Learning and Teaching about Food Webs in a Computationally Rich Environment: A Mixed-Methods Study

This dissertation conducted a science classroom intervention using two instructional approaches, computational modeling and paper-based pictorial modeling, in the context of food webs. A series of research papers were written on the impact of the intervention on students' attitudes and learning, and on teachers via professional development and teaching. The first study employed quasi-experimental design, examining the impacts of computational modeling and paper-based pictorial modeling on students' systems thinking and CT skills. A total of 365 seventh-grade students were involved in week-long online learning activities. The students were purposefully assigned to either synchronous computational modeling condition (n = 224) or asynchronous paper-based pictorial modeling condition (n = 141). Students modeled the interrelation between concepts and components of a food web in both conditions. The students took CT and systems thinking-embedded food web assessments before and after the activity and formative assessment after each activity to gather their perceptions of what they thought they learned from the activities. Multilevel modeling and Epistemic Network Analysis were used to analyze the data. The findings indicated a significant increase in students' systems thinking skills regardless of the conditions. Students in computational modeling condition had a substantial increase in their CT scores, while such a result was not detected in the paper-based pictorial modeling condition. ENA results supported this finding, showing that students in computational modeling condition expressed that they learned both science and CT concepts. The second study explored the predictors of students' interests in computationally intensive (or AI-informed) science careers and the impact of computational modeling activity on middle school students' interests in such careers. In parallel, an instrument to measure students' career interests called the Computationally Intensive Science Career Interest (CISCI), was developed and validated. A combination of classical test theory and item response theory was used to validate the CISCI instrument using a sample N = 934. Multiple linear regression and paired-sample "t"-tests were performed to analyze the data. The results showed that the CISCI is a valid and reliable instrument consisting of five constructs. Results from regression tests revealed that students' science attitudes, computer science attitudes, CT skills, and prior computer science-related activities significantly predicted students' career interests. Furthermore, a significant increase in students' perceptions of intensity of discussing computationally intensive careers with their parents was detected after participating in a computational modeling activity. The third study employed a mixed-methods design to investigate the changes in, and sources of, middle school teachers' self-efficacy for teaching science in a computationally rich environment. A total of eleven middle school teachers participated in this study. They took two questionnaires four times--before and after training and teaching--measuring their self-efficacy for teaching science and CT. The teachers were also either interviewed or asked to provide written reflections after taking the questionnaires. Non-parametric Skillings-Mack tests and thematic analysis were used to analyze the data. The results revealed a significant increase in teachers' self-efficacy for teaching science and CT after training and teaching. These changes in self-efficacy were tied to three sources, namely: (1) exposure to computer programming, (2) students' interests and responses to a computationally rich science environment, and (3) teaching repetition and field experience. Results from the three studies highlight the potential benefit of engaging students with learning science in a computationally rich environment to positively influence both their CT and systems thinking skills as well as content knowledge. The study also highlighted the challenges and possibilities for preparing science teachers to teach computational modeling with little or no background in programming or computer science. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]