Managing Small Groups to Meet the Social and Psychological Demands of Scientific and Engineering Practices in High School Science

Principal Investigator: 
Project Overview
Background & Purpose: 

How does students’ small group work differ when students are engaged in engineering design tasks versus scientific inquiry tasks? Do students need a different mix of individual resources (cognitive, social and affective resources) in order to engage successfully in an engineering design task vs. a scientific inquiry task? How do small groups collectively cope with the various cognitive, social and affective demands posed by inquiry and engineering design tasks? How does the quality of a group’s joint management of this complex set of demands impact student learning in each type of task?

Setting: 

The proposed study will recruit high school science students in Western Massachusetts representing diverse school contexts in terms of race/ethnicity, socioeconomic status and urbanicity.

Research Design: 

The project uses a longitudinal research design and will generate evidence that is descriptive [observational], associative/correlational [quasi-experimental], and causal [statistical modeling]. Original data will be collected on high school students using assessments of learning, observation [personal observation, videography] and survey research [self-completion questionnaire, semi-structured or informal interviews, focus groups].

In order to answer RQ1 (effect of student affective resources on group construction of the problem solving space), we will use quantitative and qualitative methods. Students’ individual interest will be assessed with the Class-Specific Interest and Domain-Specific Interest scales (Marsh, Köller, Trautwein, Lüdtke, & Baumert, 2005). Items will be modified to reflect the specific science content in the participating classes. Because no measure exists for students’ situational interest of specific tasks, we will develop a measure following Renninger’s and Su’s (2012) summary of learner characteristics and task environment during development of situational interest. Students’ motivation is assessed with three subscales (perceived competence, perceived task choice and relatedness to group) of the Intrinsic Motivation Inventory (IMI; McAuley, Duncan, & Tammen, 1987), which assesses participants’ subjective experiences in various contexts. The three dimensions of the Group Problem-Solving Space will be assessed separately. The level of co-construction of the cognitive space will be measured with the Group Interaction Questionnaire (Visschers-Pleijers, et al., 2005). Co-construction of the relational space will be assessed using Sargent & Sue-Chang’s (2001) Social Cohesion scale and the Social Loafing and Positive Group Interaction scales (Linnenbrink-Garcia, et al. 2011). The affective component of the problem-solving space will be measured using Edmondson’s (1999) Psychological Safety scale and the Group Feeling Thermometer (Wilcox, Sigelman & Cook, 1987; Alwin 1997). We will address RQ2 (effect of student social resources on group construction of the problem solving space) with two scales measuring the individual student’s interpersonal social competence. The first scale, the Objectives of Social Competence Scale (OSCS; ten Dam & Volman, 2007) addresses interpersonal skills necessary for effective interactions and will be modified based on the original Dutch version. Our second scale, the Social Skills Improvement System Rating Scale (SSISRS; Gresham & Elliott, 1990) focuses on interactions skills such as self-control, co-operation, and assertion.

Our third research question (RQ3) refers to the effect of student cognitive resources on group construction of the problem solving space. We will measure student task-specific prior knowledge and skills for developing arguments. In order to assess knowledge and skills, in collaboration with the teachers we will develop four content related questions that also demand students to engage in arguments. These questions will use a format familiar to students in each class. RQ4 focuses descriptively on the dynamics within the group (which cognitive, social and affective demands are present during inquiry vs. engineering design group tasks, and how do students collectively cope with them). Data collection will include videotaping of groups during tasks and student focus groups after the last task as well as a teacher focus group. RQ5 (How does the quality of group learning behavior affect student achievement?) is addressed with a final Science Content and Skills Test four teacher-developed science content-related items and five items reflecting inquiry and engineering design principles, particularly engaging in arguments, and drawn from MOSART tests (http://www.cfa.harvard.edu/smgphp/ mosart/ aboutmosart_2.html), which are comprised of multiple-choice items linked to the K–12 physical science and earth science content, and K–8 life science content in the NRC National Science Education Standards; or from tests such as PISA (http://www.oecd.org/statisticsdata/0,3381,en_2649_35845621_1_119656_1_1_...) and TIMSS (http://timss.bc.edu/TIMSS2007/items.html). This test will be administered to all participating students after the last group observation as a paper-and-pencil test.

Quantitative analyses (descriptive statistics; hypotheses testing using HLM, since measures are collected at two levels of analysis; and final estimation using more robust two-level structural equation models in AMOS) will be conducted on data gathered from the Individual Resources Questionnaire, Group Learning Behavior Questionnaires, and final Science Content/Skills Test, answering the above research questions by evaluating how student affective, social and cognitive resources affect the quality of student group work (RQ1, 2 and 3) and in turn, how quality of group work impacts student learning (RQ5). Qualitative data collected during student group work observations and focus group interviews addressing RQ4 will be analyzed using Chi’s (1997) eight-step protocol. Using a mixed methods approach, the analysis of these sources of data will follow themes isolated through the analysis of quantitative measures (Tashakkori & Teddlie, 1998), as well as themes highlighted in prior qualitative research on group work. Using NVivo software, themes generated through this procedure will be quantified. Thus, qualitative and quantitative data collected through the interviews and videotaped group work observations will be triangulated with quantitative data from the surveys in order to seek convergence of results across both methods, with the multiple data sources complementing each other in order for one method to explicate the results of the other.

Findings: 

Findings will be posted as they become available.