Business Essays - Business Disciplines

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Mixed Method Instruction Across Business Disciplines

The methods of teaching and pedagogical practices, supported by empirical research, are far too many for any teacher in a specific discipline to master and implement in the classroom. According to Svinicki (2000) research in teaching and classroom learning is overwhelming even for those who specialize in the field of teacher education. Thousands of studies have been conducted. Scholars have written compendiums covering decades of research in education and reviewing nearly 2,600 studies (see Pascarella and Terenzini (1991) and Feldman and Newcomb (1969) for examples). Nearly all recognized areas of learning and teaching have been researched. The cognitive, psychomotor, and affective domains first introduced by Bloom and his colleagues (1956) have been discussed, studied and implemented by classroom teachers. Teachers plan lessons and establish performance or behavioral objectives based on some level of cognition, affect or psychomotor learning they would like for students to achieve.

There are endless varieties of strategies and tools instructors can use in pursuit towards more effective teaching: Students can be placed in pairs (dyads) for the purpose of enhancing learning; teachers can use student-led discussion groups to cover topics; teachers can use cooperative or collaborative learning groups; teachers might use the full lecture, enhanced with two five minute breaks in the middle and at the end of their large lecture classes; teachers might select the mini-lecture of 20 to 30 minutes and then break his or her class into discussion groups to enhance learning; the teacher might quiz students at the beginning of class once or twice per week to create a more suitable attitude towards the course content and to verify that students have read assigned readings prior to class; teachers might grade homework in class to assure students completed it; teachers might assign students to groups and require a presentation on a specific topic. All those methods may be effective or they may not. Certainly, the effectiveness of many of them are supported by empirical research.

Cross (1999) offers a critically important overview in an article titled, What Do We Know About Students’ Learning, And How Do We Know It? After thoroughly examining the major issues in students’ learning, Cross (1999) offers a suggestion:

That research, however, is going to require of all of us a deeper level of understanding than the research of the past. Research should become the working partner of both our own experience with learning and focused conversations about learning with our colleagues. If we are taking learning seriously, we will need to know what to look for (through research), to observe ourselves in the act of lifelong learning (self reflection), and to be much more sensitively aware of the learning of the students that we see before us everyday.

Students’ learning is critically important to the student and the teacher. Teachers derive satisfaction from student learning. However, it is important that the teacher knows if what he or she does in the classroom is effective. The question then becomes: What are the best methods for classroom instruction and student learning? Although there are no magic bullets in teaching (meaning there are no cookie cutter approaches to what all teachers can do to maximize student learning at all times), there are practices that work better than others.

Reinforcement does work, such as quizzing students to motivate reading. An “A” to “F” grading scale structured in the correct course design works as a motivating factor. Teacher “wait-time” after asking a direct question works better than not waiting for a student response. Teacher’s praise works. Student-led discussion groups work. The Socratic method works if done correctly. There are many research supported classroom practices that work to aid the teacher in effectuating student learning. However, the problem does not lie in the credibility of the research studies. The problem is in the optimal application of accepted teaching practices.

Limitation and Delimitation

The study was limited to the learning preferences of students at a medium size regional institution of higher learning. The thirteen original statements reflect a very small number of potential teaching methods available for instructors to use in classrooms. No attempt was made to address all available methods for instruction; furthermore, the thirteen statements selected for this study merely reflect the preferred combined teaching methods of the three authors. The study results should not be generalized to any population other than the approximate 1,000 business majors sampled at the medium size university. It is also assumed that the findings are valid for all race classification for the total population sampled due to the fact that perceptions were measured pertaining to teaching practices supported by research findings at all other types of institutions.

Problem

The problem faced by most classroom teachers is three-fold: (1) Students must be diagnosed properly early in the semester for determining the range of the ability differences; (2) teachers must attempt to accommodate the range of ability differences and offer instruction that assures learning across that range; and (3) teachers are faced with learning style difference in both the homogenous and heterogeneous classroom; whereby at least a significant percent will not learn the content with a particular type of instruction in the first attempt. Generally it is thought that the teacher should employ an array of teaching methods and practices. This advice seems logical.

The approach of using an array of methods and practices is a problem for the teacher; a finite amount of time for grading, teaching, research and service prevents trial and error approaches. Busy teachers need to know what works best most of the time. Among the hundreds of methods determined useful in the research, what, if any, is an optimal combination of methods that could be deemed most effective for the teacher at the college level. The problem faced by business teachers at the college level is that many do not know where to start. Hence, this study began a process of sorting through a hand-full of teaching methods that students perceive contributed to their own learning and those that they perceive did little to contribute to their learning.

Procedure

Three business professors at a medium size regional university set out to determine student perceptions of their own learning. Eight classes were used in the study (two Introduction to Business classes, two Business Communication classes, two Principles of Microeconomics classes and two Personal Finance classes). The three teachers asked their own students to complete a survey during the last week of the semester or after the final examination. The survey was strictly voluntary. Students who volunteered received bonus points for completed surveys. Two hundred thirty eight students completed a survey containing 13 statements on teaching methods (related to empirical research) and several demographic items. As shown in Appendix A, the students responded to a five-item Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Table 1 shows important works that could be linked to each of the statements.

Table 1

Statements and Empirical Links

S1: I learned the subject better when the instructor lectured on a topic for 15 to 20 minutes. (for example, Doe, 1970; Smith and Wesson, 1999).

S2: I learned the subject better when the instructor placed students in dyads (two students) to practice vocabulary. (for example, Doe, 1970; Smith and Wesson, 1999).

S3: I learned the subject better when the instructor placed students in small groups composed of three to five members to solve a case outside of class. (for example, Doe, 1970; Smith and Wesson, 1999).

S4: I learned the subject better when the instructor placed us in student-led discussion groups in class to talk about various topics. (for example, Doe, 1970; Smith and Wesson, 1999).

S5: I learned the subject better when the instructor used the Internet to reinforce main points. (for example, Doe, 1970; Smith and Wesson, 1999).

S6: I learned the subject better when the instructor gave a “review” at least a week before an examination. (for example, Doe, 1970; Smith and Wesson, 1999).

S7: I learned the subject better when the instructor required students to write-out all vocabulary words and definitions by hand. (for example, Doe, 1970; Smith and Wesson, 1999).

S8: I learned the subject better when the instructor graded homework in class. (for example, Doe, 1970; Smith and Wesson, 1999).

S9: I learned the subject better when the professor tested students over three chapters from the textbook rather than five. (for example, Doe, 1970; Smith and Wesson, 1999).

S10: I learned the subject better when the instructor gave students two or more chances to redo an assignment. (for example, Doe, 1970; Smith and Wesson, 1999).

S11: I learned the subject better when the instructor called on students by name to answer specific questions. (for example, Doe, 1970; Smith and Wesson, 1999).

S12: I learned the subject better when I was given an assignment to be completed on my own. (for example, Doe, 1970; Smith and Wesson, 1999).

S13: I learned the subject better when the instructor required a group presentation. (for example, Doe, 1970; Smith and Wesson, 1999).

Literature Review

Results

Research questions were presented that this study answered. These questions were converted into null hypotheses for statistical testing. The purpose of this study was to determine if the 13 original statements associated with students’ perceptions of their own learning could be reduced to a smaller number of factors without losing most of the meaning for the original combined statements Initially, an exploratory factor analysis using squared multiple correlations (SMC) as prior communality estimates was used. The principal factor method using Promax Rotation was performed. Correlation analyses were performed to assess significant correlations among demographic variables and the three factors. Multiple regression analyses were performed using the derived factors as criterion variables to determine the significance of college majors, grade level attainment, and gender on the three derived factors.

The three derived factors were used as the dependent variables to measure the predictive effect of the independent demographic variables. The null hypothesis was rejected if the F value was significant at the .05 confidence level. Rejecting the null hypothesis meant that the predictor variables significantly influenced that factor’s variance. Adjusted R-squared statistics were used to determine the combined influence that the predictors had on the criterion variable or factor. Beta weights (standardized multiple regression coefficients) were reviewed to assess the predictive significance of variable (college major and grade level attainment) on each factor (F1: Cooperative Learning Dyads And Small Groups; F2: Socratic Inquiry With Independent Learning; and F3: P---O, Expectancy Probability That Performance Will Produce Desired Outcome).

Descriptive Data

Surveys were disseminated to a total of 238 college undergraduate students by their instructors. They were selected randomly based on their enrollment. All eight courses were either University Core or College of Business (COB) Core requirements. All COB students are required to complete Principles of Microeconomics and Business Communication. Each class sampled was assumed to be normally distributed and all eight courses combined were representative of the entire COB student body. Near the end of the Spring 2003 and the middle of the Fall 2003, a total of 238 surveys had been completed, providing an overall return rate of 100%. However, 20 surveys returned were not usable due to selection set bias. Table 2 presents information concerning usable questionnaire returns for the two groups.

Table 2

Usable Questionnaire Returns by Groups

Group Type

Group Size

Usable Returns

Usable Percent

Business Students

238

220

99

Analysis of the demographic data revealed that 121 males and 97 females completed the survey. The average credit hours completed was 52 hours with a standard deviation of 32 hours. The declared major of the respondents was: Accounting – 24, Management – 54, Marketing – 18, Finance – 16, MIS – 48, Double-major – 29, and Non-business major – 29. Among the respondents, there were 50 Freshmen (which comprised 23% of the respondents), 69 Sophomores (32%), 73 Juniors (33%) and 26 Seniors (12%). Table 3 presents the breakdown of respondents across majors and class standings. Respondents were not asked to report their ethnicity. One open-ended question appeared at the end of the survey and asked “Please add any comments you like.” Twenty-five of the 218 respondents responded to the open question.

Table 3:

Class

Accounting

Management

Marketing

Finance

MIS

Double Major

Non-Business

Senior

0

6

3

1

5

5

6

Junior

6

20

4

5

23

4

11

Sophomore

10

14

6

5

16

9

9

Freshman

8

14

5

5

4

11

3

Total

24

54

18

16

48

29

29

Respondents were asked to circle the most important statement for each of the original 13 statements. This question was used as a device to alter respondents' likelihood to develop selection sets when answering the questions. Table 4 provides detailed information regarding the most frequently selected responses for each of the original 13 statements as well as the means and standard deviations for each. Statements are indicated in Table 4 by “S1, S2, S3, etc.” For example, S2 refers to statement 2, “…instructor placed students in dyads…”

Table 4

Students’ Perceptions of Learning: Means, Standard Deviations, and Percent of Responses Indication Level of Agreement With Statement:

Statement

M

SD

5

4

3

2

1

S1

3.7

1.13

90.0

4.0

2.0

2.0

1.0

S2

3.5

1.07

90.0

4.0

2.0

2.0

1.0

S3

3.7

1.06

90.0

4.0

2.0

2.0

1.0

S4

3.7

1.06

90.0

4.0

2.0

2.0

1.0

S5

3.6

1.12

90.0

4.0

2.0

2.0

1.0

S6

4.3

1.11

90.0

4.0

2.0

2.0

1.0

S7

3.3

1.19

90.0

4.0

2.0

2.0

1.0

S8

3.4

1.07

90.0

4.0

2.0

2.0

1.0

S9

4.2

1.18

90.0

4.0

2.0

2.0

1.0

S10

4.0

1.14

90.0

4.0

2.0

2.0

1.0

S11

3.4

1.23

90.0

4.0

2.0

2.0

1.0

S12

3.8

1.1

90.0

4.0

2.0

2.0

1.0

S13

3.5

1.18

90.0

4.0

2.0

2.0

1.0

Note. * = mean below one Standard Deviation of the Mean. “S” = Statement (see Appendix A for teaching method statement). “S#” = Statement number. ** percents based on 218 respondents.

Factor Analysis

Responses to the 13 item survey were subjected to an exploratory factor analysis using squared multiple correlations (SMC) as prior communality estimates. The principal factor method was used to extract the factors and was followed by a Promax Rotation. A Scree test suggested, as can be seen from Graph 1 below, three meaningful factors; so only three factors were retained for rotation.

The rotated factor pattern is presented in Table 5. In interpreting the rotated factor pattern, an item is said to load on a given factor if the factor loading was .45 or greater for that factor (Gradagnoli & Velicer, 1988) and was less than .45 for the others.

Table 5

Rotated

Variable

Factor Loadings

1

2

3

Uniqueness

S1

-0.15

0.20

0.48

0.69

S2

0.57

-0.25

0.32

0.51

S3

0.82

0.12

-0.23

0.44

S4

0.61

0.11

0.04

0.53

S5

0.07

0.36

-0.02

0.85

S6

-0.05

0.72

-0.01

0.51

S7

0.21

0.03

0.26

0.81

S8

0.12

0.29

0.13

0.79

S9

0.05

0.67

0.01

0.51

S10

0.14

0.26

0.26

0.68

S11

0.07

-0.08

0.55

0.70

S12

-0.16

0.12

0.57

0.66

S13

0.63

-0.10

-0.03

0.66

Using these criteria, four items were found to load on the first factor (S2, S3, S4, S13) which was subsequently labeled “Cooperative Learning Dyads And Small Groups”; three items loaded on factor two (S1, S11, S12) which was labeled Mini Lecture and Socratic Inquiry With Independent Learning” and two items loaded on factor three (S6, S9) which was labeled “P-O, Expectancy Probability That Performance Will Produce Desired Outcome.” Table 6 presents the three new factors, final communality estimates and item descriptions.

Table 6

New Factors, Item Descriptions and Final Communality Estimates

New Factor One: Cooperative Learning Dyads And Small Groups

h1

S2: I learned the subject better when the instructor placed students in dyads (two

students) to practice vocabulary.

.51

S3: I learned the subject better when the instructor placed students in small groups

composed of three to five members to solve a case outside of class

.44

S4: I learned the subject better when the instructor placed us in student-led discussion

groups in class to talk about various topics.

.53

S13: I learned the subject better when the instructor required a group presentation.

.66

New Factor Two: Mini Lecture and Socratic Inquiry With Independent Learning

h1

S1: I learned the subject better when the instructor lectured on a topic for 15 to 20

minutes.

.69

S11: I learned the subject better when the instructor called on students by name to

answer specific questions.

.70

S12: I learned the subject better when I was given an assignment to be completed on

my own.

.66

New Factor Three: P-O, Expectancy Probability That Performance Will Produce Desired Outcome

h1

S6: I learned the subject better when the instructor gave a “review” at least a week

before an examination.

.51

S9: I learned the subject better when the professor tested students over three chapters

from the textbook rather than five.

.51

Comparability between sample and population patterns could be a limitation concerning the adequacy of the sample size. The rule of thumb for an adequate sample size to conduct a factor analysis and derive an accurate solution ranges from 2:1, depending on the author and the publication. These reported inconsistencies led to a literature review regarding sample size and accuracy of the solution when performing factor analysis. One article was obtained which directly dealt with the relation of sample size to the stability of component patterns (Gaudagnoli & Velicer, 1988). The authors stated:

Contrary to popular rules, sample size as a function of the number of variables was not an important factor in determining stability. Component saturation and absolute sample size were the most important factors. To a lesser degree, the number of variables per component was also important, with more variables per component producing more stable results… a sample size of 150 observations should be sufficient to obtain an accurate solution… If components possess four or more variables with loadings above .60, the pattern may be interpreted whatever the sample size. (p.28)

The authors used a Monte Carlo procedure to vary sample size. Number of variables, number of components, and component saturation in order to examine systematically the condition under which a sample component pattern becomes stable relative to the population. The principal factor analysis, using a Promax Rotation, revealed high factor loadings above .60 for five of the nine factor loadings. Thus, the component pattern derived from the factor analysis was stable and the factor pattern was interpretable to the population. The sample size of 218 was considered adequate. To ascertain if there were any differences in students’ perceptions among the demographic variables (grade level, college declared major and gender), results were analyzed using bivariate correlations, multiple regression, and a two-way multivariate analysis of variance (MANOVA), with between-groups design.

Hypotheses Testing

A MANOVA procedure was used to ascertain whether differences existed between three independent variables (a) college grade level, (b) declared major, and (c) gender regarding students’ perceptions of teaching practices on their own learning. Pillai’s trace criterion was used to determine the acceptance or rejection of the null hypotheses since Pillai’s Trace is a better criterion for determining significance than Wilk’s lambda when there are unequal cell sizes and the assumption of homogeneity of variance is violated. Table 7 summarizes the MANOVA results for the three null hypotheses tested at the .05 significance level.

Table 7

Summary of Two-Way Multivariate Analysis of Variance with Between Groups Design

Source

Pillai’s Trace

DF

F Statistic

p-value

Model

0.268

30,621

2.03

0.001

College Grade Level

0.069

9,621

1.62

0.105

Declared Major

0.190

18,621

2.33

0.001

Gender

0.017

3,205

1.17

0.323

Research question one focused on whether a difference existed among college students at different grade levels regarding their perceptions of teaching practices. The null hypothesis was:

Ho1: There is no statistically significant difference among students at different college grade levels on measures of perception regarding teaching practices.

This hypothesis was analyzed using a two-way MANOVA with between-groups design. The analysis revealed a marginally significant multivariate effect for the different grade levels with Pillai’s Trace = 0.069, F(9,621) = 1.62 and p value = 0.105. The null hypothesis therefore cannot be rejected at 5%.

Research question two focused on whether a difference existed among college students with declared majors regarding their perceptions of teaching practices. The null hypothesis was:

Ho2: There is no statistically significant difference among declared business and non-business majors on measures of perception regarding teaching practices.

The second hypothesis was analyzed using a two-way MANOVA with between-groups design. The analysis revealed a significant multivariate effect for declared business majors with Pillai’s Trace = 0.190, F(18, 621) = 2.33 and p value = 0.001. The null hypothesis therefore can be rejected even at 0.1% level of significance.

Research question three focused on whether a difference existed among male vis-à-vis female students regarding their perceptions of teaching practices. The null hypothesis was:

Ho3: There is no statistically significant difference among male vis-à-vis female students regarding their perceptions of teaching practices.

The third hypothesis was analyzed using a two-way MANOVA with between-groups design. The analysis revealed an insignificant multivariate effect for gender with Pillai’s Trace = 0.017, F (3, 205) = 1.17 and p value = 0.323. The third null hypothesis therefore cannot be rejected.

Table 8

The p-values of testing the hypotheses of no significant difference between the mean vectors of each grade level and the other grade levels.

Group

Senior

Junior

Sophomore

Freshman

Senior

0.5357

___

___

___

___

Junior

0.0618

0.5800

___

___

___

Sophomore

0.0882

0.2385

0.0078

___

___

Freshman

0.6646

0.4894

0.1584

0.8733

___

Table 9

The p-values of testing the hypotheses of no significant difference between the mean vectors of each major and the other majors.

Group

ACCY

MGMT

MRKT

FIN

MIS

Double Major

Non-Business

ACCY

0.6507

___

___

___

___

___

___

___

MGMT

0.6289

0.4762

___

___

___

___

___

___

MRKT

0.8599

0.9345

0.6772

___

___

___

___

___

FIN

0.7812

0.9171

0.7065

0.7981

___

___

___

___

MIS

0.0006

0.0164

0.1784

0.0930

0.0666

___

___

___

Double Major

0.0507

0.0773

0.4713

0.2021

0.2956

0.3595

___

___

Non-business

0.0003

0.2114

0.0012

0.1811

0.1586

0.0000

0.0002

___

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