An official website of the United States government
This work-in-progress paper shares findings of the early stage of a 3-year research funded by the National Science Foundation. The major aim of the project is to advance engineering and mathematics (EM) education theory and practice related to students’ self-regulation of cognition and motivation skills during problem-solving activities. The self-regulation includes students’ metacognitive knowledge about task (MKT) and self-regulation of cognition (SRC). The motivational component of self-regulation (SRM) includes self-control of the motivation needed to maintain the level of engagement and deliberate practice necessary for scientific thinking and reasoning. To be effective problem-solvers, students must understand the relationship between the MKT, SRC and SRM throughout the problem-solving activities. Four research questions will guide the research: (1) How do students perceive their self-regulation of cognition (SRC) and motivation (SRM) skills for generic problem-solving activities in EM courses; (2) How does students’ metacognitive knowledge about problem-solving tasks (MKT) inform their Task interpretation?; (3) How do students’ SRC and SRM dynamically evolve?; and (4) How do students’ SRC and SRM reflect their perceptions of self-regulation of cognition and motivation for generic EM problemsolving activities? A sequential mixed-methods research design involving quantitative and qualitative methods are used to develop complementary coarse- and fine-grained understandings of undergraduate students’ SRC and SRM during academic problem-solving activities. Two 2nd year EM courses: Engineering Statics, and Ordinary Differential Equations were purposefully selected for the contexts of the study. One hundred forty two students from both courses were invited and participated in quantitative data collection using two validated surveys during spring 2022 semester. Later in the semester, qualitative data will be generated with twenty students in both courses through one-on-one interviews with students and course instructors, think-aloud protocols with students, and classroom observations. Coarse-grained understandings of students’ SRC and SRM are currently developed through analysis of quantitative data collected using self-report surveys (i.e., BRoMS and PMI). Fine-grained understandings of students’ SRC and SRM will be developed through analysis of qualitative data gathered via one-on-one interviews, think-aloud protocols, classroom observations, and course artifacts gathered as students engage in EM problem-solving activities.
more »
« less
Quantitative Analysis of Self-Regulation in Engineering and Mathematics Education
This paper shares the initial findings of a three-year research project. Quantitative methods were used to develop coarse-grained understandings of undergraduate students’ self-regulation of cognition (SRC) and self-regulation of motivation (SRM) during academic problem-solving activities in two undergraduate engineering and mathematics (EM) courses. Two research questions were constructed to guide this study: (1) How are SRC and SRM strategies related to each other while solving EM problems?; and (2) How do students perceive their SRC and SRM strategies for problem-solving activities in EM courses? Two 2nd year EM courses, Engineering Statics and Ordinary Differential Equations, were purposefully selected as the contexts of the study. There were a combined total of 142 students (120 male and 20 female), across both courses, that participated in quantitative data collection using two validated surveys during spring 2022. Quantitative data were collected using two selfreport surveys: Brief Regulation of Motivation Scale (BRoMS), and the Physics Metacognitive Inventory (PMI). Although PMI was initially designed for Physics, it can be used to assess students’ metacognition for problem solving in other knowledge domains by simply revising the word “physics” to another domain knowledge. Both descriptive and inferential statistics were conducted to analyze the collected quantitative data. During data analysis we found: (1) a significant relationship between students’ strategies to selfregulate their cognition and motivation during EM problem-solving activities; (2) no significant difference between male and female’s self-regulation of cognition (SRC) and self-regulation of motivation (SRM); (3) no significant difference of SRM between students who engaged in Engineering Statics and Ordinary Differential Equation problem-solving activities; and (4) a significant difference of reported strategies in interpreting problem and evaluating strategies between those who engaged in Engineering Statics and Ordinary Differential Equation problemsolving activities. Participants reported using certain SRM strategies, such as “If I need to, I have ways of convincing myself to keep working on a tough assignment” more frequently than other strategies during problem solving.
more »
« less
- Award ID(s):
- 2110769
- PAR ID:
- 10509369
- Publisher / Repository:
- American Society for Engineering Education
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This complete evidence-based practice paper discusses the strategies and results of an introduction to mechanics course, designed to prepare students for introductory-level physics and other fundamental courses in engineering, such as statics, strength of materials, and dynamics. The course was developed to address historically high failure (DFW) rates in the physics courses and is part of a set of interventions implemented to support student success in a college of engineering and computer science. The course focuses on providing in-depth understanding of Newton’s Laws of motion, free-body diagrams, and linear and projectile motion. Because it focuses on a limited number of competencies, it is possible to spend more time on inquiry-based activities and in-class discussions. The course framework was designed considering the Ebbinghaus’ Forgetting Curve, to provide students with learning opportunities in 6-day cycles: (i) day 1: a pre-class learning activity (reading or video) and a quiz; (ii) day 2: in-class Kahoot low-stakes quiz with discussion, a short lecture with embedded time for problem-solving and discussion, and in-class activities (labs, group projects); (iii) day 4: homework due two days after the class; (iv) day 6: homework self-reflection (autopsy based on provided solutions) two days after homework is due. The assessment of course performance is based on the well-characterized force concept inventory (FCI) exam that is administered before the intro to mechanics course and both before and after the Physics I course; and on student performance (grades) in Physics and Statics courses. Results from the FCI pre-test show that students who took the introduction to mechanics course (treatment group) started the physics course with a much better understanding of force concepts than other students in the course. The FCI post-test shows better normalized gain for the treatment group, compared to other students, which is also aligned with student performance in the course. Additionally, student performance is significantly better in statics, with 25% DWF rate compared to 50% for the other students. In summary, the framework of the course, which focuses on providing students with in-depth understanding of force concepts, has led to better learning and performance in Physics I, but importantly it has also helped students achieve better performance in the Statics course, the first fundamental course in civil and mechanical engineering programs.more » « less
-
This work in progress research paper considers the question, what kind of problems do engineering students commonly solve during their education? Engineering problems have been generally classified as ill-structured/open-ended or well-structured/closed-ended. Various authors have identified the characteristics of ill-structured problems or presented typologies of problems. Simple definitions state that well-structured problems are simple, concrete, and have a single solution, while ill-structured problems are complex, abstract, and have multiple possible solutions (Jonassen, 1997, 2000). More detailed classifications have been provided by Shin, Jonassen, and McGee (2003), Voss (2006), and Johnstone (2001). It is commonly understood that classroom problems are well-structured while workplace problems are ill-structured, but we cannot find any empirical data to confirm or deny this proposition. Engineers commonly encounter ill-structured problems such as design problems in the field therefore problem-solving skills are invaluable and should be taught in engineering courses. This research specifically looks at the types of problems present in the two most commonly used statics textbooks (Hibbeler, 2016; Beer, et al., 2019). All end-of-chapter problems in these textbooks were classified using Jonassen’s (2000) well-known typology of problem types. Out of 3,387 problems between both books, 99% fell into the algorithmic category and the remaining fell into the logic category. These preliminary results provide an understanding of the types of problems engineering students most commonly encounter in their classes. Prior research has documented that textbook example problems exert a strong influence on students' problem-solving strategies (Lee et al., 2013). If instructors only assign textbook problems, students in statics courses do not see any ill-structured problems at that stage in their education. We argue that even in foundational courses such as statics, students should be exposed to ill-structured problems. By providing opportunities for students to solve more ill-structured problems, students can become more familiar with them and become better prepared for the workforce. Moving forward, textbooks from several other courses will be analyzed to determine the difference between a fundamental engineering course such as statics and upper-level courses. This research will allow us to determine how the problem types differ between entry level and advanced classes and reveal if engineering textbooks primarily contain well-structured problems. Keywords: problem solving, textbooks, ill-structured problemsmore » « less
-
Learning is usually conceptualized as a process during which new information is processed in working memory to form knowledge structures called schemas, which are stored in long-term memory. Practice plays a critical role in developing schemas through learning-by-doing. Contemporary expertise development theories have highlighted the influence of deliberate practice (DP) on achieving exceptional performance in sports, music, and different professional fields. Concurrently, there is an emerging method for improving learning efficiency by combining deliberate practice with cognitive load theory (CLT), a cognition-architecture-based theory for instructional design. Mechanics is a foundation for most branches of engineering. It serves to develop problem-solving skills and consolidate understanding of other subjects, such as applied mathematics and physics. Mechanics has been a challenging subject. Students need to understand governing principles to gain conceptual knowledge and acquire procedural knowledge to apply these principles to solve problems. Due to the difficulty in developing conceptual and procedural knowledge, mechanics courses are among those which receive high DFW rates (percentage of students receiving a grade of D or F or Withdrawing from a course) and students are more likely to leave engineering after taking mechanics courses. Since deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance, this study is to evaluate how deliberate practice helps students learn mechanics during the process of schema acquisition and consolidation. Considering cognitive capacity limitations, we will apply cognitive load theory to develop deliberate practice to help students build declarative and procedural knowledge without exceeding their working memory limitations. We will evaluate the effects of three practice strategies based on CLT on schema acquisition and consolidation in two mechanics courses (i.e., Statics and Dynamics). Examples and assessment results will be provided to evaluate the effectiveness of the practice strategies as well as the challenges.more » « less
-
null (Ed.)Learning is usually conceptualized as a process during which new information is processed in working memory to form knowledge structures called schemas, which are stored in long-term memory. Practice plays a critical role in developing schemas through learning-by-doing. Contemporary expertise development theories have highlighted the influence of deliberate practice (DP) on achieving exceptional performance in sports, music, and different professional fields. Concurrently, there is an emerging method for improving learning efficiency by combining deliberate practice with cognitive load theory (CLT), a cognition-architecture-based theory for instructional design. Mechanics is a foundation for most branches of engineering. It serves to develop problem-solving skills and consolidate understanding of other subjects, such as applied mathematics and physics. Mechanics has been a challenging subject. Students need to understand governing principles to gain conceptual knowledge and acquire procedural knowledge to apply these principles to solve problems. Due to the difficulty in developing conceptual and procedural knowledge, mechanics courses are among those which receive high DFW rates (percentage of students receiving a grade of D or F or Withdrawing from a course) and students are more likely to leave engineering after taking mechanics courses. Since deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance, this study is to evaluate how deliberate practice helps students learn mechanics during the process of schema acquisition and consolidation. Considering cognitive capacity limitations, we will apply cognitive load theory to develop deliberate practice to help students build declarative and procedural knowledge without exceeding their working memory limitations. We will evaluate the effects of three practice strategies based on CLT on the schema acquisition and consolidation in two mechanics courses (i.e., Statics and Dynamics). Examples and assessment results will be provided to evaluate the effectiveness of the practice strategies as well as the challenges.more » « less
