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Three-Dimensional Learning (3DL) is a framework that provides tools to help highlight what we want our students to be able to do with their knowledge. This framework creates opportunities for students to learn physics in sways authentic to the practice of science by aligning course content along 3 Dimensions: Scientific Practices that encompass what scientists do to explore phenomena, Core Ideas that underpin our discipline&#39;s central concepts, and Crosscutting Concepts that provide the lenses for how we view phenomena. Research into 3DL has shown that implementing the framework provides substantial benefits to student learning, but does take effort. In this talk, I will present ways to approach 3DL and how use it to create assessment items. In particular, I will discuss how the3 Dimensional Learning Assessment Protocol can be used to create new 3D physics assessment items.

AAPT SM 22

Assessing Students&#39; Knowledge and Skills with 3 Dimensional Learning

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Three-Dimensional Learning (3DL) is a framework that provides tools to help highlight what we want our students to be able to do with their knowledge. This framework creates opportunities for students to learn physics in sways authentic to the practice of science by aligning course content along 3 Dimensions: Scientific Practices that encompass what scientists do to explore phenomena, Core Ideas that underpin our discipline's central concepts, and Crosscutting Concepts that provide the lenses for how we view phenomena. Research into 3DL has shown that implementing the framework provides substantial benefits to student learning, but does take effort. In this talk, I will present ways to approach 3DL and how use it to create assessment items. In particular, I will discuss how the3 Dimensional Learning Assessment Protocol can be used to create new 3D physics assessment items.

Assessing Students' Knowledge and Skills with 3 Dimensional Learning

technical paper

Introductory mechanics is classified as a general education laboratory science at many colleges and universities. General education outcomes often include the ability to reason from evidence or justify claims with evidence. These skills are also central components of the Next Generation Science Standards for K-12 education. In contrast to this mandate to focus on empirical reasoning, both the teaching and the assessing of the ability to reason from evidence are often implicit rather than explicit parts of the introductory mechanics laboratory curriculum. In this presentation I report the first results of an ongoing attempt to scaffold the learning of reasoning from evidence and to make the assessment of this skill explicit by employing the “Claim, Evidence and Reasoning”[1] framework in a college-level, algebra-based introductory mechanics laboratory.

Claims, Evidence and Reasoning in the Introductory Mechanics Lab

A primary learning outcome for our introductory physics labs for non-majors is scientific modeling. With over 80% of our students planning to enter the health profession, we have been seeking to create project-based labs with obvious applications to medical fields. We recently designed a lab where students model MRI principles, such as gradient magnetic fields and the relationship between magnetic field strength and magnetic resonance, using electromagnets. In this talk, I will discuss the lab prompts and present experimental data collected in the lab.

Physics labs that resonate with pre-med students

During an introductory physics lab on drop times, I asked my students to time intervals less than the average human reaction time. To my surprise almost all the students were able to accurately time these short falls. It led to the possibility that students were not merely reacting to the falling object but were predicting when the ball would hit the ground. I used this surprising observation as an opportunity to explore competing explanatory models. Students came up with fun and interesting experiments to distinguish these two models. In this talk I will discuss the experiments, and what my students found.

How did they time that? An Investigation of Fall Time

We have a General Physics I lab activity for students to investigate conservation of energy in a mass-spring system. A mass is hanging on a spring and oscillates up and down. The speed and height are recorded using a Pasco 850 universal interface and a motion sensor. The kinetic energy, potential energy, and total energy are calculated and graphed. Initially, the mass of spring was not taken into account. The variation of the system's energy with time could be as high as 40% of the maximum energy. After including the mass of the spring and testing for lighter and heavier springs, there was still 5-20% oscillation of a system’s total energy. Our measurements and findings are to be reported.

What Went Wrong in Mass-Spring Conservation of Energy?

One of the goals of physics instruction is to help students develop and refine their reasoning skills. However, measuring changes in reasoning is challenging since both content understanding, and appropriate reasoning strategies are necessary to arrive at a correct response. An instrument that would allow us to disentangle these factors (to the degree possible) would also allow us to measure changes along these two dimensions. In this study, we are drawing on the Dual-process theories of reasoning (DPToR) to develop an instrument based on the screening-target methodology: screening questions probe student conceptual understanding, and target questions require the application of that understanding in more complex situations. Comparison of performance on screening and target questions makes it possible to infer changes in conceptual understanding and reasoning skills due to instruction. We will show illustrative examples of student performance and discuss the implications for instrument development and implementation.

Measuring changes in student reasoning: Theoretical framework and methodology*

Response-shift bias (RSB) describes how instructional interventions induce a downshift in students’ self-assessment of their pre-instructional capabilities and interests, such as measured by the Colorado Learning Attitudes about Science Survey (CLASS). This downshift produces a misalignment between the students' perception of their learning experience and pre-to-post self-assessment shifts, possibly explaining why positive CLASS shifts are difficult to obtain. To further explore this downshift, we added a reflective question to four items on the CLASS asking students to explain how they determined their responses. We examine correlations between themes in students’ reflections, finding that the students made more connections with their conceptual knowledge at the end of the semester than at the beginning. This change highlights the role of RSB in their survey outcomes. These trends offer insight into additional dimensions of the student learning experience and could help physics educators design more engaging courses.

Impact of Response-Shift Bias on Students' Sense of Relevance

‘Doing physics' entails extensive use of mathematical expressions to contain and convey contextual information. Consequently, students' sensemaking on mathematical formalisms has attracted considerable attention in physics education research (PER). In the current work, we explore how introductory students construct meaning using algebraic inequalities while sensemaking on a physics problem. We qualitatively analyze a case study of a student (pseudonym: Matthew) through the frameworks of Sensemaking Epistemic Game and Symbolic Forms. The case study highlights Matthew’s verbal and written references to inequalities ‘driving’ explanation generation during sensemaking. A preliminary analysis of Matthew and other students' references to inequalities reveals a cluster of `comparing' symbolic forms conveying the idea of a quantitative comparison between physical quantities. Observations made in this study shed insights on how students construct and extract meaning through algebraic inequalities.

Analyzing students' sensemaking with algebraic inequalities

As part of a project to develop a written instrument for assessing learning assistants’ (LAs’) pedagogical content knowledge (PCK) in the context of questioning (PCK-Q), we are experimenting with questions in a Likert-style format. Previously, we have developed and validated questions in free-response format. We are now using those questions as the basis for Likert-style questions. Likert-style questions are different because they require the LAs’ to evaluate possible LA responses to students in classroom scenarios. They are also beneficial because scoring can be automated. The instrument will examine a LA’s ability to identify appropriate responses that provide evidence of the application of PCK-Q in the classroom. We will discuss problem development, present sample problems, and outline ongoing plans regarding validation, reliability testing, and dissemination efforts.

Development of a Likert-style instrument to assess LA’s PCK-Q

Concise, specific, and measurable statements of desired student learning, often called learning objectives, can aid instructors during curricular development. Similar statements, which we call assessment objectives, can aid researchers in developing and validating research-based assessments. In this talk, we discuss the benefits of articulating assessment objectives during the development of two assessments, one for upper-division thermodynamics and one for lower-division physics labs. Specifically, assessment objectives helped us identify concepts and practices that are important to instructors, aided in the development of targeted assessment items, provided an additional means for establishing face and construct validity, and can serve as a way for instructors and researchers to determine if these assessments are appropriate for use in their courses and research studies in the future.

Affordances of Articulating Assessment Objectives

We present a citizen science campaign, Dark Energy Explorers, aiming to improve the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). HETDEX will collect at least one million galaxies that are 9 - 11 billion light-years away, yielding one of the largest maps of the universe. We created Dark Energy Explorers on Zooniverse, the world’s largest citizen science platform to help us classify these astronomical objects. With Dark Energy Explorers we train participants, of any background, to be able to distinguish different galaxies by swiping left or right on their smartphone. Initial analysis has shown that citizen science is an efficient and effective tool for classification most accurately done by the human eye. Since launching in late February 2021, we have reached 3.5 million classifications by 8,000 volunteers in 80 countries! Dark Energy Explorers has proven to be a uniquely powerful tool for science advancement and increasing accessibility to science worldwide.

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Assessing Students' Knowledge and Skills with 3 Dimensional Learning

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