An official website of the United States government
A central goal of the Learning Assistant (LA) model is to improve students’ learning of science through the transformation of instructor practices. There is minimal existing research on the impact of college physics instructor experiences on their effectiveness. To investigate the association between college introductory physics instructors’ experiences with and without LAs and student learning, we drew on data from the Learning About STEM Student Outcomes (LASSO) database. The LASSO database provided us with student-level data (concept inventory scores and demographic data) for 4,365 students and course-level data (instructor experience and course features) for the students’ 93 mechanics courses. We performed Hierarchical Multiple Imputation to impute missing data and Hierarchical Linear Modeling to nest students within courses when modeling the associations be- tween instructor experience and student learning. Our models predict that instructors’ effectiveness decreases as they gain experience teaching without LAs. However, LA supported environments appear to remediate this decline in effectiveness as instructor effectiveness is maintained while they gain experience teaching with LAs.
more »
« less
This content will become publicly available on February 17, 2027
Developing Problem-Solving Competency in Data Science: Exploring A Case-Based Approach
Data Science Problem Solving (DSPS) competency refers to the ability to make key decisions when tackling real-world data challenges. As generative AI becomes increasingly capable of automating low-level routine tasks, it is critical to focus on developing students' higher-order reasoning and problem-solving skills. Despite the high demand for such competencies, training them effectively within typical data science courses remains a challenge. To help students develop robust problem solving competency, it is essential to expose them to a wide range of problem-solving scenarios. Meanwhile, it is desirable to let students receive timely feedback to prompt reflection and enhance learning. In this paper, we present our experience piloting Caselets (bite-sized case studies) designed to scaffold students in data science problem-solving—in graduate-level data science courses. We describe the rationale, design and implementation of the Caselets tool, analyze student performance and experience using the tool as part of their course, and reflect on the instructional design implications. Drawing from instructors' observations and reflections, we discuss lessons learned and offer recommendations for improving and scaling caselet-based practices to better support the needs of both students and instructors.
more »
« less
- Award ID(s):
- 2429590
- PAR ID:
- 10702831
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400722561
- Page Range / eLocation ID:
- 204 to 210
- Subject(s) / Keyword(s):
- data science education case-based learning problem-solving
- Format(s):
- Medium: X
- Location:
- St. Louis MO USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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
-
Scientific investigation is a core competency in undergraduate chemistry programs, yet instructors often do not explicitly assess students’ engagement with the full range of investigative practices in laboratory courses. Students are more likely to develop these skills when there are opportunities to engage with these practices and when assessments provide constructive feedback for improvement. Rubrics assessing investigative practices can help achieve this by creating a shared understanding of the practices between instructors and students. It also enables instructors to reflect on their teaching and make necessary changes to increase alignment between instruction and learning objectives. Here, we present rubrics designed to assess multiple dimensions of scientific investigation, such as generating investigable questions, planning and carrying out investigations, and applying mathematical and computational thinking, within undergraduate chemistry laboratories. Instructors and graduate teaching assistants who used these rubrics in their laboratory courses reported that they were useful for assessing student performance and generally viewed the resulting scores as reflective of students’ proficiency with the targeted science practices. These rubrics offer a resource for instructors to better align assessment with learning objectives and to intentionally design laboratory experiences that foster comprehensive engagement with scientific investigation.more » « less
-
This study examines the critique resolution strategies employed by students in introductory computer science and engineering courses when responding to automated critiques of their code. Drawing on data from both Computer Science (CS) and Engineering Fundamentals (EF) students using an automated feedback system, we investigate how students adapt their coding behavior in response to critiques and how these adaptations evolve across multiple submissions. The tool we have developed is a code critiquing tool that provides real-time feedback on syntax, logic, and stylistic errors in student code, designed specifically for educational environments. Through a multi-semester analysis of student behavior data, we evaluate the comparative performance of students enrolled in CS and EF courses and their approaches to resolving critiques. This cross-disciplinary comparison illuminates distinct patterns of problem-solving, engagement, and learning between students of varying technical backgrounds. The data for this study is drawn from three semesters of course assignments where the critiquer system was deployed in both CS and EF courses. Each submission was automatically logged by the critiquer system, capturing data such as time between submissions, antipattern identification, critique counts, and time to critique resolution. The behavioral patterns are analyzed by assignment, allowing us to assess trends over time and across multiple academic disciplines. By understanding how students from different disciplines engage with automated feedback, we can tailor these tools more effectively to meet the diverse needs of learners. These insights have implications for both the design of automated feedback systems and the pedagogical strategies employed in introductory programming courses. The study further proposes enhancements to the critiquer system that include customizable feedback options for different educational contexts and improved usability for both students and instructors.more » « less
-
In engineering education, laboratory learning that is well aligned with core content knowledge is instrumental as it plays a significant role in students’ knowledge construction, application, and distribution. Learning in laboratories is interactive in nature, and therefore students who learn engineering through online platforms can face many challenges with labs, which were frequently documented during the recent pandemic. To address those reported challenges, innovative online lab learning modules were developed and learning strategies were implemented in five courses in electrical engineering, Circuits I, Electronics I, Electronics II, Signals and Systems, and Microcomputers I, through which students gain solid foundation before students take on senior design projects. Lab modules with open-ended design learning experience through using a lab-in-a-box approach were developed to allow students to solve lab problems with multiple approaches that allow problem solving independently and collaboratively. Because this innovative lab design allows problem solving at various cognitive levels, it is better suited for concept exploration and collaborative lab learning environments as opposed to the traditional lab works with a “cookbook” approach that tend to lead students to follow certain procedures for expected solutions with the absence of problem exploration stage. In addition to the open-ended lab modules, course instructors formed online lab groups through which students shared the entire problem-solving process from ideas formation to solutions through trial and error. To investigate the effectiveness of the open-ended online lab learning experiences, students in all courses were randomly divided into experimental and control groups. Students in the control group learned in labs through learning materials that are aligned with core concepts by following a completed given procedures students in the experimental group learned through inquiry-based labs learning materials that required them to work in teams by integrating core concepts together to find solutions with multiple approaches. To maximize the online lab learning effect and to replicate the way industry, commerce and research practice, instructor structured cooperative learning strategies were applied along with pre-lab simulations and videos. The research results showed that generally students in the experimental group outperformed their counterparts in labs especially with more advanced concept understanding and applications, but showed mixed results for the overall class performance based on their course learning outcomes such as quizzes, lab reports, and tests. Further, survey results showed that 72% of students reported open-ended lab learning helped them learn better. According to interviews, the initial stage of working with team members was somewhat challenging from difficulties in finding time to work together for discussion and problem solving. Yet, through many communication tools, such as course LMS and mobile apps they were able to collaborate in lab problems, which also led them to build learning communities that went beyond the courses.more » « less
