Dialogue Acts (DAs) can be used to explain what expert tutors do and what students know during the tutoring process. Most empirical studies adopt the random sampling method to obtain sentence samples for manual annotation of DAs, which are then used to train DA classifiers. However, these studies have paid little attention to sample informativeness, which can reflect the information quantity of the selected samples and inform the extent to which a classifier can learn patterns. Notably, the informativeness level may vary among the samples and the classifier might only need a small amount of low informative samples to learn the patterns. Random sampling may overlook sample informativeness, which consumes human labelling costs and contributes less to training the classifiers. As an alternative, researchers suggest employing statistical sampling methods of Active Learning (AL) to identify the informative samples for training the classifiers. However, the use of AL methods in educational DA classification tasks is under-explored. In this paper, we examine the informativeness of annotated sentence samples. Then, the study investigates how the AL methods can select informative samples to support DA classifiers in the AL sampling process. The results reveal that most annotated sentences present low informativeness in the training dataset and the patterns of these sentences can be easily captured by the DA classifier. We also demonstrate how AL methods can reduce the cost of manual annotation in the AL sampling process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Background: Extensive research has documented the importance of faculty advisors for graduate students' experiences and outcomes. Recent research has begun to provide more nuanced accounts illuminating different dimensions of advisor support as well as attending to inequalities in students' experiences with advisors. Purpose: We extend the research on graduate student advisor relationships in two important ways. First, building on the concept of social capital, and in particular the work on institutional agents, we illuminate specific benefits associated with student-advisor relationships. Second, we advance prior work on inequality in advisor relationships by examining students' experiences at the intersection of race and gender. Research Design: To illuminate the nuances of graduate students' experiences with advisors, this study included interviews with 79 students pursuing PhD's in biological sciences. Thematic coding revealed several important dimensions of benefits associated with advisor relationships. Corresponding codes were grouped into three categories, describing three groups of students with notably different experiences with advisors. Findings: The data revealed three distinct student-advisor relationship profiles which we term scholars, subordinates, and marginals. The three groups had vastly different experiences with access to knowledge and resources, access to networks, and cultivation of independence. Moreover, the distribution across these three groups was highly unequal with unique patterns observed at the intersection of race and gender. White men benefited from both racial and gender privilege and were notably overrepresented in the scholars group while White women and racial/ethnic minority (REM) students were more likely to be socialized as subordinates. REM men had the least favorable experiences with the majority of them being in the marginal category, along with a substantial proportion of White and REM women. Notably, even experiences of negative relationships with advisors were gendered and raced: REM men's negative relationships with advisors were characterized by benign neglect while women primarily experienced conflictual relationships. Conclusion and Recommendations: The findings illuminate important consequences of student-advisor relationships and pronounced inequalities in who has access to benefits accrued through those relationships. Creating more equitable experiences will necessitate substantial attention to improving mentoring and eliminating gender and racial/ethnic inequalities in faculty support.

Mentoring promotes underserved students’ persistence in STEM but is difficult to scale up. Conversational virtual agents can help address this problem by conveying a mentor’s experiences to larger audiences. The present study examined college students’ (N= 138 ) utilization of CareerFair.ai, an online platform featuring virtual agent-mentors that were self-recorded by sixteen real-life mentors and built using principles from the earlier MentorPal framework. Participants completed a single-session study which included 30 min of active interaction with CareerFair.ai, sandwiched between pre-test and post-test surveys. Students’ user experience and learning gains were examined, both for the overall sample and with a lens of diversity and equity across different, potentially underserved demographic groups. Findings included positive pre/post changes in intent to pursue STEM coursework and high user acceptance ratings (e.g., expected benefit, ease of use), with under-represented minority (URM) students giving significantly higher ratings on average than non-URM students. Self-reported learning gains of interest, actual content viewed on the CareerFair.ai platform, and actual learning gains were associated with one another, suggesting that the platform may be a useful resource in meeting a wide range of career exploration needs. Overall, the CareerFair.ai platform shows promise in scaling up aspects of mentoring to serve the needs of diverse groups of college students. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Developing models and using mathematics are two key practices in internationally recognized science education standards, such as the Next Generation Science Standards (NGSS) [1]. However, students often struggle at the intersection of these practices, i.e., developing mathematical models about scientific phenomena. In this paper, we present the design and initial classroom test of AI-scaffolded virtual labs that help students practice these competencies. The labs automatically assess fine-grained sub-components of students’ mathematical modeling competencies based on the actions they take to build their mathematical models within the labs. We describe how we leveraged underlying machine-learned and knowledge-engineered algorithms to trigger scaffolds, delivered proactively by a pedagogical agent, that address students’ individual difficulties as they work. Results show that students who received automated scaffolds for a given practice on their first virtual lab improved on that practice for the next virtual lab on the same science topic in a different scenario (a near-transfer task). These findings suggest that real-time automated scaffolds based on fine-grained assessment data can help students improve on mathematical modeling. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Introduction: Informational graphics and data representations (e.g., charts and figures) are critical for accessing educational content. Novel technologies, such as the multimodal touchscreen which displays audio, haptic, and visual information, are promising for being platforms of diverse means to access digital content. This work evaluated educational graphics rendered on a touchscreen compared to the current standard for accessing graphical content. Method: Three bar charts and geometry figures were evaluated on student (N = 20) ability to orient to and extract information from the touchscreen and print. Participants explored the graphics and then were administered a set of questions (11-12 depending on graphic group). In addition, participants' attitudes using the mediums were assessed. Results: Participants performed statistically significantly better on questions assessing information orientation using the touchscreen than print for both bar chart and geometry figures. No statistically significant difference in information extraction ability was found between mediums on either graphic type. Participants responded significantly more favorably to the touchscreen than the print graphics, indicating them as more helpful, interesting, fun, and less confusing. Discussion: Accessing and orienting to information was highly successful by participants using the touchscreen, and was the preferred means of accessing graphical information when compared to the print image for both geometry figures and bar charts. This study highlights challenges in presenting graphics both on touchscreens and in print. Implications for Practitioners: This study offers preliminary support for the use of multimodal, touchscreen tablets as educational tools. Student ability using touchscreen-based graphics seems to be comparable to traditional types of graphics (large print and embossed, tactile graphics), although further investigation may be necessary for tactile graphic users. In summary, educators of students with blindness and visual impairments should consider ways to utilize new technologies, such as touchscreens, to provide more diverse access to graphical information.

Objective/Research Question: This research explores how community college students, who are underrepresented in science, technology, engineering, and mathematics (STEM) fields and aspire to vertical transfer in STEM make choices about majors and transfer destinations. The question is important to advancing equity in STEM, which continues to perpetuate disparities in attainment for minoritized, first-generation, and financially disadvantaged students, who disproportionately enter higher education in community colleges. Methods: Using a longitudinal, qualitative research design, the study relied on semi-structured interviewing to generate in-depth evidence about student experiences. Results: Findings showed that career goals were uniformly influential to students, yet career information was unevenly available or comprehensible during community college. Students’ choices about what to major in and where to transfer were iterative and intertwined, with these choices deeply connected to students’ families and lifetime priorities. Delays in student decision-making tended to have less to do with uncertain individual preferences than to lack of information about a specific STEM major and its alignment with possible future degrees, transfer destinations, and career pathways, as well as contingencies associated with the transfer admission process. Conclusions/Contributions: This research demonstrated STEM-specific nuance in how underrepresented community college students navigate major, career, and transfer destination decision-making as well as the influence of family and location-based priorities in student choices. Future research should investigate how to best provide directional support for students’ major and transfer destination decisions, including major-to-career awareness and the academic and personal dimensions of transfer. © The Author(s) 2023.

Background and Context. With increasing efforts to bring computing education opportunities into elementary schools, there is a growing need for assessments, with arguments for validity, to support research evaluation at these grade levels. After successfully piloting a 10-question computational thinking assessment (Assessment of Computing for Elementary Students - ACES) for 4th graders in Spring 2020, we used our analyses of item difficulty and discrimination to iterate on the assessment. Objectives. To increase the number of potential items for ACES, we created isomorphic versions of existing questions. The nature of the changes varied from incidental changes that we did not believe would impact student performance to more radical changes that seemed likely to influence question difficulty. We sought to understand the impact of these changes on student performance. Method. Using these isomorphic questions, we created two versions of our assessment and piloted them in Spring 2021 with 235 upper-elementary (4th grade) students. We analyzed the reliability of the assessments using Cronbach's alpha. We used Chi-squared tests to analyze questions that were identical across the two assessments to form a baseline of comparison and then ran Chi-Squared and Kruskal-Wallis H tests to analyze the differences between the isomorphic copies of the questions. Findings. Both assessment versions demonstrated good reliability, with identical Cronbach's alphas of 0.868. We found statistically similar performance on the identical questions between our two groups of students, allowing us to compare their performance on the isomorphic questions. Students performed differently on the isomorphic questions, indicating the changes to the questions had a differential impact on student performance. Implications. This paper builds on existing work by presenting methods for creating isomorphic questions. We provide valuable lessons learned, both on those methods and on the impact of specific types of changes on student performance.

This work compares two approaches to provide metacognitive interventions and their impact on preparing students for future learning across Intelligent Tutoring Systems (ITSs). In two consecutive semesters, we conducted two classroom experiments: Exp. 1 used a classic artificial intelligence approach to classify students into different metacognitive groups and provide static interventions based on their classified groups. In Exp. 2, we leveraged Deep Reinforcement Learning (DRL) to provide adaptive interventions that consider the dynamic changes in the student's metacognitive levels. In both experiments, students received these interventions that taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Six weeks later, we trained students on a probability tutor that only supports BC without interventions. Our results show that adaptive DRL-based interventions closed the metacognitive skills gap between students. In contrast, static classifier-based interventions only benefited a subset of students who knew how to use BC in advance. Additionally, our DRL agent prepared the experimental students for future learning by significantly surpassing their control peers on both ITSs.

We examined to what extent subgroups of students identified with learning disabilities (LDs; N = 630) in the Early Childhood Longitudinal Study, Kindergarten Class of 1998 to 1999 (ECLS-K): 1998 national longitudinal study displayed heterogeneity in longitudinal profiles of reading and mathematics achievement from first to eighth grades. Multivariate growth mixture modeling yielded four classes of combined reading and mathematics trajectories for students with LD. The largest class of students with LD (Class 2, 54.3%) showed mean T-scores for both achievement domains that averaged about 1 SD below the mean, with modest decline over time. Almost a quarter of the sample (Class 1, 22.3%) displayed mean T-scores in both achievement areas near the peer-normed average; these students were mostly White, from higher socioeconomic status (SES) backgrounds, and had experienced earlier classification as LD as well as shorter duration of LD service. Classifying heterogeneity in longitudinal trajectories of both achievement areas shows promise to better understand the educational needs of students classified LD.

Using data on ninth graders, math teachers, and schools from the nationally representative High School Longitudinal Study of 2009, we investigate the following questions: (1) How do ninth graders’ perceptions of their math teachers as equitable relate to their math identity at the intersection of adolescents’ race and gender? and (2) Do differences in the percentage of students at the school who share the adolescent’s race moderate (i.e., differentiate) the salience of perceptions of math teachers for adolescents’ math identities? Our results suggest that adolescents who perceive their math teachers as equitable typically have higher levels of math identity regardless of their race or gender. Adolescents’ perceptions of their math teachers as equitable are most salient for adolescents’ math identity in racially diverse schools, where racial differences and stereotypes may be more visible. Findings also indicate the seeming resistance of Black youth to racist stereotypes, whose math identity remains high regardless of their perceptions of their teachers.