A Meta-Analysis of the Effectiveness of Small-Group Instruction Compared to Lecture-Based Instruction in Science, Technology, Engineering, and Mathematics (STEM) College Classes

Principal Investigator: 
Co-Investigator: 
Project Overview
Background & Purpose: 

The significance of the project stems from the numerous and consistent calls from federal agencies, employers, and national societies for an urgent need for STEM instructional reform and innovation in K-12 through graduate school. These organizations have emphasized the need to examine current teaching practices and student-learning processes with an emphasis on various forms of active small-group learning pedagogies such as cooperative, collaborative, problem-based learning, or team-based learning methods. These methods of learning/teaching emphasize effective communication and interactive teamwork skills among the students in the STEM college classrooms, as opposed to the traditional lecture-based instruction. These educational goals can be accomplished by using active small-group learning/teaching methods that provide students with self-directed and real-life learning experiences that help STEM graduates to be competitive in the global workplace. By  replacing or supplementing the traditional lecture-based instruction with the various active small-group learning pedagogies in STEM college classrooms, the learning power shifts from the instructor to the learner, and is deemed to be far more effective in promoting higher achievement, better attitudes towards subject matters, and persistence in STEM classrooms as compared to the traditional lecture-based instruction.

A major purpose of this meta-analytic study was to produce rigorous scientific evidence and an answer to the question of whether the use of various forms of small-group learning methods in STEM college classrooms is more effective than the traditional lecture-based instruction in promoting higher achievement and attitudes toward STEM subjects as well as persistence in STEM college classrooms.  This is an important question and is particularly challenging if it is to be answered in a way that can provide useful information for educators and policy makers on the most effective pedagogies of teaching/learning STEM college courses. The answer will help them to design future instructional environments that can be implemented successfully in real STEM college classrooms.

The design and implementation of this project produced three major meta-analytic data sets, one data set for each of the three outcome measures (achievement, attitude, and persistence). Each data set presented formidable logistical challenges with respect to (a) searching and identifying the STEM primary studies based on preset rigorous inclusion/exclusion criteria, (b) reducing the publication bias by searching the library databases for dissertations and unpublished reports, (c) coding of the study features and characteristics and the necessary summary statistics for calculating the effect sizes of the 193 primary studies included in the review, (d) ensuring the accuracy and quality of the coding process by training the coders and designing a comprehensive coding instrument, (e) using the multilevel approach for meta-analysis to increase the generalizability of the findings of this study, and (f) using sensitivity and publication bias analyses to assess the meta-analytic decisions that were made and the conclusions that were reached. The process of confronting these challenges over the period of this project has produced valuable lessons for future meta-analysts who conduct large scale meta-analytic project.

Specifically, six major research questions had guided this work:

  1. How effective are active small-group teaching methods including cooperative small-group instruction compared to lecture-based instruction in STEM courses in promoting better achievement, persistence, and more positive attitudes toward STEM fields of study?
  2. How much variability exists in the effect sizes across the primary studies within the three broad categories of the outcome measures (achievement, persistence, and attitudes) toward STEM fields of study?
  3. Which of the explanatory moderator variables explain the variability in the effect sizes across the STEM primary studies within the three broad categories of the outcome measures (achievement, persistence, and attitudes) toward the STEM field of study)?
  4. What are the potential sources of bias in the proposed meta-analysis study?
Setting: 

The research setting includes library and manual searches to locate the primary studies for the project. Research activities related to the extensive and exhaustive literature searches and setting the inclusion/exclusion criteria since the start of the project, January 1, 2009, and ending on December 31, 2011, included the following

Searching and collecting the STEM primary studies

The research team used extensive search procedures to identify published and unpublished primary studies that focus on the effectiveness of active small-group instruction/learning compared to lecture-based instruction and individualistic instruction in STEM fields of study. The keywords used in this study included “cooperative learning”, “collaborative learning”, “Problem-based learning”, small-group learning”, “Peer learning”, Team-based learning” as the key learning pedagogies combined with “Science”, “Technology”, “Engineering”, “Mathematics”, “Statistics”, “Biology”, Chemistry, “Physics”, “Health”, “Nursing”, “Computer” as the STEM discipline and fields of study descriptors. These two keywords were also combined with sample descriptors including “College”, “Undergraduate Education”, and Undergraduate Students”, and “College Students”.

Library searches conducted through:

  1. Electronic databases, such as ERIC, JSTOR, ProQuest Dissertations, WorldCat Dissertations and Theses, Abstracts International, CINAHL, Web of Science, Google Scholar, Directory of Open Access Journals, Academic OneFile, General OneFile, and PsycINFO, as well as print and online individual journals. For example, “Journal of Technology Education”, “Journal of STEM Education”, “Journal of Statistics Education”.
  2. References of the previously conducted meta-analysis reviews (e.g., Springer, Stanne, & Donovan, 1999; ) to identify other potential relevant primary studies that can be included in the review.
  3. References of the located primary studies to identify other potential relevant primary studies that can be included in the review.
  4. Contacting STEM researchers who are involved in STEM research to identify  

In sum, the team included in this meta-analytic review both published and unpublished sources to minimize the possibility of publication bias. Every effort was made by the team to locate all relevant STEM primary studies.

Developing inclusion/exclusion criteria for the STEM primary studies

Five stringent inclusion criteria were established for this project to determine whether a STEM primary study was qualified to be included in the final data set of the meta-analytic review.

  1. The first criterion was selecting two-group experimental, quasi-experimental, and comparative designed studies that compared the achievement, persistence, and/or attitudes of college students taught using active small-group instruction (Cooperative Learning, Collaborative Learning, Small-Group Learning, Problem Based Learning, Peer Learning, and Team Based Learning) to their counterparts, who taught using traditional lecture-based instruction in STEM classes. Therefore, one-sample pre-post primary studies that examined the change in the students’ achievement and attitudes as a result of implementing small-group learning methods were excluded from this meta-analytic review.
  2. The second criterion was the availability of the necessary descriptive and sufficient statistics of the outcome variables (Mean, Standard Deviation, Sample Size) for both groups (the group of students who were taught using small-group learning methods and the group of students who were taught using lecture-based instruction) in each of the STEM primary studies. The primary studies that reported the t-values, F-values, or the exact significance levels of the comparisons were also included in the review and the effect sizes were calculated from these test statistics. In addition, if a study reported the grade distribution of the scores and not the summary statistics, then the summary statistics and the effect sizes were calculated from these distributions. Furthermore, if a study reported the exact p-value of the test statistics and not the value of the test statistics itself (e.g., p-values of t-test), then the effect size was calculated from the reported p-value. But, a study was excluded if the primary study failed to report the exact significance level (reported p<0.05 instead of p = 0.05).
  3. The third criterion was that the primary study must focus on one of the Science, Technology, Engineering, and Mathematics (STEM) disciplines and fields of study.
  4. The forth criterion was that the primary study must focus on one of STEM subject matters (e.g., biology, chemistry, physics, medical technology, information technology, computer, mathematics, statistics, health, nursing, social science, psychology, and engineering). Thus, primary studies that focused on subject matters such as business and education were excluded.
  5. The fifth criterion was that the STEM primary study must focus on undergraduate college students and courses. Therefore, primary studies that focused on graduate level courses were excluded. For example, primary studies that focused on medical education were excluded because medical education is considered as a graduate level education. But, studies that focused on undergraduate pre-medicine classes were included in the review.
  6. The sixth criterion was that the primary study must have achievement scores, attitude scores, and/or persistence information reported in the STEM primary studies. Therefore, primary studies that reported the effectiveness of small-group learning on college students’ self-esteem, motivation, satisfaction, and enjoyment were excluded from this review.

During the project period, the research team had located and read about 7,498 STEM primary studies and identified a total of about 193 primary studies that focused on the three outcome measures (achievement, attitude, and persistence) that were qualified to be included in the meta-analytic review using the established inclusion criteria set for this project. Table 3.1 summarizes and lists the reasons for excluding the STEM primary studies from the current meta-analytic review. Many studies were excluded because the studies’ foci were not STEM undergraduate college education. A large number of other studies were eliminated because student achievement, attitudes toward STEM subject, or persistence in STEM classrooms was not the dependent variable. Also, a large number of studies were not included because it did not have control groups.

Research Design: 

The research design for this project was a synthesis project that used meta-analysis methods. the primary empirical studies were that examined and explored the effectiveness of active small-group learning and teaching methods in different STEM fields compared to lecture-based instruction.

Meta-analysis is a quantitative statistical method for synthesizing and integrating descriptive statistics from multiple primary research studies that address and test the same research question and hypothesis (Glass, 1976; Hedges and Olkin, 1985; Kalaian and Raudenbush, 1996; Kalaian and Kasim, 2008). The main goals of integrating the descriptive summary statistics from the multiple primary studies are to produce summary measures of (a) the average effect of the outcomes based on the reported summary statistics from the published experimental treatments (interventions), quasi-experimental, and non-experimental primary studies, and (b) the uncertainty and heterogeneity in the collection of the primary studies under review and model this heterogeneity to explain some of the variability.

Meta-analysis methods provide many advantages to researchers and policy makers for policy and decision-making purposes. Objectivity, bias reduction, statistical power, improved generalizability, research quality control, and precision of the meta-analytic quantitative estimates are examples of such advantages. The multilevel meta-analysis (mixed-effects, or random-effects regression) methods (Kalaian and Kasim, 2008; Raudenbush and Bryk, 2002) was used to synthesize and integrate the accumulated STEM literature on the effectiveness of various forms of active small-group instruction (e.g., cooperative, collaborative, problem-based, inquiry-based) compared to lecture-based instruction on undergraduate students’ achievement, persistence, and attitudes.  In the following section, a brief summary of the meta-analysis design that was used for this project is provided.

In this study, the independent effect sizes were estimated and calculated to measure the effectiveness of active small-group learning instruction (e.g., cooperative-learning, collaborative-learning, problem-based, peer learning, inquiry-based learning, peer-led team learning, team-based learning) compared with the lecture-based instruction in evaluating college students’ achievement scores, persistence, and attitudes toward STEM fields. Therefore, the effect sizes were estimated and reported separately for each major outcome category (achievement, persistence, and attitudes toward STEM fields). For each major outcome, several effect sizes were extracted from a single primary study as long as they were independent and deal with distinguishable separate subgroups (e.g., male and female groups). These effect-size indices were calculated based on the research design of the primary study. For example for two-group post-only research design, the effect-size index was calculated by taking the mean performance (achievement) difference between the small-group and the lecture-based groups and dividing the difference by the pooled standard deviations.

Two main types of two-level Hierarchical Linear Modeling (HLM) for meta-analysis were performed to analyze the data. First, by combining the effect sizes extracted from all the STEM primary studies, an overall effect size across all studies were calculated and tested for statistical significance using the basic unconditional Hierarchical Linear Model via the HLM6 software. This unconditional Hierarchical Linear Model, where no explanatory moderator variables are included, helps the meta-analyst to estimate and examine the heterogeneity in the primary studies’ effect sizes in order to assess the need for modeling this heterogeneity in the subsequent conditional between-studies models. Second, conditional Hierarchical Linear Modeling analyses were performed to investigate the extent to which study features moderated the effect sizes using HLM moderator analyses (Kalaian & Kasim, 2008; Raudenbush & Bryk, 2002).

A Brief Overview of the Meta-Analysis Steps

To give a clear picture of the meta-analysis design for the present meta-analytic study, the main steps that were taken to conduct the present study are summarized below. The detailed processes of each of the steps are provided in the subsequent chapters.

Identification of the Relevant STEM Studies

The research team used extensive search procedures to identify published and unpublished primary studies that deal with the effectiveness of active small-group instruction/learning compared to lecture-based instruction in STEM undergraduate college classrooms across various STEM disciplines and fields of study. Searches were conducted through:

  1. Electronic databases, such as ERIC, JSTOR, ProQuest Dissertations and Theses, and PsycINFO, as well as print library searches.
  1. References of the located primary studies and previously conducted meta-analytic reviews to identify other potential by relevant primary studies that could be included in the review.
  1. Published STEM conference proceedings (e.g., the proceedings of the American Society for Engineering Education).
  1. Contacting STEM researchers who are involved in STEM research to identify additional most recent STEM primary studies.   

In conclusion, the research team included both published and unpublished sources, such as the Dissertation Abstracts International database and ProQuest Dissertations as well as conference proceedings, to minimize the possibility of publication bias. More detailed description of the STEM literature searches is provided in Chapter 3.

Determination of the Inclusion Criteria

Stringent inclusion criteria were established to determine whether a primary STEM study is qualified to be included in the meta-analytic review. One of the most significant criteria was including experimental and comparative designed studies that compared active small-group instruction (e.g., cooperative, problem-based, or collaborative) to lecture-based instruction on college (two-year and four-year) students’ achievement, persistence, and attitudes in Science, Technology, Engineering, and Mathematics (STEM) classes. Another important criterion was the availability of the necessary descriptive and sufficient statistics for both groups in the primary studies in order to quantitatively integrate these studies. More detailed description of the inclusion/exclusion criteria is provided in Chapter 3.

Coding of Study Features and Outcome Measures

Study features were coded by at least two coders (Both of the Co-PIs and a graduate student) to examine the methodological and substantive characteristics that might contributed to the variations in findings among studies. Based on the review of the small-group effectiveness literature, a coding framework was constructed to cover the features of each primary study (e.g., research design, publication year, sample characteristics, contextual study features, instructional duration, type of small-group instruction (e. g., instruction type, group size, performance test type, study design). These identified study features were organized into at least three major categories (e.g., outcome measures, methodological, and substantive features). More detailed description of the development of the coding measures and the coding process are provided in Chapter 4.

Estimating and Calculating Effect Sizes

In this study, the independent effect sizes were estimated and calculated to measure the effectiveness of active small-group learning instruction (e.g., cooperative-learning instruction, collaborative-learning instruction, problem-based learning, inquiry-based learning) compared with the traditional lecture-based instruction in evaluating college students’ achievement scores, persistence, and attitudes toward STEM fields. Therefore, the effect sizes were estimated and reported separately for each major outcome category (achievement, persistence, and attitudes toward STEM fields). For each major outcome, several effect sizes were extracted from a single primary study as long as they are independent and deal with distinguishable separate subgroups (e.g., male and female groups; senior, junior, freshman, and sophomore college years; minority and nonminority students). For achievement and attitude outcomes, the effect-size indices were calculated by taking the mean performance (achievement or attitude) difference between the groups who were instructed using various forms of small-group learning methods and lecture-based groups and dividing the mean difference by the pooled standard deviation. Then, the unbiased effect sizes were calculated by multiplying the effect sizes by the bias-correction factor, which is a function of sample sizes (Hedges and Olkin, 2005; Kalaian and Kasim, 2008). More detailed description of the effect-size calculations is provided in Chapter 5. For persistence outcome, the effect sizes were calculated by taking the difference in percentages of students who withdrew from the STEM classrooms and/or failed the course from the classrooms that used small-group learning methods and the classrooms that used lecture-based instruction.

Multilevel Modeling approach for Meta-analysis

Two main types of two-level Hierarchical Linear Modeling (HLM) for meta-analysis were performed to analyze the data. First, by combining the effect sizes extracted from all the STEM primary studies, an overall effect size across all studies was calculated and tested for statistical significance using the basic unconditional Hierarchical Linear Model via the HLM 6 software. Generally, the unconditional Hierarchical Linear Model, where no explanatory moderator variables are included, helps the meta-analyst to estimate and examine the heterogeneity in the primary studies’ effect sizes in order to assess the need for modeling this heterogeneity in the subsequent conditional between-studies models. Second, conditional Hierarchical Linear Modeling analyses were performed to investigate the extent to which study features moderated the effect sizes using HLM moderator analyses (Kalaian and Kasim, 2008; Raudenbush and Bryk, 2002).

Findings: 

A total of 193 studies met the established criteria and were included in the meta-analysis. One-hundred and twenty two of the 193 primary studies focused on students’ achievement scores in STEM college courses, another 33 of the 193 primary studies focused on students’ attitudes toward STEM subject matter in the college classrooms, and the remaining 46 studies focused on students’ persistence (retention) in STEM college classrooms.. For the achievement and attitude primary studies, 158 and 33 standardized mean-differences effect sizes were extracted respectively. For the persistence primary studies, 70 proportion-difference effect sizes were extracted.

The results of the present meta-analytic review show that during the last four decades many different forms of small-group learning methods (cooperative learning, collaborative learning, problem-based learning, team-based learning, peer learning, and inquiry-based learning) have been developed, used, and evaluated in STEM college classrooms. The results also show that when compared with lecture-based instruction, all forms of small-group learning methods had positive impacts, in varied degrees, on student achievement, attitude, and persistence in various STEM college courses. For example, the results show that the weighted average effect-size for achievement was 0.37 using various forms of small-group learning methods across all STEM disciplines. An average effect size of 0.37 means that students’ STEM achievement is changed from the 50th percentile for the students who had been taught by the traditional lecture-based instruction to the 65th percentile for the students who had been instructed using various forms of small-group learning methods. In addition, the results show that there are differential effects of various forms of small-group learning methods across the different STEM disciplines. For example, the technology primary studies had a weighted average effect-size of 0.55, while the mathematics primary studies had a weighted average effect-size of 0.33.

Furthermore, the results show that various forms of small-group learning methods were effective in (a) promoting students’ attitudes towards STEM subject matters with a weighted average effect-size of 0.31, and (b) reducing students’ withdrawal level and failure in STEM college classrooms by 7%.

The findings of this research are consistent with and confirm previously reported and published meta-analytic findings and conclusions about the effectiveness of small-group learning methods in increasing students’ achievement in STEM college classrooms (e.g. Springer, Stanne, and Danovan, 1999; Johnson, Johnson, and Stanne, 2000).

Many primary studies were excluded from our meta-analysis because they were missing important summary statistics (e.g., means, standard deviations, sample sizes) needed to calculate the effect sizes. Consequently, the investigators and researchers of the effectiveness of various forms of small-group learning methods need to use better practices in reporting the results of the STEM primary studies. For example, reporting the basic descriptive statistics for both groups (mean, standard deviations, and sample sizes) and the pedagogical implementation practices in the STEM classrooms. Furthermore, our results show that a small number of primary studies in engineering and technology disciplines were implemented to assess and disseminate the results of the effectiveness of various forms of small-group learning methods compared to lecture-based instruction. Therefore, there is an urgent need to conduct more primary studies, especially in engineering and technology disciplines, to examine the effectiveness of small-group learning in STEM college classrooms.

In conclusion, the findings of the present meta-analytic study have shed some light on the accumulated literature of the effectiveness of various methods of small-group learning in STEM college classes. This study provided a positive answer to the effectiveness of various forms of small-group learning methods in comparison to the traditional lecture-based instruction in promoting higher STEM achievement, more positive attitude toward STEM subject matters, and increased persistence (retention) in STEM college classrooms. We learned that if students who are taking STEM classes in colleges are placed in an environment in which they can actively connect the STEM instruction to their previously learned materials and have an opportunity to experience collaborative and cooperative scientific inquiry, the academic achievement of these students in STEM courses will be accelerated. Based on the results of this study, it is important to note that STEM teachers and educators are recommended to use and implement any of the various small-group pedagogies and methods (e.g., cooperative, collaborative, team-based, problem-based, and inquiry-based) that have been shown to be effective in improving student achievement in STEM courses. Therefore, the current findings provide an evidence-based knowledge to future STEM researchers and educators, which will have significant educational policy implications in undergraduate STEM education.

Publications & Presentations: 
 
During the project period, which started on January 1, 2009 and ended on December 31, 2010 followed by a one year no-cost extension period (ended on December 31, 2011), the following dissemination activities have been taken place. The activities included (1) paper presentation at national and regional annual meeting and conferences, (2) Publications in peer-reviewed journals, and (3) web site development specially designed for this project.
 
Presentations at Professional Conferences
The following is a listing of the 15 papers and posters presented at the national and regional conferences during the grant period
 
1.    Kasim, R. M., & Kalaian, S. A. (2012, April). Small-Group Learning Versus Lecture-Based Instruction in Mathematics College Classrooms: A Meta-Analysis. A paper presented at the Annual Meeting of the American Educational Research Association (AERA) on April 2012. Vancouver, Canada.
 
2.     Kalaian, S. A., & Kasim, R. M. (2011, October). A Meta-Analysis of the Effectiveness of Small-Group Instruction Compared to Lecture-Based Instruction in Science, Technology, Engineering, and Mathematics (STEM) College Classes. Poster presented at the Principal   Investigator (PI) meeting of the National Science Foundation’s (NSF) REESE program. Washington, D.C.
 
3.    Kalaian, S. A., & Kasim, R. M. (2011, October). Effectiveness of Small-Group Learning in Statistics and Research Methods College Classes: A Multilevel Meta-Analysis Approach.   A paper presented at the Annual Meeting of the Mid-Western Educational Research Association in Saint Louis, Missouri.
 
4.    Kasim, R. M., & Kalaian, S. A. (2011, October). A Meta-Analytic Study to Examine the Effectiveness of Cooperative Learning in Science College Classes. A paper presented at the Annual Meeting of the Mid-Western Educational Research Association in Saint Louis, Missouri.
 
5.    Kalaian, S. A., & Kasim, R. M. (2011, April). The Effectiveness of Small Group Learning in Health Science College Classrooms.Paper presented at the Annual Meeting of the American Educational Research Association (AERA) in April 2011. New Orleans, Louisiana.
 
 
6.     Kalaian, S. A., & Kasim, R. M. (2010, October). A Multilevel Meta-Analysis of the Effectiveness of Collaborative Learning in Undergraduate STEM Labs. Paper presented at the Annual Meeting of the Mid-Western Educational Research Association. Columbus, Ohio.
 
 
7.     Kasim, R. M., & Kalaian, S. A. (2010, October). Multilevel Meta-Analysis Study of the Effectiveness of Active Small-Group Learning in College Science Classes. Paper presented at the Annual Meeting of the Mid-Western Educational Research Association. Columbus, Ohio.
 
 
8.    Kalaian, S. A., & Kasim, R. M. (2010, October). A Global Perspective on the Effectiveness of Small-Group Learning in STEM College Classrooms. Paper presented at the Midwest Regional Comparative and International Education Society 2010 Conference. Ypsilanti, Michigan.
 
 
9.    Kasim, R. M., Kalaian, S. A., & Kasim, N. R. (2010, October). A Comparative Study to Examine the Effectiveness of Cooperative Learning across Science Disciplines. Paper presented at the Midwest Regional Comparative and International Education Society 2010 Conference. Ypsilanti, Michigan.
 
10. Kalaian, S. A., & Kasim, R. M. (2010, May). Effectiveness of   Cooperative Learning Compared to Lecture-Based Learning in College STEM Classes.Paper presented at the Annual Meeting of the American Educational Research Association (AERA) in May 2010. Denver, Colorado.
 
11. Kasim, R. M., & Kalaian, S. A. (2010, March). Preliminary Results of the Effectiveness of small-group learning in the Mathematics and Statistics College Courses: A Meta-Analysis.   Poster presented at the Principal Investigator (PI) meeting of the REESE program of the National Science Foundation (NSF). Washington, D.C.  
 
12. Kasim, R. M., & Kalaian, S. A. (2009, October). Multilevel Modeling of the Effectiveness of small-group learning in the Mathematics and Statistics College Courses: A Meta-Analysis.Paper presented at the Annual Meeting of the Mid-Western Educational Research Association. St. Louis, Missouri.
 
13. Kalaian, S. A., & Kasim, R. M., &. (2009, October). Synthesizing the Effectiveness of small-group learning in STEM Classes Using Multilevel Meta-Analysis Methods.Paper presented at the Annual Meeting of the Mid-Western Educational Research Association. St. Louis, Missouri.
 
14. Kalaian, S. A., & Kasim, R. M. (2009, April). Should We Use the Mixed-or-Fixed-Effects Meta-Analysis Approaches for Analyzing STEM Effectiveness Data? Paper presented at the Annual Meeting of the American Educational Research Association (AERA). San Diego.
 
15. Kalaian, S. A., & Kasim, R. M. (2009, February).     Effectiveness of Active Small-Group Instruction Compared to Lecture-Based Instruction in Science, Technology, Engineering, and Mathematics (STEM) College Classes. Poster presented at the Principal Investigator (PI) meeting of the National Science Foundation’s (NSF) REESE program. Washington, D.C.
 
Publications
The following is a listing of the publications and proceedings that were prepared and/or submitted for publication in peer-reviewed journals during the grant funding period
1.    Kalaian, S. A., & Kasim, R. M. (under review).The Effectiveness of Small-Group Learning Methods in Statistics: A Multilevel Approach for Meta-Analysis. Manuscript submitted to the Journal of Statistics Education.
 
2.   Kalaian, S. A., & Kasim, R. M. (2010). A Global Perspective on the Effectiveness of Small-Group Learning in STEM College Classrooms. Proceeding of the Midwest Regional Comparative and International Education Society (MWCIES) Conference.
 
3.   Kasim, R. M., Kalaian, S. A., & Kasim, N. R. (2010). A Comparative Study to Examinethe Effectiveness of Cooperative Learning across Science Disciplines. Proceeding of the Midwest Regional Comparative and International Education Society (MWCIES) Conference.
 
4.   Kalaian, S., & Kasim, R. M. A Meta-Analysis of the Effects of Small-Group Learning on Achievement, Attitude, and Persistence in STEM College Classrooms. Manuscript in preparation to be submitted to Journal of STEM Education.
 
5.   Kalaian, S., & Kasim, R. M.Small-Group Learning Versus Lecture-Based Instruction in Mathematics College Classrooms: A Meta-Analysis. Manuscript in preparation to be submitted to Journal of Mathematics Education.
 
6.   Kalaian, S., & Kasim, R. M. The Effectiveness of Small Group Learning in Health Science College Classrooms. Manuscript in preparation to be submitted to Journal of Health Education.
 
7.   Kasim, R. M., Kalaian, S. Effectiveness of Small-Group Learning in Science College Classrooms: A Meta-Analytic Study. Manuscript in preparation to be submitted to Journal of Science Education.
 
8.   Kasim, R. M., Kalaian, S. Small-Group Learning Versus Lecture-Based Instruction in Technology and Computer Sciences College Classrooms. Manuscript in preparation to be submitted to Journal of Technology Education.
 
Web Site Development for the Project
A web site for this project is created to include a bibliography of the 203 (122 achievement, 24 attitude, and 79 retention) primary studies that were collected and included in the meta-analysis that compares small-group learning to lecture-based instruction and individualized instruction in STEM college classrooms, which is the focus of the present project. Also, a brief description of the project is posted on the main page of the web site. In addition, the Curriculum Vitas of the PIs are included. Further, web links to important web sites that contain significant information about small-group learning methods. It is important to note that this web site will be frequently updated as more manuscripts will be published in peer-reviewed journals The link to the website is