Team Cognition in Complex Engineering Tasks: Study

Article critique on Lee, M., & Johnson, T. E. (2008). Understanding the effects of team cognition associated with complex engineering tasks: Dynamics of shared mental models, Task‐SMM, and Team‐SMM. Performance Improvement Quarterly, 21(3), 73-95.

Research questions

The key research question used in the study was:

  1. How do Team-SMM and Task-SMM vary?

This research question was aimed at understanding the types of changes in Team-SMM and Task-SMM. This question could be answered by answering two other questions.

  1. Is it true that Team-SMM would vary with time? When changes occur, how would they impact a team solving complex tasks?
  2. Is it true that Task-SMM varies with time? When changes occur, how would they impact a team solving complex tasks?

The study participants were students studying an engineering course in a university in the United States. The study aimed at understanding the dynamics encountered in Team-SMM and Task-SMM by students who were assigned complex engineering tasks (Lee & Johnson, 2008). The engineering students were expected to have high Team-SMM and Task-SMM so that they could solve complex engineering problems. The researchers understood the role played by team-based learning, which is essential in solving complex problems. The researchers aimed at examining how shared mental models (SMM) enabled students to solve complex engineering tasks. The students could solve the problems easily when there was co-ordination from many perspectives. The researchers were confident that they would answer the research questions by testing the null and alternative hypotheses formulated in the study.

Hypotheses

The researchers tested the null and alternative hypotheses to make conclusions on their findings. The study encompassed one null hypothesis and one alternative hypothesis on Team-SMM similarity scores, and one null hypothesis and one alternative hypothesis on Task-SMM similarity scores. The null hypothesis for the Team-SMM scores was annotated as follows:

H0: µtime1 = µtime2

Where:

H0 = the null hypothesis

µtime1teamscores = Team-SMM similarity scores at time 1, and

µtime2teamscores = Team-SMM similarity scores at time 2.

The null hypothesis implied that there would be no change in Team-SMM similarity scores between time 1 (when the experiment was being started) and time 2 (at the end of the experiment).

The alternative hypothesis for Team-SMM similarity scores was annotated as follows:

H1: µtime1 < µtime2

Where:

H1 = the alternative hypothesis

µtime1teamscores = Team-SMM similarity scores at time 1, and

µtime2teamscores = Team-SMM similarity score at time 2.

The alternative hypothesis implied that the Team-SMM similarity scores at time 2 (at the end of the experiment) would be greater than Team-SMM similarity scores at time 1 (at the start of the experiment). If the alternative hypothesis is true, then it implies that there is a positive change in the variables being tested.

The null hypothesis for Task-SMM similarity scores was annotated as follows:

H0: µtime1 = µtime2

Where:

H0 = the null hypothesis

µtime1taskscores = Task-SMM similarity scores at time 1, and

µtime2taskscores = Task-SMM similarity scores at time 2.

The null hypothesis implied that there would be no change in Task-SMM similarity scores between time 1 (when the experiment was being started) and time 2 (at the end of the experiment).

The alternative hypothesis for Task-SMM similarity scores was annotated as follows:

H1: µtime1 < µtime2

Where:

H1 = the alternative hypothesis

µtime1taskscores = Task-SMM similarity scores at time 1, and

µtime2taskscores = Task-SMM similarity score at time 2.

The alternative hypothesis implied that the Task-SMM similarity scores at time 2 (at the end of the experiment) would be greater than Task-SMM similarity scores at time 1 (at the start of the experiment).

Methods and study design

The study used a sample size of 33 teams of participating students. The number was chosen because it would give a statistical power during statistical analysis. The participants were assigned their teams randomly. Different teams were given different manufactured products to initiate members’ problem-solving processes. The study participants worked on the tasks the way engineers work on product manufacturing in industries. The revised elaborated and interpreted the manufactured products to have their SMMs triggered.

The demographic survey was used to collect data in the study. The participants’ demographic information recorded was age, gender, and race. Apart from the survey, instruments were also used to collect data in the study. Two instruments measured Team-SMM and the other two measured Task-SMM. In each of the two categories, one instrument was used to measure the structure of the changes of the variables while the other was used to measure the degree of the changes of the variables. The Task-SMM and Team-SMM were assessed by administering questionnaires to the participating students. On the other hand, changes in the Team-SMM and Task-SMM structures were assessed by testing how well the pairs of the participants in the study groups related and co-operated in performing tasks together. The dependent variables used in the study were the Task-SMM similarity scores and the Team-SMM similarity scores. The independent variable used in the study was time.

Data analysis

Data were analyzed in two steps. In the first step, the original data were changed into similarity scores while in the second step, the similarity scores were analyzed. If a similarity score was zero, then it implied that there was no similarity. On the other hand, if a similarity score was one, then it implied that there was complete similarity. One of the statistical tests used in the study was one-way repeated measures ANOVA. The statistical test gave significant changes in the Team SMM scores. A paired sample t-test was used to assess the specificity in time during which changes in Team-SMM occurred. The scores increased significantly from time 1 to time 2. Therefore, the null hypothesis was rejected, and the alternative hypothesis was accepted. Results on the Team-SMM degree demonstrated significant increases in the Team-SMM degree scores from time 1 to time 2, thus the null hypothesis was rejected in this case.

Critique

The one-way repeated measures ANOVA statistical test that was used in the data analysis assumed that the differences among the different teams had no significant outliers (Jackson, 2012). The t-test used in the analysis also assumed that the data were normally distributed (Jackson, 2012). The study findings are quite relevant because they increase the academic knowledge on Team-SMM and Task-SMM. Moreover, the findings showed that the two dependent variables tested increased over time, hence supporting other previous studies. Improvements upon the study could be made by using statistical methods that do not assume that data were normally distributed. It could also be improved by using more post-hoc tests than were used in the study.

Summary

The study findings showed that Team-SMM and Task-SMM changed over time. However, the components of Team-SMM and task-SMM varied differently at the start of the study. Also, Team-SMM varied significantly from time 1 to time 2.

References

Jackson, S. L. (2012). Research methods and statistics: A critical thinking approach (4th ed.). Belmont, CA: Wadsworth.

Lee, M., & Johnson, T. E. (2008). Understanding the effects of team cognition associated with complex engineering tasks: Dynamics of shared mental models,

Task‐SMM, and Team‐SMM. Performance Improvement Quarterly, 21(3), 73-95.