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ACTIVE VERSUS PASSIVE TEACHING STYLES, Exercises of Business

ABSTRACT. This study compares the impact of an active teaching approach and a traditional (or passive) teaching style on student cognitive outcomes.

Typology: Exercises

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Download ACTIVE VERSUS PASSIVE TEACHING STYLES and more Exercises Business in PDF only on Docsity! Small Business Instutite® National Proceedings  Vol. 33, No. 1  Winter, 2009    ACTIVE VERSUS PASSIVE TEACHING STYLES: AN EMPIRICAL STUDY OF STUDENT LEARNING OUTCOMES Norbert Michel, Nicholls State University John Cater, Nicholls State University Otmar Varela, Nicholls State University ABSTRACT This study compares the impact of an active teaching approach and a traditional (or passive) teaching style on student cognitive outcomes. Across two sections of an introductory business course, one class was taught in an active or — nontraditional“ manner, with a variety of active learning exercises. The second class was taught in a passive or — traditional“ manner, emphasizing daily lectures. Although the active learning approach does not appear to have improved overall mastery of the subject, we did find evidence that active learning can lead to improved cognitive outcomes within a class. INTRODUCTION Due to increasing competitive demands both in the business world and in the academic community, management educators strive to provide the most productive classroom experience for their students in order to prepare them for careers in the business world. To achieve this objective, management educators constantly search for new and improved teaching methods. For many years, college instructors and professors in the United States have operated under a paradigm in which they sought to impart knowledge to students in a form of information transfer (Boyer, 1990). In this approach to teaching, students passively receive information from the professor and internalize it through some form of memorization. This process is characterized as passive learning (Stewart-Wingfield & Black, 2005). Although passive learning has been the dominant teaching method, many educators argue that students require more than a mere transfer of knowledge. Not surprisingly, the search for the best approach to business education has led educators to explore many different teaching techniques, ranging from the traditional lecture class or passive learning approach to various experimental approaches, such as active learning (Bonwell & Eison, 1991). While researchers intuitively suppose that active learning should be superior to passive learning, such superiority has proved somewhat difficult to quantify (Whetten & Clark, 1996). Although some studies claim that active learning is more effective than passive learning (Benek-Rivera & Matthews, 2004; Dorestani, 2005; and Sarason & Banbury, 2004), research directly comparing both methods are the exception. The fact that much of the active learning research has focused on attitudinal reactions (student satisfaction) rather than cognitive outcomes has complicated matters even more. Another difficulty in comparing previous studies is the wide range of activities that can be defined as active learning. The main purpose of the present study is to compare the impact of an active teaching approach and a traditional or passive teaching style on student cognitive outcomes. Our research is guided by the question, —Is the active teaching approach more effective than the passive teaching approach in regards to learning outcomes?“ Aside from our findings, our study differs from previous research by (1) using a dual-factor criteria (broad and specific learning) that facilitates comparisons between teaching styles; (2) clearly separating teaching Small Business Instutite® National Proceedings  Vol. 33, No. 1  Winter, 2009    approaches across two classes for a full semester; (3) studying a broad (and relatively large) sample of students; and (4) explicitly controlling for various student-specific factors as well as survivor bias. Literature Review In response to the increase in competition both in the business world and among business schools, excellence in teaching is becoming an essential avenue for faculty members to produce a competitive advantage for their colleges (McKeachie, Pintrich, Lin, & Smith, 1986). Given the significance of the need to improve teaching approaches, it is not surprising that many different teaching methods have been developed within the past thirty years. In management education, variations of active learning include experiential learning (Kolb, 1984), problem- based learning (Miller, 2004), participative learning (Mills-Jones, 1999), and cooperative learning (Johnson, Johnson, & Smith, 1991). We will briefly describe active learning and some other related teaching approaches that are commonly categorized as active learning, and then compare these methods with the traditional or passive approach. Lastly, we will compare our research with those studies that focus on comparing how the two teaching methods impact cognitive outcomes. ACTIVE LEARNING Active learning is a broadly inclusive term, used to describe several models of instruction that hold learners responsible for their own learning. The leaders in the field of active learning, Bonwell and Eison (1991) have contributed heavily to its development and to the acceptance of active learning as a viable approach. Proponents of active learning describe a process in which students engage in —doing things and thinking about what they are doing“ in the classroom (Bonwell & Eison, 1991, p. 2). Active learning encompasses various practices, such as pausing in lectures for students to consolidate their notes, interspersing short writing exercises in class, facilitating small group discussions within the larger class, incorporating survey instruments, quizzes, and student self-assessment exercises into the course, leading laboratory experiments, taking field trips, and using debates, games, and role play (Bonwell & Eison, 1991; Sarason & Banbury, 2004; Ebert-May, Brewer, and Allred (1997)). Bonwell and Eison (1991) suggest that active learning provides the following benefits: students are more involved than in passive listening; students are engaged in activities such as reading, discussing, and writing; student motivation is increased; students can receive immediate feedback; and students may engage in higher order thinking, such as analysis, synthesis, and evaluation. In order to have a positive effect on students, the management educator must apply the principles of active learning to the practical setting of the classroom. Auster and Wylie (2006) suggest that four dimensions are necessary to create a systematic approach to promote active learning in the classroom: context setting, class preparation, class delivery, and continuous improvement. Context setting refers to creating an open and relaxed atmosphere for learning in the classroom. Class preparation involves thought, planning, and creativity before the class session. Class delivery refers to the implementation of the planned lesson in the classroom. Continuous improvement entails seeking and using feedback concerning the teaching approach Other Related Teaching Approaches Experiential learning is an associated concept in which students learn from relevant experiences provided in the course of instruction (Kolb, 1984). Management educators should be aware of two cautions. First, experiential exercises alone may not be sufficient to induce learning and, secondly, students will need time to reflect on the experience (Stewart-Wingfield & Black, 2005). Kolb (1984: 41) explains that learning is a process, not an outcome; that learning comes from experience; that learning requires resolution of dialectically opposed demands; Small Business Instutite® National Proceedings  Vol. 33, No. 1  Winter, 2009    knowledge creation in Yang‘s (2003) learning theory, in which knowledge acquisition is assimilated to learning cognitive outputs (declarative knowledge), whereas knowledge creation assimilates learning to ongoing processes (procedural knowledge). We expect the use of a dual dependent variable contributes to enhance our understanding on the effect of teaching approaches (passive, active) over learning outcomes. Because the active teaching approach engages the student and stimulates student involvement in the course, we propose that this enhanced involvement and interest will result in a higher understanding of the subject. This improved learning outcome will manifest itself when students are tested on the general subject matter of a course. Therefore, we propose the following hypothesis: Hypothesis 1: Broad student learning outcomes are stronger in active teaching contexts than passive ones. Next, we directly investigate student mastery of the specific material covered in a class. Active learning, according to the leading proponents in the field, Bonwell and Eison (1991), should involve the students to a greater degree than the passive approach. When students are engaged in active exercises, their motivation to work in the class should increase. Given immediate feedback as the active approach suggests, students should more readily engage in higher order intellectual activities, such as analysis and synthesis of class material. Therefore, we propose that this enhanced feedback and intellectual activity will lead to increased student learning that will result in higher cognitive outcomes for those exposed to active (rather than passive) teaching when students are tested on the specific material covered in the class. Therefore, we also propose the following hypothesis: Hypothesis 2: Class specific learning outcomes are stronger in active teaching contexts than passive ones. Method EXPERIMENTAL DESIGN The main goal of our experiment was to test whether active learning methods, compared to passive learning methods, can improve cognitive outcomes among students. We conducted our experiment in two sections of an Introduction to Business class, each taught by a different instructor. The two sections were taught in consecutive time periods in the same classroom. The earlier class was taught with the active learning approach and is referred to herein as the active section. The later class was taught with the passive learning approach and is referred to herein as the traditional section. Each section started with approximately 150 students enrolled. In the active section, students were placed into groups of four to five individuals at the beginning of the semester. Students were placed into groups using Kolb‘s (1984) learning styles. The groups were designed so that students with different learning styles were placed in each group with as much variation as possible. The instructor assigned business projects for each group, due at the end of the semester that required the students to make many group-specific decisions. The instructor facilitated group-based critical thinking exercises and students engaged in class discussions in every class. All of the class discussions and exercises were geared toward integrating the class topics with the business plan projects. At the beginning of each class, students were given a short quiz assessing knowledge of the material covered in the prior class period. Overall course grades in the active section were compiled (equally) from quiz averages and grades on the business projects. Following Ebert-May, Brewer, and Allred (1997), the daily quizzes were used to provide an incentive for taking part in the class discussions and group exercises. As described, the active learning in this section consisted of elements of experiential, problem-based, participative, and cooperative learning. In contrast, the instructor in the traditional section employed the typical Small Business Instutite® National Proceedings  Vol. 33, No. 1  Winter, 2009    lecture method. Grades in the traditional section were predicated on three in-term exams and one final exam. All quizzes and exams across the two sections consisted of machine-graded multiple-choice/true-false questions. Manipulation Check A survey assessing participants‘ perceptions of teaching styles was applied. Three items inquired about delivery of instruction with respect to in-class activities, involvement opportunities, lecturing emphasis, and group work. Examples of these survey items were —The instructor devoted extended periods of time to lecturing in class“ and —Team work was highly encouraged in class“. A 7-point Likert scale was utilized to assess items. Because measure reliability reached standard levels (.72), items were aggregated. Then, both classes (active versus traditional) were contrasted via a t-test aimed at testing the extent to which participant perceptions of teaching styles differed. The t-test results indicate a significant difference (5.33, p < .05) between classes. Cognitive Outcome Measures The instructors in both sections designed their classes to teach the broad topics found in most introductory business courses. For example, both professors provided a broad survey of all functional areas of business. At the end of the semester, the instructors compiled a common exam, with each professor contributing 25 multiple choice questions. This 50-question common exam was then administered to students in both sections. As such, a standard 50 item questionnaire was utilized to test learning outcomes (both class-specific and broad- knowledge outcomes) across sections. As an incentive for performance, students were awarded 5 points on their final grade if they scored at least 90 percent, and 3 points if they scored at least 80 percent. To utilize the exam scores for testing class-specific knowledge, we relied exclusively on matching students‘ scores on the 25 questions provided by their instructor. To use the exam scores for testing broader knowledge, we relied on the overall score with one caveat. Because both teachers were aware of only the broad topic areas taught in both classes, several exam questions assessed specific items covered in only one section. While this feature of the exam caused us to rely on only 38 questions out of the original 50, it also prevented the possibility of either instructor teaching to address specific questions covered in the other section. Experimental Issues Random Assignment. As with any experimental study of this nature, potential bias-related issues exist. One problem is that students were not randomly assigned to the two sections of the course. This lack of random assignment would be most problematic if students registered for either section knowing that it would be taught using one particular method. While it is possible that this sort of self selection exists in our data, it is highly unlikely because more than 70 percent of the students in both sections were first-semester freshmen and neither professor made the experiment known to students prior to the start of the semester. Demographic Issues. Table 1 presents basic summary statistics for each section of the course. As seen on Table 1, the students in the traditional course had slightly lower high school GPAs and ACT scores. Freshmen accounted for 72 percent of the students in the traditional class, while they accounted for almost 80 percent in the active section. A slightly higher percentage of females were in the active course (50.35 vs. 46.53), and a slightly lower percentage of enrolled students withdrew from the active course (15 vs. 19). More than two-thirds of students in both sections attended public high schools. Table 1 also shows that students in the traditional class had lower final and core assessment grades. The —core assessment“ Small Business Instutite® National Proceedings  Vol. 33, No. 1  Winter, 2009    grades consist of only quiz averages for the active section, and only exam averages for the traditional class. In both the traditional and active classes, all cognitive outcome assessments consisted of multiple-choice/true- false questions that were machine graded. Student Withdrawal. Range restriction (or survivor bias) challenges our analysis in that withdrawing students are left out of the final sample. Our approach for handling student withdrawals is based on the work in Grimes and Nelson (1998), where two distinct styles of teaching introductory economics were studied. Because student attrition has been shown to bias OLS estimators (Becker and Walstad (1990)), we use the propensity score approach to account for the likelihood students will withdraw from a class. Under this approach, the propensity (probability) for dropping the course is estimated with a probit equation and then included as an independent variable in the main regression. Grimes and Nelson (1998) use the Heckman selection model in a similar fashion. We have checked our results using the Heckman approach, and the overall results are virtually identical to those reported in the paper. Analysis Probit Equation. The probit equation is as follows: Withdraw = α + β1Gender + β2Age + β3hsGPA + β4 ACT+ β5eACT + β6PerAbsent + e. (1) In the probit model, Withdraw is the student‘s binary choice of dropping the course through formal withdrawal (set to 1 for withdrawal and zero for remaining enrolled), Gender is set to one for males, hsGPA is the student‘s cumulative high school GPA, ACT is the student‘s composite ACT score, eACT is the student‘s score on the English portion of the ACT, and PerAbsent is the percentage of total classes the student missed. The strongest predictor of whether students would withdraw from the class is the variable for the percentage of classes missed (see Table 2). Each student‘s predicted probability of withdrawing is then included in our main regressions. We also checked the robustness of our results against several alternative probit equations (all probit models are run using robust standard errors), again finding virtually the same results (available upon request). OLS Equation. Our main OLS equation is as follows: Exam Score = α + β1GrLev + β2hsGPA + β3Priv + β4ACT + β5Gender + β6Age + β7PerAbsent + β8SectionID + β9p-hat + e. (2) For our dependent variable in model (2), we initially use the measure for broad student learning outcomes, the exam scores on the 38 common-question exam. The independent variables in model (2) are as follows: GrLev is the student‘s grade level, hsGPA is the student‘s cumulative high school GPA, Priv is an indicator set to one for students who attended a privately funded high school (included as a proxy for socioeconomic status), ACT is the student‘s composite ACT score, Gender is set to one for males, PerAbsent is the percentage of total classes the student missed, and p-hat is the propensity score (predicted probability of withdrawing) from the probit model. The remaining independent variable, SectionID, is an indicator variable set to one for students in the active course and zero for those in the traditional course. This variable, therefore, represents the marginal difference in the cognitive outcome for students in the active section. A statistically significant positive coefficient for SectionID, for instance, would indicate that students in the active course performed better on the exam questions than students in the traditional course. The above analytical procedures are then repeated using our second student learning outcome measure (for class-specific knowledge) as the dependent variable. RESULTS AND DISCUSSION Table 3 highlights summary statistics related to our cognitive outcome measures. These statistics show that when students were Small Business Instutite® National Proceedings  Vol. 33, No. 1  Winter, 2009    IMPLICATIONS In terms of broad learning goals, the active learning approach does not seem superior to the passive learning approach. For instance, our results imply that using the active learning approach instead of the passive approach will not produce better statistics students, better business students, better economics students, etc. Still, in terms of narrowly defined learning goals, the active learning approach can improve student learning outcomes. For example, if students in a particular course are —forced“ to engage through active learning methods because their grades depend on how well they engage, student learning can improve with regard to their class material. Our results might be best explained in terms of different types of learning. The literature on learning separates Declarative Knowledge (DK) from Knowledge Structures (KS). DK refers to amounts of information gained in learning whereas KS is based on the notion that information is part of complex entities (structures) wherein information is mentally arranged in patterns (Day, Arthur, & Gettman, 2001). Day et al., maintain that KS facilitate application of information into broader contexts as individuals learn to make sense of information by understanding its relationships with other concepts. In this context, our results imply students in the actively taught class do a better job learning (memorizing) the material they are exposed to, compared to those in the passively taught section. However, it does not appear that students in the actively taught class translated DK into KS. When confronted with questions not directly covered in class, these students were unable to infer answers from knowledge gained. In other words, students were unable to apply the knowledge they acquired (there was a lack of development of knowledge structures). If these implications are accurate, we can say that further research should separate DK from KS as dependent variables in testing teaching style outcomes. Further, because teaching with active learning methods can require additional class time, it is possible that using the active learning approach may result in sacrificing some base knowledge in a course. Perhaps active learning is more appropriate once students already have a foundation in the particular subject matter. Particularly in freshmen courses with high attrition, it may not be worth the time and effort to structure a course completely around the active learning approach. Instead, teachers should determine which areas of their subject matter are best suited for the active learning approach in order to supplement those areas where the passive approach is best. CONCLUSION Our study contributes to the management education literature with quantitative evidence that the active teaching approach may have a greater positive influence on student learning than the passive teaching approach in some contexts. Our results show higher student cognitive outcomes on specific material covered in a class taught with the active learning approach as opposed to one taught with the passive teaching approach. These results are consistent with those of Ebert-May, Brewer, and Allred (1997), who also found higher cognitive outcomes on specific material covered with an active teaching approach compared to a control group. In order to draw further and more general conclusions, future researchers may want to expand this type of study to include multiple subjects and/or even classes taught at multiple universities. Further research is especially needed concerning cognitive student outcomes as opposed to affective responses from students to determine the best teaching approaches for the advancement of management education. We would also add that greater care needs to be exercised in defining certain types of activities as —active learning.“ Because most studies utilize several types of active learning, Small Business Instutite® National Proceedings  Vol. 33, No. 1  Winter, 2009    it is not always clear precisely which active learning exercises may make a difference. For instance, the authors of the present study strongly suspect that having daily quizzes in the active section (so as to better engage students) could be largely responsible for the differences across the active and passive sections. We plan to test this possibility in future research. REFERENCES Albanese, M. A., & Mitchell, S. 1993. Problem-based learning: A review of the literature on outcomes and implementation issues. Academic Medicine, 68, 52-81. Auster, E. R., & Wylie, K. K. 2006. Creating active learning in the classroom: A systematic approach. Journal of Management Education, 30(2), 333-354. Becker, W.E., & Walstad, W.B. 1990. Dataloss from pretest to posttest as a sample selection problem. Review of Economics and Statistics, 72 (1), 184-188. Benek-Rivera, J., & Matthews, V. E. 2004. Active learning with jeopardy: Students ask the questions. Journal of Management Education, 28(1), 104-118. Bonwell, C., & Eison, J. 1991. Active learning: Creating excitement in the classroom, ASHEERIC Higher Education Report No. 1. Washington, D. C.: The George Washington University, School of Education and Higher Education. Boyer, E. 1990. Scholarship reconsidered. Lawrenceville, NJ: Princeton University Press. Dorestanni, A. 2005. Is interactive learning superior to traditional lecturing in economics courses? Humanomics, 21(1/2), 1-20. Ebert-May, D., Brewer, C., & Allred, S. 1997. Innovation in large lectures œ Teaching for active learning. Bioscience, 47(9), 601-607. Grimes, P. W., & Nelson, P. S. 1998. The social issues pedagogy versus the traditional principles of economics: An empirical examination. American Economist, 42(1), 56-64. Johnson, D. W., Johnson, R. T., & Smith, K. A. 1991. Active learning: Cooperation in the college classroom. Edina, MN: Interaction Book Company. Kirschner, P.A., Sweller, J. & Clark, R.E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry based teaching. Educational Psychologist, 41, 75-86. Kolb, D. 1984. Experiential learning. Upper Saddle River, NJ: Prentice-Hall. McKeachie, W. J., Pintrich, P. R., Lin, Y., & Smith, D. A. F. 1986. Teaching and learning in the college classroom: A review of research literature. Ann Arbor: Regents of the University of Michigan, ED 31499, pp. 124, MF-01: PC-05. Miller, J. S. 2004. Problem-based learning in organizational behavior class: Solving students‘ real problems. Journal of Management Education, 28(5), 578-590. Mills-Jones, A. 1999. Active learning in IS education: Choosing effective strategies for teaching large classes in higher education. Proceedings of the 10 th Australasian Conference on Information Systems, Wellington, New Zealand, 5-9. Miner, F. C., Jr., Das, H. & Gale, J. 1984. An investigation of the relative effectiveness of three diverse teaching methodologies. Organizational Behavior Teaching Review, 9(2), 49-59. Sarason, Y., & Banbury, C. 2004. Active learning facilitated by using a game-show format or who doesn‘t want to be a millionaire? Journal of Management Education, 28(4), 509-519. Stewart-Wingfield, S., & Black, G. S. 2005. Active versus passive course designs: The impact on student outcomes. Journal Small Business Instutite® National Proceedings  Vol. 33, No. 1  Winter, 2009    of Education for Business, 81(2), 119- 125. Van Eynde, D. F., & Spencer, R. W. 1988. Lecture versus experiential learning: Their different effects on long-term memory. Organizational Behavior Teaching Review, 12(4), 52-58. Whetten, D. A., & Clark, S. C. 1996. An integrated model for teaching management skills. Journal of Management Education, 20(1), 152- 18. Active Learning 23 Table 1 Descriptive Statistics of Students Sampled Basic Traditional Teaching Style Class Active Teaching Style Class Scores Mean Median SD n Mean Median SD n GPA 2.48 2.81 1.24 144 2.99 3.21 1.14 143 ACT 17.35 19.5 7.6 144 20.66 21 4.63 143 Age 21.79 20 6.87 105 19.62 19 3.68 94 Final Grade 77.7 77 10.59 116 85.35 87.32 8.42 122 Core Assessment 74.87 74.5 10.9 116 75.74 82.9 20.47 139 % Missed 16.63 17.64 12.5 144 18.41 9.1 24.94 142 Note. GPA = Grade point average; ACT = American College Testing. Core Assessment = Average scores for all quizzes and exams. SD = Standard deviation. Table 2 Summary Statistics for Learning Outcomes Learning Outcome Traditional teaching style Active teaching style Mean Median SD n Mean Median SD n Class Specific Learning 66.25 68.00 12.73 105 70.17 72.00 19.17 116 Broad Learning Outcomes 74.90 74.00 10.13 105 74.40 78.00 12.29 116 50-question Outcomes 63.41 64.00 11.82 105 56.64 58.00 13.83 116 Note. Class specific reports students‘ percentage of correct answers on questions only related to their class. Broad Learning Outcomes reports scores on the 38-question common test. For the sake of completeness, results for the 50-question test are also provided. Table 3 Probit Equation Results to Control for Bias Caused by Students Withdrawal Dependent β SE Variable Gender .59 .76 Age -.01 .04 HSGPA .19 .40 ACT .24 .19 English -.35 .22 Absentper 3.96 1.82 Constant -2.03 1.47 Pseudo R2 .32 Note. Gender = Set one for females. HSGPA = Grade point average from high school. ACT = American College Testing composite score. English = American College Testing English score. Absentper = Percentage of classes missed.
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