Gita Taasoobshirazi

Over the past decade, the demand for skilled statisticians and data scientists has grown dramatically, driving a need to attract diverse talent to the field. To foster a more diverse and inclusive data science workforce, it is critical to understand how students perceive the field and see themselves within it. This study examined how 168 undergraduate and master’s-level statistics students perceive data scientists, and how those perceptions relate to students’ own gender and racial identities. We further assessed key motivational factors (self-efficacy, expectancy, domain identification, persistence) and attitudes (social dominance orientation, gendered associations of statistics). Results revealed a predominant perception of data scientists as White and male. However, female students in more advanced courses were significantly more likely to report a female data scientist. Male students reported stronger social dominance orientation and a greater tendency to associate statistical terms with masculinity. Both domain identification and class level predicted persistence intentions. These findings underscore the role of stereotypes, motivation, and attitudes in relation to student trajectories, offering critical insights for developing interventions to promote inclusivity in statistics education.

As artificial intelligence (AI) becomes increasingly integrated into educational settings, understanding how students perceive and prefer to interact with an AI-Buddy (learning companion) is essential for responsible design. This study investigated 168 undergraduate students’ perceptions and preferences regarding an AI-Buddy, including its pedagogical role, knowledge level, identity customization, voice, mind attribution, demographic characteristics, and comfort sharing academic struggles. Results revealed that students most frequently preferred the AI-Buddy as a tutor with an omniscient or professor-level knowledge base, a non-human and neutral interface, and a warm yet robotic voice. Preferences for mind attribution were split, with nearly half preferring low mind attribution. Female students were more likely to prefer a female AI-Buddy, and male students a male AI-Buddy; White and Black students showed same-race preferences. These findings highlight the diversity of student preferences and underscore the need for a flexible, customizable AI-Buddy that prioritizes transparency and user agency rather than one-size-fits-all designs. We discuss how our findings can be extended to future research and educational AI design.

Background: Wearable fitness trackers have become a valuable tool in public health research due to their ability to collect large-scale, individual-level data at a low cost. Because of this, wearables have the potential to mitigate the urgent need for health research in low-resource settings. However, their use has been largely limited to high-income settings, and a major challenge remains: high rates of missing data. This problem may be exacerbated in low-resource environments where logistical and operational barriers further complicate data collection. Wearable sleep data collected in an urban informal settlement in Uganda during The Onward Project on Well-being and Adversity project revealed substantial challenges related to missingness in low-resource research settings. Objective: The study aimed to characterize the patterns and frequency of missing data and to demonstrate the use of analytical methods to determine the underlying missingness mechanism. Methods: For this study, 300 women in slum communities in Kampala were equipped with Garmin smartwatches that collected data over 5 days. The nature of missingness was assessed through four methods: pattern analysis, the Little test, a random forest classification model, and a logistic regression classification model. Pattern analysis is an established method of identifying missing data patterns, while the Little test is used specifically to identify missing completely at random data. Random forest and logistic regression models are more recent methods proposed to identify the mechanism of missingness; both were used to examine agreement between the 2 methods and to ensure a thorough examination of missingness patterns. Results: Approximately 30% of nighttime data were missing. Three patterns were identified that occurred in over 10% of participants’ data: no data missing, fifth night of data missing, and all nights of data missing. Pattern analysis and the Little test ( P <.001) indicated that the data were not missing completely at random. Both the random forest (area under the curve=0.7) and logistic regression models suggested that the data were missing at random (MAR). Conclusions: Evidence of missingness in this dataset was consistent with MAR. Potential causes of missingness include device removal, battery failure, and technical malfunctions. These findings have important implications for both wearable device users and future research. Understanding the mechanisms behind missingness can inform strategies to improve data quality, particularly in low-resource settings, and allow researchers to manage missingness using appropriate strategies designed to handle MAR data without introducing bias into the results.

This study pilots a unique test item known as the ordered multiple-choice (OMC) item. These OMC items were administered to two high school astronomy classrooms participating in a NASA classroom of the future: Astronomy village program. The OMC items were included on a pre- and post-test to assess common misconceptions in astronomy, as part of study employing a quasi-experimental design. Each answer choice in an OMC item is linked to varying levels of student understanding, allowing for diagnostic interpretation of student responses. Results from the items indicated that although students did improve in their levels of understanding at the end of the program, there were still gaps in the students’ knowledge. We anticipate these items will allow for a unique and comprehensive assessment of student understanding of science.

This paper recommends that the research on giftedness, expertise, and gender/racial disparities in science be used in combination, on behalf of a new theoretical framework, for studying academic success in science, technology, engineering, and mathematics. The variables characterizing expertise are presented followed by a discussion of what constitutes giftedness. We then discuss the variables considered to be contributing factors to gender and racial disparities in science. The paper concludes that the variables that define these areas of research can comprehensively identify and provide a firm paradigm for what researchers should evaluate collectively to understand success in science. We put forth several recommendations for future research studying science learning and for efforts to support expertise, particularly for women and underrepresented minorities.

The present study analyzes Imposter Phenomenon (IP) through the lens of three different motivational frameworks. Expectancy Value Theory, Attribution Theory, and Self-Determination Theory were used to study IP among academics. With 72% of participants experiencing frequent or intense IP levels, IP was prevalent among those sampled. Females experienced higher IP than males, although race and first-generation status did not significantly impact IP levels. Post docs had higher IP scores than tenured faculty and full-time non-tenured faculty had higher IP scores than tenured faculty. Younger academics had higher IP scores. Analyses of the motivational frameworks showed significant differences by IP level.

Stereotype threat (ST) has been extensively explored as an explanation for gender disparities in achievement and participation in mathematics. However, there is a lack of research evaluating ST in statistics. The present study evaluated the impact of ST on gender differences in student performance, self-efficacy, and anxiety in statistics using a four-group, quasi-experimental design. Specifically, 102 elementary statistics students at a university in the Southeast United States were randomly assigned to one of four ST conditions including an explicit ST condition, an implicit ST condition, a reverse ST condition, and a nullified ST condition. Results indicated that there were no gender differences by ST condition in statistics self-efficacy, test anxiety, and performance. Analyses of student responses to open-ended questions indicated that females were more likely than males to report that they had fewer opportunities to achieve in statistics. Implications of our findings and suggestions for future research are discussed.

Impostor Phenomenon (IP), also called impostor syndrome, involves feelings of perceived fraudulence, self-doubt, and personal incompetence that persist despite one’s education, experience, and accomplishments. This study is the first to evaluate the presence of IP among data science students and to evaluate several variables linked to IP simultaneously in a single study evaluating data science. In addition, it is the first study to evaluate the extent to which gender identification is linked to IP. We examined: (1) the degree to which IP exists in our sample; (2) how gender identification is linked to IP; (3) whether there are differences in goal orientation, domain identification, perfectionism, self-efficacy, anxiety, personal relevance, expectancy, and value for different levels of IP; and (4) the extent to which goal orientation, domain identification, perfectionism, self-efficacy, anxiety, personal relevance, expectancy, and value predict IP. We found that most students in the sample showed moderate and frequent levels of IP. Moreover, gender identification was positively related to IP for both males and females. Finally, results indicated significant differences in perfectionism, value, self-efficacy, anxiety, and avoidance goals by IP level and that perfectionism, self-efficacy, and anxiety were particularly noteworthy in predicting IP. Implications of our findings for improving IP among data science students are discussed.

Study purpose. The Metarepresentations Survey for Physics (MSP) was developed to assess students’ metarepresentational knowledge during physics problem solving. Materials and methods. The survey was given to 288 introductory-level college physics students. Psychometric properties of the instrument, including construct validity, were evaluated by confirmatory factor analysis and Rasch analysis. Results. We also examined students’ beliefs about the use of free-body diagrams, as well as thoroughly examined the link between students’ problem solving success and free-body diagrams. Conclusions. We recommend the use of the MSP for physics instructors and science education researchers who want to evaluate students’ free-body diagrams. Additionally, we suggest the subject of physics can be replaced with chemistry, genetics, or another science to assess metarepresentations in other domains.