Students taking introductory physics are rarely exposed to computational modeling. In a one-semester large lecture introductory calculus-based mechanics course at Georgia Tech, students learned to solve physics problems using the VPython programming environment. During the term, 1357 students in this course solved a suite of 14 computational modeling homework questions delivered using an online commercial course management system. Their proficiency with computational modeling was evaluated with a proctored assignment involving a novel central force problem. The majority of students (60.4%) successfully completed the evaluation. Analysis of erroneous student-submitted programs indicated that a small set of student errors explained why most programs failed. We discuss the design and implementation of the computational modeling homework and evaluation, the results from the evaluation, and the implications for computational instruction in introductory science, technology, engineering, and mathematics (STEM) courses.

The performance of over 2000 students in introductory calculus-based electromagnetism ͑E&M͒ courses at four large research universities was measured using the Brief Electricity and Magnetism Assessment ͑BEMA͒. Two different curricula were used at these universities: a traditional E&M curriculum and the Matter & Interactions ͑M&I͒ curriculum. At each university, postinstruction BEMA test averages were significantly higher for the M&I curriculum than for the traditional curriculum. The differences in post-test averages cannot be explained by differences in variables such as preinstruction BEMA scores, grade point average, or SAT Reasoning Test ͑SAT͒ scores. BEMA performance on categories of items organized by subtopic was also compared at one of the universities; M&I averages were significantly higher in each topic. The results suggest that the M&I curriculum is more effective than the traditional curriculum at teaching E&M concepts to students, possibly because the learning progression in M&I reorganizes and augments the traditional sequence of topics, for example, by increasing early emphasis on the vector field concept and by emphasizing the effects of fields on matter at the microscopic level.

The performance of over 5000 students in introductory calculus-based mechanics courses at the Georgia Institute of Technology was assessed using the Force Concept Inventory (FCI). Results from two different curricula were compared: a traditional mechanics curriculum and the Matter & Interactions (M&I) curriculum. Post-instruction FCI averages were significantly higher for the traditional curriculum than for the M&I curriculum; the differences between curricula persist after accounting for factors such as pre-instruction FCI scores, grade point averages, and SAT scores. FCI performance on categories of items organized by concepts was also compared; traditional averages were significantly higher in each concept. We examined differences in student preparation between the curricula and found that the relative fraction of homework and lecture topics devoted to FCI force and motion concepts correlated with the observed performance differences. Limitations of concept inventories as instruments for evaluating curricular reforms are discussed.Each year more than 35% of American college and university students enroll in a physics course. 1 Only a small fraction of these students ultimately complete a degree in physics; the vast majority pursue a degree in engineering or another science. 2 Many are students in an introductory physics course; approximately 175,000 students each year enroll in introductory calculus-based physics. 3 However, many of these students fail to acquire an effective understanding of concepts, principles, and methods from these introductory courses. Rates of failure and withdrawal from these courses are often high and substantial research into this subject has shown that students' misconceptions in physics persist after instruction. 4,5 This paper describes an attempt to evaluate, using a multiple-choice concept inventory, 6 a reformed introductory mechanics curriculum 7 which aims to mitigate these issues by altering the goals and content (i.e., the curriculum) of the typical mechanics course.To help improve student learning in physics, many new methods of content delivery (pedagogy) have been developed in recent years. Typically, these methods have been implemented with little change to course curricula. Well established pedagogical modifications now used widely include tutorials, 8 clicker questions, 9 peer instruction, 10 Socratic tutorial homework systems, 11 multiple representations of concepts and principles, 12 and reconfigurations of the instructional environment. 13 There is ample evidence that students who experience these pedagogical reforms perform better on end-of-course concept inventories than students in passive lecture courses. Concept inventories are useful tools to make such comparisons in these cases where all courses (with and without pedagogical reform) share, for the most part, the same core content and goals.By contrast, there is sparse research on how student learning is affected by substantial alterations to the goals and content (curriculum) of introductory physics cours...

A computing laboratory for introductory quantum mechanics Am.Abstract. Students taking introductory physics are rarely exposed to computational modeling. In a one-semester large lecture introductory calculus-based mechanics course at Georgia Tech, students learned to solve physics problems using the VPython programming environment. During the term 1357 students in this course solved a suite of fourteen computational modeling homework questions delivered using an online commercial course management system. Their proficiency with computational modeling was evaluated in a proctored environment using a novel central force problem. The majority of students (60.4%) successfully completed the evaluation. Analysis of erroneous student-submitted programs indicated that a small set of student errors explained why most programs failed. We discuss the design and implementation of the computational modeling homework and evaluation, the results from the evaluation and the implications for instruction in computational modeling in introductory STEM courses.

No abstract