Problem solving is one of the central activities performed by computer scientists as well as by computer science learners. Whereas the teaching of algorithms and programming languages is usually well structured within a curriculum, the development of learners’ problem-solving skills is largely implicit and less structured. Students at all levels often face difficulties in problem analysis and solution construction. The basic assumption of the workshop is that without some formal instruction on effective strategies, even the most inventive learner may resort to unproductive trial-and-error problemsolving processes. Hence, it is important to teach problem-solving strategies and to guide teachers on how to teach their pupils this cognitive tool. Computer science educators should be aware of the difficulties and acquire appropriate pedagogical tools to help their learners gain and experience problem-solving skills.

Thesis (Ph. D.)--University of Oregon, 2003. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 97-105). Also available for download via the World Wide Web; free to University of Oregon users.

<p>The need for STEM employees is on the rise in direct relation with the changing needs of our globe (Jang, 2015). There are gaps to be filled not only in the workforce and industry, but also by academia and government (Jang, 2015). K-12 STEM education has the ability to address 21^st century problems, in particular, the need for more highly skilled workers in STEM fields by focusing on developing students’ 21^st century skills. A critical skill for students to develop to be able to properly collaborate on teams and engage in the STEM workforce is problem solving. Problem solving is thought of as being the most important cognitive goal of education in every educational context: formal, informal, public schools, universities, and everything in between (Jonassen, 2010). In order to properly assess students, and know where improvements could be made, it is vital that we examine teachers first. By exploring how teachers approach problem-solving, and how self-efficacious they feel teaching problem-solving, then we can determine how to better assist both teachers and students. The current study sought to address this gap in the literature through surveying three states agriculture and science teachers using two established instruments. The survey was distributed online via Qualtrics and was available to participants for three weeks during the month of January 2020. The survey is divided into three major sections with the first two sections being the instruments used: (1) Problem Solving Inventory, (2) Teaching Science as Inquiry, and (3) Demographics. Demographics was placed at the end of the survey following recommendations from Dillman et al. (2014).</p><p> The final response rate for the survey was 9.04% for agriculture teachers and 13.4% for science teachers, a total of 22.44% (n = 504). After data cleaning there is a total of 4.3% of useable responses from agriculture and 5.58% for science resulting in a usable response rate of 9.88% (n = 205). A little more than half of the participants were female (59%) with the remainder being male (39.5%) and a small percentage (1.5%) elected not to respond or selected “prefer not to say”. The largest population that responded to the survey were between the ages of 44 and 54 (43.1%) and teaching for 21-25 years (19%). The survey found that teachers thought of themselves as being confident problem-solvers but used a more avoidance-style. Teachers also felt they had less control or had more negative feelings in regard to problem-solving. Overall, teachers from both agriculture and science viewed themselves as being moderate to high problem-solvers in general. The instrument was not intended to measure problem-solving relating to the classroom. The second instrument, the Teaching Science as Inquiry, measured how efficacious they felt teaching problem-solving. Teachers from both science and agriculture perceived themselves as being very self-efficacious and had high expectancy outcomes. ANOVA tests were conducted between the two groups to determine if there were differences in their responses and no statistically significant differences were found. A correlation was conducted in order to determine which variables from the two instruments held relationships. The correlation suggests that the two instruments have several strong relationships between the variables like personal self-efficacy and expectation outcomes. research should focus on refining the instruments to reduce the number of questions and survey more individuals to capture more generalizable results. </p>