Comparing Student and Teacher Formed Teams on Group Dynamics, Satisfaction and Performance
Shayna A. Rusticus and Brandon J. Justus
We compared student and teacher-formed teams on aspects of group dynamics, satisfaction, and performance. Two sections of an introductory psychology research methods course were randomly assigned to either student-formed (n = 28) or teacher-formed (n = 33) teams. We conducted t-tests on 10 measures related to group dynamics, satisfaction, and success. Academic performance and group work contribution were the only measures found to be statistically different, with the student-formed teams scoring higher than the teacher-formed teams. Follow up individual interviews or focus groups conducted with 13 of these students suggested a slight preference for the teacher-formed method because it was transparent and eliminated the stress of having to choose one’s, team members. We further recommend this method because of its simplicity and closer approximation to real-world scenarios. Several factors identified as being important for effective team functioning, regardless of group formation methods are also discussed.
Rusticus, S. A. & Justus, B. (2019). Comparing student- and teacher-formed teams on group dynamics, satisfaction, and performance. Small-Group Research, 50(4), 443–457. https://doi.org/10.1177/1046496419854520
Establishing Equivalence Thresholds Using a Distribution-Based Approach
Shayna A. Rusticus, Kyla Javier, and Kevin W. Eva
Establishing group equivalence, as opposed to group differences, is a common goal in many educational/research contexts. Tests of equivalence are used to address such goals; however, a key methodological consideration is how to operationalize equivalence. This study sought to verify if a distribution-based approach, based on effect size, can establish a generalizable criterion for identifying equivalence. A sample of 331 students was presented with a series of numerical statements or bar graphs representing three measures: (1) overall academic achievement, (2) an individual exam score, and (3) a course evaluation survey. Descriptive statistics and a mixed ANOVA examined the effects on equivalence ratings of (a) the difference between means, (b) spacing of the differences (narrow/wide), and (c) presentation format (bar graph/numerical). Across the measures and conditions, the equivalence threshold (i.e., the point at which 50% of participants rated the mean difference as non-equivalent) ranged from an effect size of d = 0.37 to d = 1.15, suggesting that a single effect size criterion for establishing the equivalence threshold may not be achievable. Guidelines are provided for setting an appropriate equivalence threshold.
What are the Key Elements of a Positive Learning Environment?
Perspectives from Students and Faculty
Shayna A. Rusticus, Tina Charmchi, and Andrea Mah
The learning environment comprises the psychological, social, cultural, and physical setting in which learning occurs and has an influence on student motivation and success. The purpose of the present study was to qualitatively explore, from the perspectives of both students and faculty, the key elements of the learning environment that supported and hindered student learning. We recruited a total of 22 students and 9 faculty to participate in either a focus group or individual interview session on their perceptions of the learning environment at their university. We analyzed the data using directed content analysis and organized the themes around the three key dimensions of personal development, relationships, and institutional culture. Within each of these dimensions, we identified subthemes that facilitated or hindered student learning, and faculty work, experiences. We also identified and discussed similarities in subthemes identified by students and faculty.
This study has been submitted to Learning Environments Research.
A Guide to the Selection of Self-Report Measures for Research Studies
Shayna A. Rusticus and Brandon J. Justus
Introduction: Students engaging in research often need to select measures for their research or to critically evaluate the quality of published measures. The selection of high-quality measures is important to ensure that they provide accurate information for their intended purpose.
Statement of the Problem: Students may not know what information to look for, or what levels of quality are needed, in evaluating such measures.
Literature Review: We provide guidelines on how to assess the psychometric quality of self-report rating scale measures based on four key indicators: validity, reliability, relevance, and utility. First, we discuss validity in regards to the five sources of validity evidence endorsed by the Standards for Educational and Psychological Testing. Second, we present an overview of different types of reliability evidence. Third, we discuss the importance of using a measure as intended. Fourth, we highlight the importance of considering the practical use of a measure. We also provide a discussion of factors to consider in the scoring and interpretation of measures.
Conclusion: By carefully appraising the quality of existing measures prior to their use, and ensuring that they are administered and scored as intended, accurate and meaningful interpretations can be made from the data.
This paper has been submitted to Scholarship of Teaching and Learning in Psychology
Does self-directed learning readiness predict undergraduate students’ instructional preferences?
Brandon J. Justus, Shayna A. Rusticus, and Brittney Stobbe
Self-directed learning is a process by which students take the lead, with or without the help of others, in determining their learning needs and managing their learning strategies and outcomes. Relatedly, self-directed learning readiness (SDLR) looks at the attitudes, abilities, and personality characteristics necessary for self-directed learning. In study one, we shortened, and slightly modified, the SDLR scale (Fisher et al., 2001) for use among undergraduate university students and examined its factor structure and reliability. In a sample of 194 students, the three-factor structure of this scale (self-management, desire to learn, and self-control) was confirmed with acceptable reliability. In study two, we examined whether SDLR subscales predicted a preference for a teacher-directed or student-directed class format in a sample of 256 undergraduate students. We conducted a series of four multiple linear regressions to examine whether the three dimensions of SDLR were predictive of four classroom preference styles (knowledge construction, teacher direction, cooperative learning, and passive learning). Three of these analyses were statistically significant with small to medium effect sizes. These findings have the potential to identify factors that may be linked to greater student engagement, more positive learning environments, and greater success in the learning process.
Justus, B. J., Rusticus, S. A., & Stobbe, B. (2020, July). Does self-directed learning readiness predict undergraduate students’ instructional preferences? [Poster]. 81st Canadian Psychological Association Annual National Convention, Montréal, Quebec, Canada. https://eventmobi.com/cpa2020/.
This paper has been submitted to The Canadian Journal for the Scholarship of Teaching and Learning