Abstract. We describe a modeling approach to help students learn expert problem solving. Models are used to present and hierarchically organize the syllabus content and apply it to problem solving, but students do not develop and validate their own Models through guided discovery. Instead, students classify problems under the appropriate instructor-generated Model by selecting a system to consider and describing the interactions that are relevant to that system. We believe that this explicit System, Interactions and Model (S.I.M.) problem modeling strategy represents a key simplification and clarification of the widely disseminated modeling approach originated by Hestenes and collaborators. Our narrower focus allows modeling physics to be integrated into (as opposed to replacing) a typical introductory college mechanics course, while preserving the emphasis on understanding systems and interactions that is the essence of modeling. We have employed the approach in a three-week review course for MIT freshmen who received a D in the fall mechanics course with very encouraging results.

We describe three iterations of a Massive Open Online Course (MOOC) developed from online preparation materials for a reformed introductory physics classroom at the Massachusetts Institute of Technology, in which the teaching staff interact with small groups of students doing problems using an expert problem‐solving pedagogy. The MOOC contains an e‐text, simple checkpoint problems and homework. We show how certain course design aspects affect student behaviour: (a) frequent quizzes correlated with students reading a large fraction of the e‐text, and (b) When homework sets are arranged by increasing (instructor‐estimated) difficulty, we found strong correlations between difficulty and time to solution, but weak correlations with percent correct. Modifications to the second offering of the course resulted in higher retention. These modifications included targeting physics teachers and posting materials well in advance. We define retention as certificates earned relative to participants who make a significant effort on the second assignment. Retention measured this way varied between 44% and 72%, being highest in the course aimed at teachers. We show that there is significant learning among MOOC participants. Applying item response theory to common homework problems showed that the MOOC participants had significantly higher ability than students in a Massachusetts Institute of Technology course and that they maintained this advantage over the duration of the MOOC.

We have given a group of 56 Massachusetts Institute of Technology (MIT) seniors who took mechanics as freshmen a written test similar to the final exam they took in their freshman course as well as the Mechanics Baseline Test (MBT) and the Colorado Learning Attitudes about Science Survey (CLASS). Students in majors unrelated to physics scored 60% lower on the written analytic part of the final than they would have as freshmen. The mean score of all participants on the MBT was insignificantly changed from their average on the posttest they took as freshmen. However, the students' performance on 9 of the 26 MBT items (with 6 of the 9 involving graphical kinematics) represents a gain over their freshman posttest score (a normalized gain of about 70%), while their performance on the remaining 17 questions is best characterized as a loss of approximately 50% of the material learned in the freshman course. On multiple-choice questions covering advanced physics concepts, the mean score of the participants was about 50% lower than the average performance of freshmen. Although attitudinal survey results indicate that almost half the seniors feel the specific mechanics course content is unlikely to be useful to them, a significant majority (75%-85%) feel that physics does teach valuable problem solving skills, and an overwhelming majority believe that mechanics should remain a required course at MIT.

Abstract. We investigate student-chosen, multi-level homework in our Integrated Learning Environment for Mechanics [1] built using the LON-CAPA [2] open-source learning system. Multi-level refers to problems categorized as easy, medium, and hard. Problem levels were determined a priori based on the knowledge needed to solve them [3]. We analyze these problems using three measures: time-per-problem, LON-CAPA difficulty, and item difficulty measured by item response theory. Our analysis of student behavior in this environment suggests that time-per-problem is strongly dependent on problem category, unlike either score-based measures. We also found trends in student choice of problems, overall effort, and efficiency across the student population. Allowing students choice in problem solving seems to improve their motivation; 70% of students worked additional problems for which no credit was given.

Abstract. We are building in LON-CAPA an integrated learning environment that will enable the development, dissemination and evaluation of PER-based material. This environment features a collection of multi-level researchbased homework sets organized by topic and cognitive complexity. These sets are associated with learning modules that contain very short exposition of the content supplemented by integrated open-access videos, worked examples, simulations, and tutorials (some from ANDES). To assess students' performance accurately with respect to a systemwide standard, we plan to implement Item Response Theory. Together with other PER assessments and purposeful solicitation of student feedback, this will allow us to measure and improve the efficacy of various research-based materials, while getting insights into teaching and learning.