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Research on self-efficacy -- or the confidence in one’s capability to perform a task -- has been shown to predict achievement and persistence in the sciences. Throughout a student’s experience in curricular and/or co-curricular activities, they may face threats to their self-efficacy. To investigate the threats or supports to students’ self-efficacy, we are using a mixed methods approach. This novel design creates a holistic narrative of which curricular and/or co-curricular activities are impacting a student’s self-efficacy as well as how and why these activities may be affecting their self-efficacy. We use a mixed methods approach to collect in-the-moment information about lived experiences. Quantitatively, we employ the Experience Sampling Method (ESM) using surveys of domain-specific self-efficacy. Qualitatively, we use daily reflections to investigate threats and supports toward students’ self-efficacy. Using an example, we will present the research design while focusing on building this conversation between the ESM and qualitative reflections.

AAPT SM 21

Identifying Threats and Supports to Self-Efficacy Using Mixed Methods Research

self-efficacy

mixed methods

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AAPT WM 22

technical paper

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Abstract. Large Hadron Collider (LHC) and Higgs boson discovery reached audience levels unprecedented for a particle physics event. The LHC can create almost a billion proton-proton collisions per second. A non-zero Higgs Field was determined to alter the properties of these particles. Understanding how this field works is one of the central particle physics research areas and justified LHC construction. From a science education research perspective, how the particulate nature of matter is introduced in the classroom is an important. The purpose of this paper explores an alternate context for which subatomic particles may be introduced to K-12 students—space weather. NASA solar probes have studied space weather and the behavior of solar energetic particles since the 1960s. The perilous impact of space weather on global commerce has motivated greater research into its particulate nature and behavior.

Instructional Context for Astrophysics: Space Weather

The Next Generation Physics Science and Everyday Thinking (NGP) curriculum materials [1] were developed to give students learning experiences that integrate all three dimensions of the Next Generation Science Standards (NGSS) [2]. Analysis of a subset of the materials with EQUIP rubric [3] confirmed this alignment and integration. However, analysis of the test bank questions that accompany the materials (using the 3D-LAP [4]) revealed that most items being used for assessment did not integrate the three dimensions. We are therefore developing assessment tasks that follow the pattern of NGP activities in integrating all three dimensions. In this presentation, we will describe the path that led us to this point, the process we are using to develop these assessment tasks, and preliminary results.

[1] https://nextgenpet.activatelearning.com
[2] NGSS Lead States. (2013). Next Generation Science Standards: For States, By
States. Washington, DC: The National Academies Press.
[3] https://www.nextgenscience.org/resources/equip-rubric-science
[4] Laverty, J. T., et. al., (2016). PloS ONE,11(9): e0162333.


Developing NGSS-aligned assessment tasks for the Next Gen PET curriculum

The Colorado Learning Attitudes about Science Survey (CLASS) assesses students’ perceptions of physics through self-reported agreement with attitudinal statements. Previous work shows that CLASS results may be subject to response-shift bias, in which knowledge gained during an intervention causes learners’ perceptions of their pre-instructional state to become more novice-like. We explored possible sources of this response-shift bias in the CLASS by asking students to (1) complete the survey twice at the beginning of the semester (providing their pre-instruction responses and expected end-of-semester responses) and twice at the end of the semester (providing their post-instruction responses and retrospective beginning-of-semester responses), and (2) comment on their concurrent responses. By comparing beginning-of-semester and end-of-semester comments with survey shifts from a broad sample of students, we illuminate an underexplored dimension of student thinking. Preliminary results show that our students expect their perceptions of the applicability of physics will grow directly from acquiring physics knowledge.

Sources of Response-Shift Bias in the CLASS’s Real-World Connection

Educators and researchers often use research-based assessments to measure the efficacy of their courses. Limited resources exist, however, for interpreting assessment results. We will present analyses and representations created with data from more than 500 introductory physics courses that provide a context for interpreting assessment results. The data set comes from the online Learning About STEM Student Outcomes (LASSO) platform and the scientific literature. The representations include scatterplots of pretest and posttest scores and distributions of effect sizes, Cohen’s d with Hedges’ correction. These representations show how courses compare to the larger database before and after instruction. The results show that common rules of thumb for effect sizes are not useful for interpreting effect sizes on research-based assessments. Excel and R files are available at https://tinyurl.com/stemequityresources for readers to create their own visualizations overlaying their course data on the larger data set.

Tools for Identifying Effective Courses

As a method to shorten the test time of the Force Concept Inventory (FCI), we suggest the use of Computerized Adaptive Testing (CAT). CAT is the process of administering a test on a computer, with items (i.e., questions) selected based upon the responses of the examinee to prior items. In so doing, the test length can be significantly shortened. As a step to develop a CAT–based version of the FCI (FCI-CAT), we examined the optimal test length of the FCI-CAT such that accuracy and precision ([which we measure in terms of root-mean-square error (RMSE)] of Cohen’s d would be comparable to that of the full FCI for a given class size. We conducted a Monte Carlo simulation to analyze how the RMSE of Cohen’s d depend upon the test-length. As a result, we found, that for a class size of 40, we may reduce the test length of the FCI-CAT to 15-19 items, thereby reducing the test time of the FCI to 50% - 63%, with an accompanying decrease in accuracy and precision of only 5-10%.

Optimizing the length of computerized adaptive testing for the FCI

The presented study investigates the use of mathematical procedures to solve items in a kinematic context. For this purpose, a validated test instrument by Ceuppens et al. (2019) was administered to high school students which included pairs of items in mathematics and kinematics contexts that are isomorphic to each other, i.e., have the same surface features. 
While solving the tasks, eye movements of N=35 learners were recorded by a stationary eyetracker. For selected items, we demonstrate that gaze behavior and thus visual strategies differ between solving the mathematical and kinematic items. 
The results show that specific solution strategies are induced by the task context, which means that procedures that can be applied in the mathematical context cannot be transferred to the kinematic context. The talk presents the study and discuss the results in details.


Gaze data analysis in the interpretation of linear graphs

Assessment scores can be used to inform instructors about their students’ learning, ideally to inform subsequent course modifications to better facilitate students’ learning. However, explicit course modifications require interpretations about what assessment scores convey instructors about the extent to which students meet the learning goals instructors have for their students, i.e., actionable feedback for instructors (hereinafter feedback). As part of developing a standardized assessment for upper-division thermal physics, we articulate a theory-based approach to generating feedback that goes beyond simply reporting students’ scores. The extent to which students met the learning goals instructors have for them guides our feedback generation. In this talk/poster, we present our theory-based approach to feedback design by integrating effective feedback practices that promote instructors’ agency. This work emphasizes the development of feedback for instructors to better facilitate students’ learning in physics classrooms. 


Research-based Assessment Feedback for Instructors

Can roles improve group cooperation and make group-work more equitable? During the past year, we developed a series of hour-long collaborative activities to supplement individual investigations for our introductory physics lab course. Students worked in groups of 4 in Zoom breakout rooms with the iOLab lesson player. We developed the activities to revolve around 4 student roles: manager, archivist, experimentalist, and skeptic. Analysis based on student work, interviews, and survey responses suggested that the roles helped support student collaboration, especially when the roles where integrated into the tasks we asked students to complete. However, by themselves, group roles were not sufficient to prevent negative gendered group interactions.

Roles in Collaborative Introductory Lab Activities

Modified Module Analysis using partial correlations (MMA-P) has proven to be a productive method for studying conceptual inventories. MMA-P can be used to analyze large datasets by forming networks of responses then using community detection algorithms to identify sets of responses that are consistently selected together. MMA-P can be extended by using multilevel network analysis. A multilevel network was constructed with pretest and post-test Force Concept Inventory (FCI) responses from five different institutions using samples ranging from N=10,000 to N=200. Each layer of the multilevel network was represented by a network made from the application of MMA-P to data from each institution. Within each layer, MMA-P finds communities of popular responses. Network comparison methods were then used to understand the similarities and differences between the sets of nodes and communities in the networks. This provides insight into the generality of previously identified properties of the FCI between institutions.

Multilevel Module Analysis of the FCI

This talk examines the retention of physics majors at a moderately sized physics department which struggles with increasing the number of graduates using 20 years of graduation data. Survival analysis identifies the point of highest risk for two potential paths out of the major: leaving college and changing majors. Substantial risk of losing majors exists through the first two years of college. Logistic regression is used to explore the factors related to retention in general and through the first two years of college. Decision trees, a method applied in machine learning, is used to quantify the thresholds of these variables important to evaluating risk.


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