nTIPERs: Tasks to Help Students “Unpack” Aspects of Newtonian Mechanics

Maloney, David P.; Hieggelke, Curtis; Kanim, Stephen; Singh, Chandralekha; Sabella, Mel; Rebello, Sanjay

doi:10.1063/1.3515239

Cited by 3 publications

(3 citation statements)

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“…Unlike previous physics education research (PER) studies designed to improve problem-solving skills through different styles of problems [10,11], feedback [12], and class dynamics [11,13,14], our intervention focused on helping students prepare for the exam, after they had already completed all of the course-required learning activities. This research builds on the testing effect [3] and learning activities such as worked examples [6,7] and tutoring [9].…”

Section: Introductionmentioning

confidence: 99%

Physics exam preparation: A comparison of three methods

Fakcharoenphol

Stelzer

2014

Phys. Rev. ST Phys. Educ. Res.

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In this clinical study on helping students prepare for an exam, we compared three different treatments. All students were asked to take a practice exam. One group was then given worked-out solutions for that exam, another group was given the solutions and targeted exercises to do as homework based on the result of their practice exam, and the third group was given the solutions, homework, and also an hour of one-on-one tutoring. Participants from all three conditions significantly outperformed the control group on the midterm exam. However, participants that had one-on-one tutoring did not outperform the other two participant groups.

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Section: Introductionmentioning

confidence: 99%

Physics exam preparation: A comparison of three methods

Fakcharoenphol

Stelzer

2014

Phys. Rev. ST Phys. Educ. Res.

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“…Physics education researchers describe student reasoning about change using the language of proportional reasoning [9,[41][42][43] and scaling [44][45][46]. In the context of covariational reasoning, we argue that proportional reasoning is a linear version of covariation and that scaling could be described in the language of the mathematics covariational reasoning framework as Mental Action 4: Chunky Continuous, in which one might ask "if I double this, what happens to that?"…”

Section: A Modeling and Covariational Reasoningmentioning

confidence: 95%

Toward understanding and characterizing expert physics covariational reasoning

Zimmerman¹,

Olsho²,

Brahmia³

et al. 2020

2019 Physics Education Research Conference Proceedings

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Relating two quantities to describe a physical system or process is at the heart of "doing physics" for novices and experts alike. In this paper, we explore the ways in which experts use covariational reasoning when solving introductory physics graphing problems. Here, graduate students are considered experts for the introductory level material, as they often take the role of instructor at large research universities. Drawing on work from Research in Undergraduate Mathematics Education (RUME), we replicated a study of mathematics experts' covariational reasoning done by Hobson and Moore with physics experts [N. L. F. Hobson and K. C. Moore, in RUME Conference Proceedings, pp. 664-672 (2017)]. We conducted think-aloud interviews with 10 physics graduate students using tasks minimally adapted from the mathematics study. Adaptations were made solely for the purpose of participant understanding of the question, and validated by preliminary interviews. Preliminary findings suggest physics experts approach covariational reasoning problems significantly differently than mathematics experts. In particular, two behaviors are identified in the reasoning of expert physicists that were not seen in the mathematics study. We introduce these two behaviors, which we call Using Compiled Relationships and Neighborhood Analysis, and articulate their differences from the behaviors articulated by Hobson and Moore. Finally, we share implications for instruction and questions for further research.

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“…Moving the course to edX in Summer 2013, we had to replace about one-third of the course assessment problems that were shared from the LON-CAPA network. We replaced these with research-based problems similar to those found in Tasks Inspired by Physics Education Research (TIPERS) (Maloney, Hieggelke, & Kanim, 2010) and the Mechanics Reasoning Inventory (Pawl, Barrantes, Cardamone, Rayyan, & Pritchard, 2010). The strong similarity of content of the courses allowed us to better examine the effects of the few differences that did exist.…”

Section: Comparison Of the Mooc Offeringsmentioning

confidence: 99%

A MOOC based on blended pedagogy

Rayyan

Fredericks

Colvin

et al. 2016

Computer Assisted Learning

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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.

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nTIPERs: Tasks to Help Students “Unpack” Aspects of Newtonian Mechanics

Cited by 3 publications

References 0 publications

Physics exam preparation: A comparison of three methods

Physics exam preparation: A comparison of three methods

Toward understanding and characterizing expert physics covariational reasoning

A MOOC based on blended pedagogy

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