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This study designed a decision-making learning module to engage students in collective decision-making in a collaborative learning environment. The purpose of this study was to explore the interplay of students' regulation learning in groups and their collective decision-making about a socioscientific issue. Thirty-eight 10th-grade students participated in the study. A variety of data were collected to examine the students' collective decision-making and regulation learning while they collaboratively conducted a socioscientific decision-making activity, including worksheets, computer activity recordings, and students' verbal interactions. The results indicated strong associations which showed that students who had sophisticated decision-making in a group tended to have more social shared regulation, and performed more regulation behaviours to refine their collective decision-making. The sequential analysis also indicated that students who manifested the serial nature of regulation processes and goal-driven learning might have exhibited a sophisticated decision-making performance when engaged in collective decision-making. The deficiencies in group interaction identified in this study underscore the need to provide scaffolds for regulation learning in order to refine students' collective decision-making in a SSI context.

The two-year study aimed to explore how students' development of different inquiry abilities actually benefited from the design of technology-infused learning modules. Three learning modules on the topics of seasons, environmental issues and air pollution were developed to facilitate students' inquiry abilities: questioning, planning, analyzing, and modeling. Two classes of seventh graders (13-14 years old) were randomly assigned to an inquiry group (n = 24) and a baseline group (n = 27). The two groups engaged in three technology-infused learning modules and conventional instruction respectively, and took two inquiry ability tests three times throughout two years. The test results showed that the inquiry group performed significantly better than the baseline group with respect to several inquiry sub-abilities. The instructional components of the tasks (i.e., design features) for which students exhibited significant improvements were further analyzed with respect to the task complexity, the function of the prompts, and the types of representations presented in the tasks. These design features identified provided useful insights into future design of technology-infused learning tasks for different inquiry abilities.

This study explores the effects of metacognitive and cognitive prompting on the scientific inquiry practices of students with various levels of initial metacognition. Two junior high school classes participated in this study. One class, the experimental group (n?=?26), which received an inquiry-based curriculum with a combination of cognitive and metacognitive prompts, was compared to the other class, the comparison group (n?=?25), which received only cognitive prompts in the same curriculum. Data sources included a test of inquiry practices, a questionnaire of metacognition, and worksheets. The results showed that the mixed cognitive and metacognitive prompts had significant impacts on the students' inquiry practices, especially their planning and analyzing abilities. Furthermore, the mixed prompts appeared to have a differential effect on those students with lower level metacognition, who showed significant improvement in their inquiry abilities. A combination of cognitive and metacognitive prompts during an inquiry cycle was found to promote students' inquiry practices.

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four master's students, and four PhD students in atmospheric sciences as participants. Participants' reasoning practices and understanding of multivariable causality were examined using semistructured interviews and recordings of their computer activities. Analyses show that participants with high expertise tended to take a mechanism approach to predict and identify relationships, focused more on multivariable relationships, and purposefully selected and tested variables. The findings also indicate that understanding about multiple causality involved recognition and identification of the integration rules of multiple effects and the attributes of variables (e.g., interactive and reciprocal) and relationships (e.g., direction and feedback loop). Additionally, this study suggests an interaction between participants' reasoning practices and their understanding of multivariable causality; participants' understanding about the integration rules and the attributes could initiate reasoning practices, and by the enactment of practices, the rules and attributes were confirmed and examined. This study provides insight into the nature of multivariable reasoning and the design of computer-based modeling tools. (Contains 5 tables and 6 figures.)