exercises. For discussion purposes, these conditions

will be referred to as the low, moderate, and high social interaction conditions, respectively.

This labeling of instructional conditions with respect to “social interaction” acknowledges

not only the role of learners as actors, but also the role of the instructor as an actor and

facilitator of reflection.

The low social interaction condition was comprised of a total of 36 students in two

course sections taught by the same instructor (Instructor 1). The moderate social

interaction condition had 2 instructors, each one with 2 courses; Instructor 2’s courses

comprised groups of 32 and 27 students and Instructor 3 had 20 and 30 students. Among

the high social interaction condition, Instructor 4 had 59 students (31 and 28) and Instructor

5 had 26 and 16 students. The total numbers of participants in the low, moderate, and high

conditions were 36, 109, and 101, respectively.

We note that we did not systematically gather individual difference data on

instructors so as to not prime instructors about the aims of our investigation. Publicly

available information indicated that the professors differed in terms of years of teaching as

follows: Instructor 1 had 10 years of experience, Instructor 2 had 8 years of experience,

Instructors 3 and 5 had 3 years of experience, and Instructor 4 had 4 years of experience.

Moreover, we gathered data on teaching evaluations for each instructor for all courses that

they taught in the two years preceding the experimental manipulation. More specifically,

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we tested, using analysis of variance, whether instructors’ averages differed on four key

items that students responded to on 5-point Likert-type scales with anchors ranging from

“strongly disagree” to “strongly agree”: The instructor clearly explains course subjects

(Item 1), the instructor clearly responds to student’s questions (Item 2), the instructor

presents different learning activities (Item 3), and the instructor uses examples according to

the course subject (Item 4). The respective ANOVAs for tests of mean differences for Item

1, F(4,1133) = 1.14, p = .34, Item 2, F(4,1133) = 1.95, p = .10, Item 3, F(4,1133) = .86, p =

.49, and Item 4, F(4,1133) = 1.83, p = .12, were statistically non-significant. On average,

each instructor was rated relatively high on each item, with the overall item means across

instructors being 4.63 (SD = .57) for Item 1, 4.65 (SD = .55) for Item 2, 4.56 (.60) for Item

3, and 4.64 (SD = .53) for Item 4.

The instructor in the lecture only condition had previous experience in that

education method and was not asked to change the conduct of his classes (i.e., to instruct

accordingly to the standard lecture format). The same point applied to the instructors who

were asked to teach in the other sections/conditions, with the exception that these

instructors were not highly familiar with the particular student reflection procedures that

were used in each condition. The other exception was that instructors in the high social

interaction condition were not as familiar with the extensive use of experiential exercises,

and were encouraged to do so with carefully selected exercises for the four sections in this

condition. Given that the standard lecture condition involved no new manipulation, no

additional information was provided. Instructors in the moderate and high social

interaction conditions were separately informed of the desire for them to use particular

reflective techniques during the semester, and to rely, in the high condition, on particular

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experiential exercises. Instructors assigned to the high condition participated in an

informational session on experience-based instructional delivery.

For the two course sections using a lecture-based format with reading assignments

as the primary mode of teaching, this instruction was mainly theory and conceptually

driven, characterized by the regular use of videos, teacher’s control, and students taking on

a passive role. The four sections of the moderate condition had regular lectures and reading

assignments, periodic use of videos and cases, small group projects, small group

discussions, and guided reflection on class activities. The guided reflection encouraged

students to use deductive and inductive ways of thinking. For example, in small groups

students discussed a case study and by the end of the activity they were asked to answer the

question “What have you learned about the content at hand?” Similarly, at the end of each

class students were asked to reflect on what they have learned in relation to the concepts

covered that day. The introduction of group activities—projects, discussions, and guided

reflection was expected to boost student-student interaction, teacher-student interaction,

and reflective activities relative to the typical lecture-based class.

The four sections of the high social interaction condition had regular lectures, but

emphasized the use of role plays, managerial simulations and other carefully structured

experience-based exercises, with infrequent use of videos and cases. These sections

included individual written reflection on personal experiences and regular instructor-guided

group debriefings on experiential class activities. Feedback took different forms such as

orally and immediately after a role play in small groups, individually and written after a

group outdoor exercise, and in a group format after an in-house managerial simulation.

Notably, the written reflective activities were adopted from Osland, Kolb, and Rubin

(2007) and explicitly asked students to respond to questions that were aligned with Kolb’s

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(1984) learning cycle. In addition, instructors in this condition used an evaluation rubric

adopted from Osland et al. to assess students’ responses on written assignments relative to

Kolb’s (1984) learning cycle.

Furthermore, in line with Boud and Walker’s (1991) approach to experiential

learning as well as a scaffolding approach to learning (Coe, 2011; Goldstein, 1999; Pea,

2004), students in the moderate and high social interaction conditions were initially asked

to intra-personally reflect on their involvement in a group activity, then in small group

discussions on another group activity, and finally in an open debriefing with the full class

concerning a third group activity. This movement from intra-personal to public reflection

as one progressed through group activities was intended to allow students to attend to

feelings and thoughts about their experiences in a more positive manner and to promote

confidence for learning within subsequent exercises. In addition, for the high condition, the

group exercises were designed to become progressively more realistic and complex

(moving from case discussions, to role plays, to outdoor group activities, and finally to the

management simulation) to provide an authentic learning experience. As such, this

educational condition can be viewed, in part, as incorporating a situated perspective to

experiential learning (see Merriam, Caffarella, & Baumgartner, 2007). Also, in this

instructional condition, feedback on the behalf of instructors was guided by the rubric noted

above, which focused on the phases of Kolb’s (1984) learning cycle.

Measures

Learning Goal Orientation and Self-Efficacy

Given that assignment to course sections was not random, we examined possible

differences in learning goal orientation and self-efficacy for the leadership course during

the first week of the course. These variables were considered since they are known to

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affect learners’ motivation to engage in educational/training activities (Button, Mathieu, &

Zajac, 1996; Dweck & Leggett, 1988; Harackiewicz, Barron, & Elliot, 1998; Martocchio &

Hertenstein, 2003; Pintrich, 2000; Pintrich & Schunk, 2002). Button et al.’s Mastery Goal

Orientation was used to measure learning goal orientation. It is comprised of 8 items on a

5-point Likert scale format, with response options ranging from strongly disagree to

strongly agree. Examples items are: “I prefer to work on tasks that force me to learn new

things”, “The opportunity to learn new things is important to me.” The reliability of this

measure was .79.

For assessments of self-efficacy, Pintrich, Smith, Garcia, and McKeachie (1991;

Pintrich & Smith, 1993) self-efficacy for learning and performance was administered in

week 1. This measure is comprised of eight items on a 7-point Likert scale with anchors

ranging from very inaccurate to very accurate. Example items are “I’m certain I can master

the skills being taught in this class”, “Considering the difficulty of this course, the teacher,

and my skills, I think I will do well in this class.” A second measure of self-efficacy was

obtained in week 15, as noted above, for evaluating the effects of learning behaviors on

efficacy for class performance. The reliabilities of the first and second measurements of

self-efficacy were .79 and .88, respectively.

Psychological Safety

A modified version of Edmondson’s (1999, 2004) team psychological safety scale

was administered. The eight items were reworded, replacing the word “team” with the word

“class.” For example, “It is safe to take a risk on this team” became “It is safe to take a risk

in this class.” Also, an item from the Team Leader Coaching dimension of Edmondson’s

scale was modified and added (i.e., The team leader is an ongoing presence in this team,

someone who is readily available). The modified item was “The instructor is an ongoing

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presence in this class, someone who is readily available.” All items were based on a 7-

point scale ranging from very inaccurate to very accurate. One item did not correlate with

any other items in the scale and did not contribute to the reliability of the scale and,

therefore, it was eliminated (i.e., No one in this class would deliberately act in a way that

undermines my efforts). The reliability of the seven-item scale was .76.

Learning Behaviors

Three scales from Peltier et al.’s (2005a) reflective learning inventory were used to

measure dialogical and reflective activities: student-student interaction scale, instructor-

student interaction scale, and reflective activity scale. Example items from the student-

student interaction scale were: “There was an open exchange of new ideas between

students”, and “My fellow students valued my opinions.” Example items from the

instructor-student scale were: My instructor… “Encouraged student questions and

comments” and “Was willing to talk about things that I disagreed with.” Example items

that assessed reflective activities were: “I often reappraised my experiences so I could learn

from them” and “I often reflected on my actions to see whether I could improve them.”

Response were made on a 5-point Likert-type scale with options ranging from strongly

agree to strongly disagree. The reliabilities were .82 for student-student dialogical

activities, .93 for instructor-student dialogical activities, and .90 for reflective activities.

Notably, the learning behaviors had relatively high inter-correlations (ranging from

.61 to .71). Given the relatively high correlations between the learning behaviors and the

expectation that these behaviors would co-occur, we also created a composite measure of

learning behavior for tests of the respective hypotheses and theoretical model. The

reliability of this composite was .94.

Performance and Learning Outcomes

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An instructor’s evaluation of the students’ in-class performance was obtained

during week 16. Instructors were asked to rate the frequency that each student

demonstrated behaviors (i.e., 15 items) that mapped onto the course content areas: Time

and stress management, teamwork, communication, and influence. An initial confirmatory

factor analysis did not provide a good fit to the data. As a result, an exploratory factor

analysis was conducted with maximum likelihood factoring and varimax rotation, which

produced three factors that reflected three of the course content areas (team work,

communication, and influence) and a fourth factor labeled work proficiency and effort.

Examples of the items comprising the scales are: work proficiency and effort, “When the

student was faced with stressful or time-pressured situations, he/she: Demonstrated

commitment and persistence toward goal achievement”; team work, “When the student was

in the role of a team member he/she: Shared information with classmates and encouraged

participation”; communication, “When the student was communicating with others, he/she:

Articulated ideas effectively when speaking”; and influence, “When the student was trying

to influence someone for a specific purpose, he/she: Avoided using demands to impose

his/her will onto others”. The items were rated on a 5-point scale with response options

ranging from never to always. The internal consistency reliabilities for the respective

scales were .88, .91, .80 and .88. For the purposes of this investigation, an overall,

composite measure of skill demonstration was used. The composite reliability was .96.

Hypotheses Testing

Since the independent variable was a three-level, categorical variable, for tests of

Hypotheses 1, two dummy variables were created. Dummy coding permitted contrasts of

each of the experimental conditions with the comparison group, the lecture only condition

(also see Aiken & West, 1991). The dummy variables were labeled "moderate” and "high”

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for the respective instructional conditions: (a) lecture-dialogue-guided reflection, and (b)

lecture-dialogue-guided reflection-experienced-based activities Also, tests of Hypotheses 1

were conducted with respect to effects corrected for dependent variable unreliability

associated with the dialogical and reflective activity scales using procedures discussed by

Raju and colleagues (Raju & Brand, 2003; Raju, Burke, Normand, & Langlois, 1991). For

the latter tests, the observed experimental effects (i.e., d-statistics, Cohen, Cohen, West, and

Aiken (2003) were initially transformed to correlation coefficients to allow for the

computation of correct confidence intervals and then transformed back to d-statistics for

reporting purposes.

Hypothesis 2, 3, and 4 concerned mediation and were evaluated within the context

of the test of the path model in Figure 1 using AMOS 22 (Arbuckle, 2013). Along with

evaluating the overall fit of the model, we examined standardized direct, indirect, and total

effects. Given the nested nature of our data, students nested within instructors, we tested all

effects with 95% confidence intervals constructed with standard errors estimated using the

bootstrap bias-corrected percentile method available within AMOS (also see Davison &

Hinkley, 1997; Mooney & Davis, 1993). Finally, given that self-efficacy measured at Time

1 would theoretically be expected to affect self-efficacy at Time 2 and that ICFES (a proxy

for cognitive ability) would be expected to underlie the development of managerial skills,

we controlled for these variables when testing the model in Figure 1. By controlling, we

included paths for the effects of self-efficacy at Time 1 on self-efficacy at Time 2 and

ICFES on skill demonstration, and examined models with and without these effects

included.

Analysis of variance tests were also used to check for significant differences

between experimental groups regarding age, ICFES, GPA, learning goal orientation, and

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self-efficacy (measured at time 1). With the exception of GPA, no statistically significant

differences were found across the educational conditions. For GPA, a small but statistically

significant difference existed, with the high social interaction condition having the lowest

GPA of the three conditions. Gender composition, major and semester, did not significantly

differ between the instructional conditions. Based on these findings, we did not statistically

control for demographic characteristics (e.g., age, gender, major, semester). However, as

noted above, for conceptual reasons we did control for ICFES (cognitive ability) and self-

efficacy (Time 1).

RESULTS

Descriptive statistics for learning behaviors and overall learning behavior with

respect to each educational condition are shown in Table 1. As seen in Table 1, the general

pattern was that the magnitudes of the means for the learning behaviors were the highest in

the high social interaction condition with the respective standard deviations being the

lowest in the high social interaction condition.

----------------------------------

Insert Tables 1 about here.

---------------------------------

Multiple regression results pertaining to tests of Hypothesis 1 are presented in Table

2. As expected, instructional method had a statistically significant overall impact on

student-student dialogical activities, instructor-student dialogical activities, reflective

activities, and overall learning behavior lending support to Hypotheses 1. While the overall

findings were as expected, only the high social interaction condition had a statistically

significant effect on each of the learning behaviors. Noting the pattern of findings in Table

2 and to illustrate the practical implications of the regression effects, we examined the

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separate standardized effects for each educational condition relative to the other educational

conditions for each of the three learning behaviors and overall learning behavior.

-------------------------------

Insert Table 2 about here.

--------------------------------

The standardized effects for the moderate and high conditions, where effects are

corrected for unreliability in the respective learning behavior measures, are reported in

Table 3. In addition, we report confidence intervals for these disattenuated effects in Table

3. The findings pertaining to the standardized effects are notable in that the high social

interaction condition has standardized effects relative to both the low and moderate

conditions that are all greater than 1.0 for dialogical activities, approximate 1.0 for

reflective activities, and are greater than 1.0 for the overall learning behavior variable. In

other words, the mean of the high condition subsequent to course completion is generally at

or above the 84

percentile of the distribution of scores for the low and moderate

conditions for each of the learning behaviors and overall learning behavior.

-------------------------------

Insert Table 3 about here.

-------------------------------

Given the findings from tests of Hypotheses 1 concerning the comparability of

effects for the low and moderate conditions, we combined the subsamples for these

conditions for tests of the hypothesized path model in Figure 1. Descriptive statistics and

correlations between the measured variables employed in the test of the hypothesized

model are presented in Table 4. Overall, as shown in Figure 2, the posited model fit the

data (GFI= .98; CFI=.98; RMSEA=.04; χ

=13.3, df=9). Effects associated with the

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statistical controls are not shown in Figure 1 as only one standardized regression coefficient

in the model changed by .01 when controlling for self-efficacy (i.e., the path between

psychological safety and self-efficacy). Yet, we do note that self-efficacy (Time1) did have

a statistically significant effect of .17 on self-efficacy at Time 2. The standardized direct,

indirect, and total effects associated with variables in the model that was tested are

presented in Table 5. A closer examination of findings relative to the hypotheses leads to

several qualifications, as discussed below, concerning support for the overall model.

---------------------------------------------

Insert Table 4 and Table 5 about here

---------------------------------------------

Recall, Hypothesis 2 concerned the expectation that psychological safety would

partially mediate the relationship between instructional method and learning behaviors. In

support of Hypothesis 2, instructional method had a statistically significant direct (.41) and

indirect (.07) effect through psychological safety on learning behaviors. However,

Hypothesis 3, which concerned the expected mediating effect of self-efficacy for the

relationship between psychological safety and skill demonstration, was not supported. This

finding was due to the direct effect of self-efficacy on skill demonstration being statistically

non-significant. Finally, the test of Hypothesis 4, which concerned the expectation that

self-efficacy would partially mediate the relationship between learning behaviors and skill

demonstration, only provided evidence of a direct effect of learning behaviors on skill

demonstration. That is, the direct effect of learning behaviors on skill demonstration of .13

was statistically significant. We note that the total indirect effects for instructional method

and psychological safety on skill demonstration were meaningful and statistically

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significant, .08 and .07, respectively, with their effects primarily flowing through the

exhibition of learning behaviors.

DISCUSSION

In this investigation, we examined direct and indirect, through psychological safety,

effects of instructional method on the exhibition of dialogical and reflective learning

behaviors in the context of management education as well as relationships between learning

behaviors, self-efficacy for class performance, and skill demonstration. Notably, our

findings indicate that, in comparison to forms of instruction that involve less social

interaction and guided reflection, instruction that that promotes such interaction and

reflection contributes not only to greater student-student dialogue, instructor-student

dialogue, and reflective activities, but that it does so in part through a perceived

psychological safe learning environment. Furthermore, our results provided evidence of

meaningful effects of learning behaviors on skill demonstration. As a whole, this research

addresses recent calls for more rigorous empirical research and theoretical foundations for

appropriate instructional design and delivery in both managerial education and training

(DeRue, Sitkin, & Podolny, 2011) and for means to develop critically thinking

professionals through management education (Khurana & Spender, 2012; Rousseau, 2012a;

Vaara & Fay, 2012). Below, we discuss the scientific contributions and practice

implications of our findings.

From a theoretical perspective, our findings confirm conceptual arguments within

social and dialogical theories of experiential learning that the development of dialogical and

reflective learning behaviors will be optimal when learning is grounded in action (Holman

et al., 1997; Schon, 1987). This argument is supported by the fact that a primary difference

between moderate and high instructional conditions was the incorporation of experience-

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based exercises into the high condition. Related, an unexpected finding was that explicit

efforts to enhance social interactions and guide reflection in the moderate condition did not

produce differences in interpersonal interactions or reflective activities in comparison to the

lecture only condition. While we expected a boost in learning behaviors as a result of

structured reflective activities, the later finding is in line with arguments that as long as

experience is dissociated from reflection and dialogue, learning may not occur or may be

shallow (Kayes, 2004; Kolb & Kolb, 2009; Raelin, 2001).

A notable feature of the high social interaction condition was that different types of

experiential exercises (role plays, outdoor activity, and simulation exercise) were coupled

with particular reflective activities. For instance, some role plays involved interrupted

feedback and debriefing, the outdoor activity involved a debriefing session along with a

written reflection activity; in-class, experiential exercises involved self-reflection and small

group discussion. In effect, coupling experience with guided, private and public reflection

activities promoted reflection in- and reflection-on action. Importantly, these reflective

activities often promoted students’ self-awareness of the effectiveness of their behavior by

asking them to consider action steps they would take based on what they learned about

themselves through the reflective analysis. Arguably, this type of activity contributed to

students’ skill demonstration.

Another important contribution of this study to the management education literature

is that the findings concerning the mediating role of psychological safety advance our

understanding of the process of learning. Recall, learners’ perceptions of psychological

safety partially mediated the effects of instructional method on students’ learning

behaviors. These findings highlight that while instructional design considerations can have

a direct impact on interpersonal interactions in educational contexts, the extent to which

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learners feel safe to express themselves with fellow students and instructors who are

available to do so further contribute to the effects associated with instructional design. In

effect, these mediational findings support Kahn’s (1990, 2010) theoretical arguments

concerning the psychological conditions for engagement summarized in the quote: “People

are more likely to engage when they feel it is meaningful to do so, when they sense that it is

safe to do so, and when they are available to do so” (Kahn, 2010, p. 22). Furthermore, the

magnitudes of the effects of psychological safety on self-efficacy and skill demonstration

suggest the practical importance of establishing an open learning atmosphere to enhance

not only confidence for successful interpersonal activity, but also skill in relation to such

managerial actions.

Another practical implication is that the findings suggest incorporating not only a

range of experiential activities into interpersonal skill-oriented management education and

training courses, but also considering how one might couple these experiences with

reflective activities that explicitly encourage self and public reflection such as a written

reflection requirement and group debriefing, respectively. Recall, we coupled role plays

with interrupted feedback and experiential exercises with group debriefings and written

reflection. Our practice suggestion for coupling exercises (such as in-class, outdoor, and

those of a simulated nature) with written reflection recognizes that the personalization of

management learning through guided self-reflection is as important as the reorganization or

reconstruction of experience through discussions with instructors and other students in

group debriefings. These practice recommendations are in line with research findings that

have recognized the impact of metacognition in learning (Bell & Kozlowski, 2008; Ford,

Smith, Weissbein, Gully, & Salas, 1998) and the role of instructional design in developing

metacognition (Gully & Chen, 2010). Also, our general practice recommendation

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concerning coupling action-oriented activities with private and public reflective activities

complements the argument that the personalization of management learning provides the

link between management education and leader development (Petriglieri, Wood, &

Petriglieri, 2011).

The typical manner in which discussions and debriefings were conducted following

group activities in the high social interaction condition also has practical implications for

developing interpersonal skills in both management education and management training.

These discussions would be characterized as having simple “semi-structures” (Okhuysen &

Eisenhardt, 2002). That is, the instructors had several probes and simple formal rules that

directed students to interact with each other (e.g., to question others and share information

about their experience). The probes often concerned what students had learned and how

that knowledge could be applied in other contexts. As such, we believe the nature of these

discussions provided a platform for knowledge and opinions to flow among students and

improve their understanding of both their personal and collective experience, and

anticipatory thinking about future action.

Our findings also add to the evidence base for effective professional management

development by clarifying pedagogical means to promote such development. While

systematic reviews and meta-analyses offer general evidence-based guidance for

incorporating socially engaging instruction into managerial education (see Burke & Day,

1986; Taylor et al., 2005), this evidence base does not clearly inform why and how

different levels of social interaction affect learning itself (i.e., individuals’ dialogical and

reflective activities) and performance related outcomes such as the demonstration of skills.

In effect, our investigation illustrates how systematic reviews and evidence-based

management (i.e., a broad family of approaches to making effective decisions based on the

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best available, cumulative scientific and organizational evidence; see Ambrose, Bridges,

DiPietro, Lovett, & Norman, 2010; Briner & Rousseau, 2011; Rousseau, 2012b), can be

meaningfully augmented by primary, empirical research.

Potential Limitations and Future Research Directions

While the quasi-experimental, predictive design with alternative measurement

sources (student and instructor) contributed to the internal validity of this investigation,

several aspects of this study may serve as potential limitations. Among the most salient

factors is the fact that participants were not randomly assigned to instructional conditions

and instructors. To some degree, this potential limitation is mitigated by several checks.

First, we found that prior to the experimental manipulations that instructors’ teaching

effectiveness, as appraised by students, did not significantly differ across instructors or

instructional conditions. In addition, checks were made at the outset of the study on

individual differences that might contribute to differences across instructional conditions.

That is, our checks on theoretically relevant individual differences that might contribute to

differences in learning behaviors and outcomes included cognitive ability, self-efficacy for

course performance, and learning goal orientation, and did not produce any meaningful

differences across instructional conditions. In addition, controlling for self-efficacy at

Time 1 and cognitively ability (ICFES) as well as estimating standard errors with robust

procedures in tests of the theoretical framework further support the causal implications of

our findings.

Second, the strength of our dialogical and reflective activity manipulation for the

moderate condition may have contributed to the lack of differences between the low and

moderate conditions. While this possibility exists, theoretical arguments within

experiential theories of learning would suggest that it was the lack of action (in the form of

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role playing, simulated experiences, or hands-on activity) that was most likely responsible

for minimal differences between the low and moderate involvement conditions. Recall,

neither of these conditions called for the incorporation of action-oriented activities into the

leadership course.

Finally, there is some possibility that relationships between some learners’ measures

are inflated due to the use of self-report measures for psychological safety, self-efficacy,

dialogical activities and reflective activities. To guard against this possibility, these

measures were obtained at three different times during the academic semester.

Future research can shed light on not only the potential limitations of this

investigation, but also the degree to which the present findings generalize across different

types of management education, educational contexts, and populations of learners and

instructors. The present course focused on the development of interpersonal skills within a

leadership course and emphasized communication skills, influencing others, and teamwork

skills. Research aimed at examining the extent to which the effects of high levels of social

interaction and guided reflection hold for other types of management skills and

competencies would be informative. To the extent that such effects are evidenced within

other contexts such as corporate training programs and for other populations such as

working adults would also be informative. The latter point would include addressing the

degree to which the strength of the present findings generalizes to other national cultures.

The present study was conducted in Colombia, a moderately collectivistic culture

(Hofstede, 2001). Here, learners in the high instructional condition, where dialogical

interactions (e.g., student-student and instructor-student) were encouraged, may have been

highly receptive to this form of instruction. Yet, we note that the dialogical activities in the

present study went beyond informal conversations and focused on purposeful dialogical

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activities aimed at learning. Nevertheless, the degree to which cultural factors contributed

to the strength of the present instructional effects is worthy of future investigation.

In addition, several calls have been made for investigating the interaction between

individual differences and instructional delivery mode (Ford, Kraiger, & Merritt, 2010;

Gully, Payne, Koles, & Whiteman, 2002; Kraiger, Ford, & Salas, 1993; Noe & Colquitt,

2002). It is plausible that personality factors not included in this investigation may

moderate the relationship between instructional design and learning behavior development.

For instance, one of the Big 5 personality characteristics, openness-to-experience, has been

identified as being positively related to attitudes toward participating in learning

experiences and with partaking in nontraditional instructional delivery such as error-

encouragement instruction (Gully et al., 2002). As such, individuals lower in openness-to-

experience —characterized as being less curious, more cautious, more narrow-minded and

less prone to participate in unknown endeavors—may not benefit as much as those higher

in openness-to-experience when involved in highly active learning that incorporates

unexpected or changing conditions.

Related to the inclusion of additional individual difference variables in future

research, we encourage research that includes variables theorized to causally affect the

demonstration of work proficiency and interpersonal skills examined in this research. The

reader is referred to performance models and modeling such as the work of Campbell

(1990) and Griffin, Neal and Parker (2007). In particular, the work of Griffin et al. would

suggest the inclusion of a measure of openness-to-experience as well as a measure of role

clarity to more completely model the exhibition of managerial skills examined in this

investigation.

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Finally, future research could investigate the relationship between the acquisition of

learning behaviors themselves and lifelong learning. The organizational learning literature

emphasizes the importance of leaders’ adaptability to constantly changing complex

environments (DeRue et al., 2011; Edmondson, 1999; Schein, 1996). Developing learning

behaviors, in particular the ability to reflect on one’s experiences may constitute a crucial

competence to enhance individuals’ continuous learning in relation to a constantly evolving

world of work. This point echoes calls for investigating variables and processes related to

managers’ tacit knowledge development (Armstrong & Anis, 2008) and prior work that

advocated in-depth reflection as a critical aspect of management pedagogy (e.g.,

Antonacopoulou, 2010; Reynolds, 1999).

A conclusion of this investigation is that the co-occurrence of action, dialogue and

reflection within a management education course can produce meaningful improvements in

students’ learning behaviors and interpersonal skill acquisition. Furthermore, the effects of

high levels of social interaction and guided reflection on learning itself were partially

mediated by learners’ perceptions of the psychological safety of the educational context for

expressing their thoughts. Together, these findings advance our understanding of both the

process of learning and the behavioral effects associated with higher levels of social

interaction and guided reflection in management education. As such, the findings from this

investigation provide a path for business educators and trainers alike to design programs to

promote the development of learning behaviors that will enhance individuals’ ability to

effectively cope with and adapt to a rapidly changing world of work.

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TABLE 1

Descriptive Statistics for Learning Behaviors by Instructional Method

Learning Behavior/

Instructional Method

Mean

Learning behaviors (overall)

Low 36

3.80

.70

Moderate 109

3.83

.69

High 101

4.44

.34

Student-student dialogical activities

Low 36

3.44

.73

Moderate 109

3.57

.74

High 101

4.17

.55

Instructor-student dialogical activities

Low 36

4.06

.85

Moderate 109

3.96

.91

High 101

4.76

.36

Reflective activities

Low 36

3.86

.80

Moderate 109

3.93

.76

High 101

4.41

.40

Low, Moderate and High respectively correspond to the instructional conditions: (a)

lecture only, (b) lecture, dialogue and guided reflection, and (c) lecture, dialogue, guided

reflection, and experiential exercises.

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TABLE 2

Regression Results for the Effects of

Instructional Engagement on Learning Behaviors

Learning Behavior/

Instructional Method

β SE F R

Learning behaviors (overall)

35.04 .22

Moderate .04 .11

High .65* .11

Student-student dialogical activities

27.40 .18

Moderate .09 .12

High .49* .13

Instructor-student dialogical activities

34.76 .22

Moderate .06 .13

High .43* .14

Reflective activities

17.94 .13

Moderate .05 .12

High .39* .12

Note: N = 246.

Moderate and High respectively correspond to the instructional conditions of

lecture, dialogue and guided reflection, and lecture, dialogue, guided reflection and

experiential exercises .

*p < .05

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TABLE 3

Standardized Effects of Instructional Method on Learning Behaviors

Learning Behavior/

Instructional Condition Comparison d

95% CI for d

Learning behaviors (Overall)

Moderate – Low .03

-.31, .37

High – Low 1.31

.94, 1.81

High - Moderate 1.17

.88, 1.55

Student-student dialogical activities

Moderate – Low .18

-.18, .56

High – Low 1.25

.85, 1.76

High - Moderate 1.04

.72, 1.42

Instructor-student dialogical activities

Moderate – Low -.10

-.45, .24

High – Low 1.25

.87, 1.71

High - Moderate 1.22

.90, 1.58

Reflective activities

Moderate – Low .08

-.26, .43

High – Low .98

.63, 1.42

High - Moderate .85

.54, 1.19

Low, Moderate and High respectively correspond to the instructional

conditions: (a) lecture only, (b) lecture, dialogue and guided reflection, and

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TABLE 4

Descriptive Statistics and Correlations Among the Variables Included in the Test of the Hypothesized Model

Variable M SD 1 2 3 4 5 6

1 Instructional Method

.41

.49

2 Self-efficacy (Time 1)

6.23

.55

.02

3 ICFES (cognitive ability)

386.5

34.46

-.08

.04

4 Psychological Safety

5.50

.90

.20*

.07

-.03

5 Learning Behaviors

4.10

.65

.47*

.03

-.06

.41*

6 Self-efficacy (Time 2)

6.20

.66

.18*

.20*

.01

.36*

.40*

7 Skill Demonstration

3.80

.63

.23*

-.03

.01

.18*

.17*

0.15*

Note: N = 246.

*p < .05.

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TABLE 5

Direct, Indirect and Total Effects for Variables in the Tested Model

Direct Effects

Instructional Psychological Self-Efficacy Learning Cognitive Self-Efficacy

Variable Method Safety (Time 1) Behaviors Ability (Time 2)_____

Psychological Safety .20* --- --- --- --- ---

Learning Behaviors .41* .33* --- --- --- ---

Self-Efficacy (Time 2) --- .22* .17* .31* --- ---

Skill Demonstration --- --- --- .13* .02 .09

Indirect Effects

Psychological Safety --- --- --- --- --- ---

Learning Behaviors .07* --- --- --- --- ---

Self-Efficacy (Time 2) .19* .10* --- --- --- ---

Skill Demonstration .08* .07* .02 .03 --- ---

Total Effects

Psychological Safety .20* --- --- --- --- ---

Learning Behaviors .47* .33* --- --- --- ---

Self-Efficacy (Time 2) .19* .32* .17* .31* --- ---

Skill Demonstration .08* .07* .02 .16* .02 .09

_____________________________________________________________________________________________________

Note. A dashed line indicates that a direct, indirect or total effect was not estimated.

*p < .05.

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FIGURE 1

A Model of the Effects of Instructional Method on Skill Demonstration in Management Education

Note: Effects in the model are standardized regression coefficients. With the exception of the effect of self-efficacy on skill demonstration, all

effects are statistically significant. Effects pertaining to control variables included in tests of the model are discussed in the text. Instructional

Method is a manipulated variable with Low, Moderate, and High social interaction conditions respectively corresponding to: (a) lecture only, (b)

lecture, dialogue and guided reflection, and (c) lecture, dialogue, guided reflection and experiential exercises.

Learning Behaviors:

Student-Student Interaction

Instructor-Student Interaction

Reflective Activities

Skill Demonstration:

Teamwork

Communication

Influence

Work Proficiency and

Effort

Instructional Method

Psychological

Safety

Self-

Efficacy

for Class Performance

.41

.33 .31

.13

.20 .22 .09

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January 27, 2015

Dear Professor Brown:

We thank you for the opportunity to revise and resubmit our manuscript AMLE-2013-0354, retitled “How Instructional Methods

Influence Skill Development in Management Education,” to Academy of Management Learning & Education. We have responded to

each of your and the reviewers’ comments. Below, we restate each of the comments of the editorial team and indicate how we handled

the points raised in each comment. For clarity, we have italicized our responses to each comment.

Editor’s Comments and Our Responses

1. Streamline the first 2 pages. The first few pages introduce ideas that aren’t central to the study and in some cases aren’t ever

tested. I noted that your first paragraph distinguishes different types of managerial work but then immediately situates the study

in personal and interpersonal skills. Reviewer #1 (point #5) notes that distinction between different forms of reflection is raised

but never tested. Read as a whole, the first few paragraphs feel loose and don’t lend themselves to an easy take-away message

about how this study contributes to the literature. Moreover, you are preaching to the choir a bit about the complexity of

managerial work and the value of educating for that role. This is what AMLE is all about! I would suggest helping the reader

by focusing in more narrowly on your research question about how different instructional methods influence the development

of managerial skills. The core logic for your study seems to be that “learning from personal and social experiences is believed

to be critical” (second half of your current first paragraph) but that studies are “relatively silent on why and how different

instructional methods affect learning” (second half of the second paragraph). I suggest thinning out the first parts of each of

these paragraphs and getting quickly to what is motivating the study.

We deleted most of the prior first page to remove superfluous material and, as a result, move more directly to the motivation for this

study.

2. Related to this streamlining and the clarity of the message, I wonder whether you should reconsider the title of the manuscript.

You use the term “behavioral effect,” which almost implies an emphasis on behavior only. Your study addresses reflection (as

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a learning behavior), psychological safety, and self-efficacy. I think the current title frames your contribution too narrowly.

Would you consider something like, “How instructional methods influence skill development in management education:

Examining learning behaviors, psychological safety, and self-efficacy.” A manuscript title is something you have to live with,

so I’m making a soft suggestion here to consider a way to better line up the title with the contributions you make to the

literature.

We agree that the title was not very descriptive of what we studied. As a result, we adopted your suggestion to retitle the paper to

“How Instructional Methods Influence Skill Development in Management Education.” Please note that we did not include a subtitle,

as that would produce a rather long title, but we are open to adding one.

3. Make sure to note the contribution about psychological safety in the beginning and end of the manuscript. On p.3, when you

introduce the model and the flow of the literature review, you should note psychological safety. This is a useful addition to the

management education literature and bolsters your study’s credentials as addressing both personal and social constructs. You

currently don’t mention safety until p.5, into the literature review. Yet safety is highlighted as a central contribution of the

model in the first paragraph of your discussion. Please provide equal attention to this construct in the front and back-end of the

paper.

We now highlight the study of psychological safety as an important aspect of our modeling earlier on (on page 3). In addition, we

edited a paragraph in the Discussion (p. 25 and top of page 26) to highlight the scientific contributions and practice implications of

our study in regard to the role of psychological safety. This paragraph begins “Another important contribution of this study to the

management education literature is that the findings concerning the mediating role of psychological safety advance our understanding

of the process of learning …”

4. Provide the results of the instructional skill (via student ratings) in the methods section. I think it makes sense to note the

results of the comparisons between the instructors in the method section where the items are introduced (p.13). You mention

the items but do not provide the statistical analyses (test statistics and effect sizes). Did I miss that somewhere?

We now report the ANOVAs for the four items in the Method on p. 13. Given the lack of statistical significance for all tests, we did not

report the mean ratings for each of the five instructors on each item. Rather, we report the overall mean (and standard deviation) for

each item to provide readers with a sense for how instructors were evaluated on these items.

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5. Work on the balance of figures and tables. Because the figures only differ in terms of having coefficients present, I think it

would be reasonable to only print it once, with the coefficients. This opens up a little room for additional tables, and I can think

of one that many readers would ask for. You note that indirect effects will be a central focus of your analyses, yet those results

are never presented in tabular form (just in text). Could you add a table of direct, indirect, and total effects?

We added a table with standardized direct, indirect and total effects (i.e., Table 5).

6. Be consistent with the use of aggregate versus disaggregated learning behaviors. Currently your ANOVA and SEM results are

using different variables, with the ANOVA using disaggregated and your SEM using aggregate learning behaviors. This

renders them incomparable. I would prefer to see the same variables used across these analyses to the extent possible. To

accomplish this goal, all you have to do is introduce the aggregated variables to your ANOVA analyses, adding rows to the

bottom of Tables 1 & 2. Also, with regard to this variable, please note evidence for the use of the aggregated scale in the

method section, including a composite reliability. I suggest moving this up from the results section (pp.22-23) into method

(p.17).

We added rows with results for the learning behaviors (overall) variable to Tables 1 and 2, and we report the composite reliability for

learning behaviors (of .94) in the Method section (on p. 17).

Finally, you asked how I would like statistical controls to be handled. Personally, I prefer using statistical controls when they are

conceptually meaningful, even if the effects are not significant. I think there is ample theoretical support for controlling for T1 self-

efficacy and GMA. It would help me if I understood exactly where these were introduced in the model, and whether they were

included when producing the fit statistics and portrayed results (p.22 in particular). You write that the effects of the controls are “not

shown.” Does that mean they were “not included” in producing the displayed results or included but not displayed? Please be clear

where the controls were added to the analyses. I agree with you that they do not need to be depicted in the figure but I would like to see

the text and the figure footnote indicate what controls were used and where.

The results (path coefficients that are now reported in Figure 1) were based on the inclusion of the statistical controls. We do not

report the paths between Self-Efficacy Time 1 and Self-Efficacy Time 2 and GMA and Skill Demonstration in Figure 1 due to the fact

that their inclusion had a very small effect on only one other path in the model. In the note to Figure 1, we indicate that the model was

tested with the inclusion of statistical controls. Furthermore, we provide more detail on the paths pertaining to the controls, where

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they were included, and their effects on the model on p. 22 as well as refer the reader to Table 5 for a presentation of direct, indirect

and total effects for all variables included in the test of the model.

Reviewer 1’s Comments and Our Responses

Taking your starting point of working within the David Kolb experiential learning model, the revisions you have made to the

manuscript hang together quite well. You have limited the bounds of your research on the student learning to the confines of the

course, leaving the question open of how well does the experiential learning of the high involvement condition result in better learning

for and on the job as managers. So there is a sound internal consistency to your overall design and conclusions. You recognize the

limitations of your research and suggest the appropriate future avenues to address them. I also appreciate your expanded section on

what the concrete implications are for the pedagogical development and course design for faculty.

Within that David Kolb model and your focus on self-efficacy, I wonder why you haven’t drawn on Bandura (Self-Efficacy, W.H.

Freeman and Company, 1997), particularly the parts of the book that address learning in the educational context. I also would like to

see the questions that make up the student rating form component for the faculty evaluations that you discuss. Most student rating

forms address the issue of student satisfaction and very little on the issue of effective student learning relative to the course learning

objectives. And that limitation of student rating forms may call into question your reported approximate equivalence of the faculty

skills involved in those teaching the courses that form basis for this research.

We included a citation to Bandura (1997) as additional support for Hypotheses 3 and 4.

Stepping back a few steps, I return to my initial concern about the limitations of the Kolb experiential model. It is a model that is a-

contextual and does not consider learning beyond the cognitive. Affect is not part of the Kolb model. You now bring in a wider

discussion of alternative experiential models, and I appreciate that. But I am puzzled by your introduction of the issue of feelings on

page 15, lines 32-39. Is this an admission that emotions and feelings are part of the experiential learning process? It is my belief that

not only emotion but also imagination, intuition, and the physiological are all inseparably bound up with the cognitive in experiential

learning in the actual world of learning. The neuroscience citations I offered in my initial review support that claim. The focus solely

on the cognitive remains a limitation of your research conclusions, in my mind, and perhaps the major critique, through the Kolb

model, of your research design.

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We were careful to point out in the prior version that while our research is largely grounded in Kolb’s work, both our theorizing and

methodology took a broader and situated perspective to learning (this discussion flowed through the Introduction and was explicitly

addressed in the Methods on the prior page 15). Although not measured, the unfolding of experiential activities was designed to take

into account students’ emotional reactions to learning in progressively more difficult activities/situations. We do not make

conclusions about non-cognitive variables as we did not measure any. That said, our Potential Limitations and Future Research

Directions discusses how the inclusion of particular situational and non-cognitive individual difference variables in future research

would meaningfully add to our work and findings.

On the technical side, Knowles, Holton, & Swanson (2011), not (2005), is missing from your reference list. The first citations of

Colquitt et al (2000) (p.6) and Pintrich et al (1991) (p.16) should present all of the author names. You have minor errors in the

information presented for Pajares (2002) and Weil & McGill (1989) in the reference list.

We have corrected these references.

Again, well done on your revisions within the bounds of the Kolb model.

We thank the reviewer for the positive feedback on our revisions.

Reviewer 2’s Comments and Our Responses

Overall, I feel the authors have been somewhat responsive in this revision, and the manuscript has been improved as a result. However,

a number of my concerns remain, to varying degrees.

1. In response to my concern about construct ambiguity, the authors relabeled “instructional engagement” to “instructional method”

and “learner involvement.” However, I feel that the basic concern still remains, only now it pertains to “instructional method” and

“learner involvement,” rather than “instructional engagement.” For example, involvement is not clearly defined, could similarly mean

very different things to different people. I do feel some improvement has been made in clarifying the learning behaviors, however.

While our instructional conditions clearly focus on involving the learner with respect to progressively higher levels of dialogue,

reflection, and action, we recognize the continuing concern that our variable label for instructional method does not reflect the

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complete nature of our manipulation and the role of the instructor. To address this ongoing concern about construct ambiguity, we

made several edits. In the Method section (p. 12), we link our theoretical discussion (from the Introduction) concerning the roles of

dialogue (between students as well as between students and instructors), reflection, and action to the levels of our manipulated

variable. Here, we state “The manipulated variable was instructional method with three conditions that varied with respect to the

promotion of dialogue (student-student and student-instructor), reflection, and action. These educational conditions were lecture only,

lecture along with dialogue and guided reflection, and lecture along with dialogue, guided reflection and experiential exercises.” For

discussion purposes, we indicate that we will label these instructional conditions in the text to refer to them as low, moderate, and high

social interaction conditions, respectively. As such, we indicate that “This labeling of instructional conditions acknowledges not only

the role of learners as actors, but also the role of the instructor as an actor and facilitator of reflection.” In the tables, we only label

the instructional conditions as Low, Moderate, and High and provide table notes with operational definitions. If desired, we can add

the term “social interaction” to the Low, Moderate, and High labels if that helps with respect to consistency of

discussion/presentation.

2. As before with engagement, I also still wonder about the dimensionality of “methods”/“involvement.” I remain unconvinced that the

manipulation merely represents an increasing level of a single construct (or single dimension of a given construct). There are no true

manipulation checks that provide a clear sense of what exactly the manipulations represent. The authors do argue that psychological

safety represents a check on the level of involvement invoked by the methods, which I found a little perplexing, given that the

manipulation isn’t purported to be a manipulation of psychological safety per se, but rather psychological safety is hypothesized as an

outcome. So, beyond dimensionality concerns, I am more generally not convinced that we have a firm grasp on exactly what

construct(s) is being manipulated. This issue is also brought into focus by the fact that the low and moderate conditions didn’t appear

to function differently. Changing the label to “instructional method” does not clear this issue up for me.

We addressed this comment in our response to Comment 1, where we more explicitly indicate that our manipulation (the instructional

conditions) is designed to progressively include higher levels of dialogue, reflection, and action. Furthermore, our labelling of

instructional conditions with respect to “social interaction” also acknowledges the multi-dimensional nature of our manipulation.

This multi-dimensional nature of our manipulation is also aligned with the measurement of three constructs/learning behaviors that

are posited as consequent to the manipulation of instructional method.

We discussed why the moderate instructional condition did not function as expected in the prior version (now at the bottom of p.27 and

top of p. 28). If there is anything we can add to this discussion, then we are open to suggestions.

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3. I think the authors did a reasonable job responding to my comments regarding potential negative self-efficacy effects. However, I

will point out a couple of things that they might wish to revise further on that front. First, the authors indicate that Vancouver &

Kendall (2006) show a negative SE/effort relationship when students believe the grades are easily achievable. However, their study

doesn’t really access the “easily achievable” piece of this argument and, moreover, their logic suggests that perceptions of how easily

achievable a grade is is likely to be influenced by self-efficacy.

That said, some work by Schmidt and colleagues does provide some support for the argument the authors are making. First, Schmidt &

DeShon (2009) found an interaction between self-efficacy and goal progress (prior performance and perceived goal-performance

discrepancies), where a negative self-efficacy effect was observed when progress was high, and a negative relationship when progress

was low. Second, Beck & Schmidt (2010, Study 2) found a negative relationship between self-efficacy and effort when participants

were assigned an easy goal, but a positive relationship when assigned a difficult goal. I do think it is plausible that students perceived

the course as rigorous enough to where a positive relationship would be expected, but it likely that this (and other potential

moderators) varies across students, and within students across the semester. In light of all of this, I am not particularly surprised that

self-efficacy did not have significant relationships with the outcomes.

We looked into this issue further and added a reference to Beck and Schmidt (2012), which provided additional and solid support for

our arguments concerning the expected positive effects of self-efficacy on effort demonstration in progressively challenging situations

(such as our instructional conditions that reflect increasingly more demanding interpersonal contexts).

In addition to lingering concerns from the first round, I have a few additional comments:

4. In general, I wonder the extent to which any of this is unique to a business student sample. Given the focus of the journal, it makes

sense that the authors have directed their attention in that direction, but I don’t really see much of anything that indicates that these

relationships should be unique in direction or magnitude for business students vs. students in general. If this is believed to be the case,

I think the authors should make a stronger case. If it is not, then I think the authors need to do more to connect this to the broader

literature on learning/education.

The degree to which our findings are unique to students enrolled in business classes is an empirical question that, as we stated in our

earlier version (now on p.28), can best be addressed by future research examining “… the degree to which the present findings

generalize across different types of management education, educational contexts, and populations of learners and instructors ….”

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We were uncertain as to the meaning of the comment that we “need to do more to connect this to the broader literature on

learning/education” as our study is firmly grounded in this broader literature.

5. Getting back to my dimensionality comment, in the introduction the authors note the distinction between “reflection in action” and

“reflection on action.” However, operationally reflection is treated as a single dimension. Is this justifiable? Factor analyses of the

measures would be helpful.

While the distinction between “reflection-in-action” and “reflection-on-action” is theoretically important, our measure of reflection is

retrospective and focused on “reflection-on-action.” As such, our measure cannot adequately tap whether or not students “reflected-

in-action.” Designing a study and measure to adequately assess “reflection-in-action” relative to a number of group activities would

be a tall order.

Moreover, in the correlation matrix, all of the learning behaviors are aggregated into a single variable, despite reflection, student-

student dialog, and student-instructor dialog being treated as separate in the hypothesis tests. This makes it difficult to gauge the degree

to which dialog and reflection are distinct, as well as other checks on the data and analyses.

We reported the range of correlations between the dialogue and reflection measures in the prior version indicating that they had

relatively high inter-correlations ranging between .61 and .71 (now reported on p. 17). Per the Editor’s suggestion, we now report

tests of Hypotheses with the aggregated learning behaviors (overall) variable to better align the tests of Hypotheses with the model

that was tested.

6. In the methods section, please indicate whether the students are undergraduates, graduate students, a mix of both, etc. Also,

please provide some information concerning the variability in age. In some respects, this touches on my point about the extent

to which this is really about/unique to “management” education.

We note in the Methods section that these students were undergraduate students. In addition, we now report the standard deviation

for age of 1.64 in the Methods.

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7. I would like to see more details on the analyses regarding teaching evaluations. What are the sample sizes and, more to the

point, how much power did these analyses have?

The analyses and findings concerning the teaching evaluations are now reported on page. In total, these evaluations were based on

data from 1,138 students with the respective instructors being evaluated by 297, 116, 105, 272, and 348 students. As such, our

analyses had adequate power for detecting small effects.

8. Please provide some more details and key cites concerning the bootstrap bias-corrected standard errors used in the model tests.

We provide references (on p. 19) to Mooney and Davis (1993) and Davison and Hinkley (1997) for readers interested in bootstrap

methods including bias-correction and percentile-correction procedures.

9. Please provide more details concerning the tests of indirect effects

Table 5 was added with standardized direct, indirect and total effects.

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Theory in Practice: Increasing Professional Effectiveness.

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Situated learning: Legitimate peripheral participation

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Self-efficacy: the exercise of control

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A. Bandura

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William A. Kahn

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