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1. EXPLORING AND EXAMINING QUANTITATIVE MEASURES

   Shih, J. ; Bostic, J. ; Carney, M. ; Krupa, E. ( October 2017 , Psychology of Mathematics Education North America)

   The purpose of this working group is to continue to bring together scholars with an interest in examining the use of and access to large-scale quantitative tools used to measure student- and teacher-related outcomes in mathematics education. The working group session will focus on (1) updating the workgroup on the progress made since the first working group at PME-NA in Tucson, Arizona, specifically focusing on the outcomes of the Validity Evidence for Measurement in Mathematics Education conference that took place in April, 2017, in San Antonio, (2) continued development of a document of available tools and their associated validity evidence, and (3) identification of potential follow-up activities to continue this work. The efforts of the group will be summarized and extended through both social media tools and online collaboration tools to further promote this work. 

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2. Design and Development: NSF Engineering Research Centers Unite: Developing and Testing a Suite of Instruments to Enhance Overall Education Program Evaluation

   https://doi.org/10.18260/1-2--36906  

   Zhao, Zhen ; Carberry, Adam ; Larson, Jean ; Jordan, Michelle ; Savenye, Wilhelmina ; Eustice, Kristi ; Godwin, Allison ; Roehrig, Gillian ; Barr, Christopher ; Farnsworth, Kimberly ( January 2021 , American Society for Engineering Education)

   National Science Foundation (NSF) funded Engineering Research Centers (ERC) must complement their technical research with various education and outreach opportunities to: 1) improve and promote engineering education, both within the center and to the local community; 2) encourage and include the underrepresented populations to participate in Engineering activities; and 3) advocate communication and collaboration between industry and academia. ERCs ought to perform an adequate evaluation of their educational and outreach programs to ensure that beneficial goals are met. Each ERC has complete autonomy in conducting and reporting such evaluation. Evaluation tools used by individual ERCs are quite similar, but each ERC has designed their evaluation processes in isolation, including evaluation tools such as survey instruments, interview protocols, focus group protocols, and/or observation protocols. These isolated efforts resulted in redundant resources spent and lacking outcome comparability across ERCs. Leaders from three different ERCs led and initiated a collaborative effort to address the above issue by building a suite of common evaluation instruments that all current and future ERCs can use. This leading group consists of education directors and external evaluators from all three partners ERCs and engineering education researchers, who have worked together for two years. The project intends to address the four ERC program clusters: Broadening Participation in Engineering, Centers and Networks, Engineering Education, and Engineering Workforce Development. The instruments developed will pay attention to culture of inclusion, outreach activities, mentoring experience, and sustained interest in engineering. The project will deliver best practices in education program evaluation, which will not only support existing ERCs, but will also serve as immediate tools for brand new ERCs and similar large-scale research centers. Expanding the research beyond TEEC and sharing the developed instruments with NSF as well as other ERCs will also promote and encourage continual cross-ERC collaboration and research. Further, the joint evaluation will increase the evaluation consistency across all ERC education programs. Embedded instrumental feedback loops will lead to continual improvement to ERC education performance and support the growth of an inclusive and innovative engineering workforce. Four major deliveries are planned. First, develop a common quantitative assessment instrument, named Multi-ERC Instrument Inventory (MERCII). Second, develop a set of qualitative instruments to complement MERCII. Third, create a web-based evaluation platform for MERCII. Fourth, update the NSF ERC education program evaluation best practice manual. These deliveries together will become part of and supplemented by an ERC evaluator toolbox. This project strives to significantly impact how ERCs evaluate their educational and outreach programs. Single ERC based studies lack the sample size to truly test the validity of any evaluation instruments or measures. A common suite of instruments across ERCs would provide an opportunity for a large scale assessment study. The online platform will further provide an easy-to-use tool for all ERCs to facilitate evaluation, share data, and reporting impacts. 

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3. An Inquiry into the Use of Intercoder Reliability Measures in Qualitative Research

   Wilson-Lopez, A. ; Minichiello, A. ; Green, T. ( July 2019 , ASEE Annual Conference proceedings)

   In this theory paper, we set out to consider, as a matter of methodological interest, the use of quantitative measures of inter-coder reliability (e.g., percentage agreement, correlation, Cohen’s Kappa, etc.) as necessary and/or sufficient correlates for quality within qualitative research in engineering education. It is well known that the phrase qualitative research represents a diverse body of scholarship conducted across a range of epistemological viewpoints and methodologies. Given this diversity, we concur with those who state that it is ill advised to propose recipes or stipulate requirements for achieving qualitative research validity and reliability. Yet, as qualitative researchers ourselves, we repeatedly find the need to communicate the validity and reliability—or quality—of our work to different stakeholders, including funding agencies and the public. One method for demonstrating quality, which is increasingly used in qualitative research in engineering education, is the practice of reporting quantitative measures of agreement between two or more people who code the same qualitative dataset. In this theory paper, we address this common practice in two ways. First, we identify instances in which inter-coder reliability measures may not be appropriate or adequate for establishing quality in qualitative research. We query research that suggests that the numerical measure itself is the goal of qualitative analysis, rather than the depth and texture of the interpretations that are revealed. Second, we identify complexities or methodological questions that may arise during the process of establishing inter-coder reliability, which are not often addressed in empirical publications. To achieve this purposes, in this paper we will ground our work in a review of qualitative articles, published in the Journal of Engineering Education, that have employed inter-rater or inter-coder reliability as evidence of research validity. In our review, we will examine the disparate measures and scores (from 40% agreement to 97% agreement) used as evidence of quality, as well as the theoretical perspectives within which these measures have been employed. Then, using our own comparative case study research as an example, we will highlight the questions and the challenges that we faced as we worked to meet rigorous standards of evidence in our qualitative coding analysis, We will explain the processes we undertook and the challenges we faced as we assigned codes to a large qualitative data set approached from a post positivist perspective. We will situate these coding processes within the larger methodological literature and, in light of contrasting literature, we will describe the principled decisions we made while coding our own data. We will use this review of qualitative research and our own qualitative research experiences to elucidate inconsistencies and unarticulated issues related to evidence for qualitative validity as a means to generate further discussion regarding quality in qualitative coding processes. 

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4. The Development of a Measure of Engineering Identity

   https://doi.org/10.18260/p.26122  

   Godwin, Allison ( January 2016 , ASEE Annual Conference & Exposition)

   Measures of subject-related role identities in physics and math have been developed from research on the underlying constructs of identity in science education. The items for these measures capture three constructs of identity: students’ interest in the subject, students’ feeling of recognition by others, and students’ beliefs about their performance/competence in the subject area. In prior studies with late secondary and early post-secondary students, participants did not distinguish between performance beliefs (e.g., believing that they can do well in a particular subject) and competence beliefs (e.g., believing that they can understand a particular subject); therefore, performance/competence beliefs are measured as a single construct. These validated measures have been successful in predicting STEM career choices including physics, math, and engineering. Based on these measures of identity, literature on engineering identity, and my prior work on understanding engineering choice and belongingness through students’ science and math identities at the transition from high school to college, I developed a set of new engineering identity measures that capture and overall identification as an engineer, future engineering career identification, and students’ engineering-related interest, recognition, and performance/competence beliefs. I conducted a pilot survey of 371 first-year engineering students at three institutions within the U.S. during the spring semester of 2015. An exploratory factor analysis (EFA) was performed to examine the underlying structure of the piloted questions about students’ engineering identity. The measures loaded on three separate constructs that were consistent with the hypothesized constructs of interest, performance/competence and recognition. The developed items were used in a subsequent study deployed in the fall semester of 2015 that measured more than 2500 first-year engineering students’ attitudes and beliefs at four institutions within the U.S. The data on engineering identity measures from this second survey were analyzed using confirmatory factor analysis (CFA). The results indicated that the developed measures do extract a significant portion of the average variance in the latent constructs and the internal consistency of the measures (Cronbach’s α) falls within the acceptable and better range. The development of these items provides ways for engineering education researchers to more deeply explore the underlying self-beliefs in students’ engineering identity formation through quantitative measures with strong evidence for validity. 

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5. Predictors of Jurors’ Understanding of Evidence Strength

   McCowan, K. ( March 2020 , Annual Conference of the American Psychology-Law Society.)

   Abstract Expert testimony varies in scientific quality and jurors have a difficult time evaluating evidence quality (McAuliff et al., 2009). In the current study, we apply Fuzzy Trace Theory principles, examining whether visual and gist aids help jurors calibrate to the strength of scientific evidence. Additionally we were interested in the role of jurors’ individual differences in scientific reasoning skills in their understanding of case evidence. Contrary to our preregistered hypotheses, there was no effect of evidence condition or gist aid on evidence understanding. However, individual differences between jurors’ numeracy skills predicted evidence understanding. Summary Poor-quality expert evidence is sometimes admitted into court (Smithburn, 2004). Jurors’ calibration to evidence strength varies widely and is not robustly understood. For instance, previous research has established jurors lack understanding of the role of control groups, confounds, and sample sizes in scientific research (McAuliff, Kovera, & Nunez, 2009; Mill, Gray, & Mandel, 1994). Still others have found that jurors can distinguish weak from strong evidence when the evidence is presented alone, yet not when simultaneously presented with case details (Smith, Bull, & Holliday, 2011). This research highlights the need to present evidence to jurors in a way they can understand. Fuzzy Trace Theory purports that people encode information in exact, verbatim representations and through “gist” representations, which represent summary of meaning (Reyna & Brainerd, 1995). It is possible that the presenting complex scientific evidence to people with verbatim content or appealing to the gist, or bottom-line meaning of the information may influence juror understanding of that evidence. Application of Fuzzy Trace Theory in the medical field has shown that gist representations are beneficial for helping laypeople better understand risk and benefits of medical treatment (Brust-Renck, Reyna, Wilhelms, & Lazar, 2016). Yet, little research has applied Fuzzy Trace Theory to information comprehension and application within the context of a jury (c.f. Reyna et. al., 2015). Additionally, it is likely that jurors’ individual characteristics, such as scientific reasoning abilities and cognitive tendencies, influence their ability to understand and apply complex scientific information (Coutinho, 2006). Methods The purpose of this study was to examine how jurors calibrate to the strength of scientific information, and whether individual difference variables and gist aids inspired by Fuzzy Trace Theory help jurors better understand complicated science of differing quality. We used a 2 (quality of scientific evidence: high vs. low) x 2 (decision aid to improve calibration - gist information vs. no gist information), between-subjects design. All hypotheses were preregistered on the Open Science Framework. Jury-eligible community participants (430 jurors across 90 juries; Mage = 37.58, SD = 16.17, 58% female, 56.93% White). Each jury was randomly assigned to one of the four possible conditions. Participants were asked to individually fill out measures related to their scientific reasoning skills prior to watching a mock jury trial. The trial was about an armed bank robbery and consisted of various pieces of testimony and evidence (e.g. an eyewitness testimony, police lineup identification, and a sweatshirt found with the stolen bank money). The key piece of evidence was mitochondrial DNA (mtDNA) evidence collected from hair on a sweatshirt (materials from Hans et al., 2011). Two experts presented opposing opinions about the scientific evidence related to the mtDNA match estimate for the defendant’s identification. The quality and content of this mtDNA evidence differed based on the two conditions. The high quality evidence condition used a larger database than the low quality evidence to compare to the mtDNA sample and could exclude a larger percentage of people. In the decision aid condition, experts in the gist information group presented gist aid inspired visuals and examples to help explain the proportion of people that could not be excluded as a match. Those in the no gist information group were not given any aid to help them understand the mtDNA evidence presented. After viewing the trial, participants filled out a questionnaire on how well they understood the mtDNA evidence and their overall judgments of the case (e.g. verdict, witness credibility, scientific evidence strength). They filled this questionnaire out again after a 45-minute deliberation. Measures We measured Attitudes Toward Science (ATS) with indices of scientific promise and scientific reservations (Hans et al., 2011; originally developed by National Science Board, 2004; 2006). We used Drummond and Fischhoff’s (2015) Scientific Reasoning Scale (SRS) to measure scientific reasoning skills. Weller et al.’s (2012) Numeracy Scale (WNS) measured proficiency in reasoning with quantitative information. The NFC-Short Form (Cacioppo et al., 1984) measured need for cognition. We developed a 20-item multiple-choice comprehension test for the mtDNA scientific information in the cases (modeled on Hans et al., 2011, and McAuliff et al., 2009). Participants were shown 20 statements related to DNA evidence and asked whether these statements were True or False. The test was then scored out of 20 points. Results For this project, we measured calibration to the scientific evidence in a few different ways. We are building a full model with these various operationalizations to be presented at APLS, but focus only on one of the calibration DVs (i.e., objective understanding of the mtDNA evidence) in the current proposal. We conducted a general linear model with total score on the mtDNA understanding measure as the DV and quality of scientific evidence condition, decision aid condition, and the four individual difference measures (i.e., NFC, ATS, WNS, and SRS) as predictors. Contrary to our main hypotheses, neither evidence quality nor decision aid condition affected juror understanding. However, the individual difference variables did: we found significant main effects for Scientific Reasoning Skills, F(1, 427) = 16.03, p <.001, np2 = .04, Weller Numeracy Scale, F(1, 427) = 15.19, p <.001, np2 = .03, and Need for Cognition, F(1, 427) = 16.80, p <.001, np2 = .04, such that those who scored higher on these measures displayed better understanding of the scientific evidence. In addition there was a significant interaction of evidence quality condition and scores on the Weller’s Numeracy Scale, F(1, 427) = 4.10, p = .04, np2 = .01. Further results will be discussed. Discussion These data suggest jurors are not sensitive to differences in the quality of scientific mtDNA evidence, and also that our attempt at helping sensitize them with Fuzzy Trace Theory-inspired aids did not improve calibration. Individual scientific reasoning abilities and general cognition styles were better predictors of understanding this scientific information. These results suggest a need for further exploration of approaches to help jurors differentiate between high and low quality evidence. Note: The 3rd author was supported by an AP-LS AP Award for her role in this research. Learning Objective: Participants will be able to describe how individual differences in scientific reasoning skills help jurors understand complex scientific evidence. 

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