Background: Learning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an implicit assumption that such measurements are free of errors. Objectives: This study addresses these gaps by investigating the psychometric pros and cons of aggregate measurements. Methods: This study proposes a framework for aggregating process data, which includes the conditions where aggregation is appropriate, and a guideline for selecting the proper reliability evidence and the computing procedure. We support and demonstrate the framework by analysing undergraduates' academic procrastination and programming proficiency in an introductory computer science course. Results and Conclusion: Aggregation over a period is acceptable and may improve measurement reliability only if the construct of interest is stable during the period. Otherwise, aggregation may mask meaningful changes in behaviours and should be avoided. While selecting the type of reliability evidence, a critical question is whether process data can be regarded as repeated measurements. Another question is whether the lengths of processes are unequal and individual events are unreliable. If the answer to the second question is no, segmenting each process into a fixed number of bins assists in computing the reliability coefficient. Major Takeaways: The proposed framework can be a general guideline for aggregating process data in LA research. Researchers should check and report the reliability evidence for aggregate measurements before the ensuing interpretation. © 2024 John Wiley & Sons Ltd.

Although it is well known that Black women are underrepresented in computing, less is known about their pre-college experiences. We hypothesize that inequities at the K-12 level result in Black women's underrepresentation in computing, because Black women have accumulated less social capital and are less embedded in courses and organizations related to computing prior to college. This paper reports the initial findings from the first round of a survey designed to gather the pre-college computing experiences of Black women and their peers. Black women in our sample were less likely to report participating in formal computer science (CS) education in school, slightly more likely to report participation in outside-of-school computing programs, about equally as likely to pursue computing experiences independently, and more likely to have had no pre-college computing experiences at all. We found that Black women were less likely to report that they were told they would be a good computer scientist, especially by friends, teachers, and guidance counselors, thus reflecting weaker social connections and lower levels of social capital. These findings suggest that organizational embeddedness or social ties from pre-college computing experiences may indeed be a factor in Black women's underrepresentation in computing and that access to these experiences outside of the formal classroom may be particularly important. The survey is one part of a study that will feature a second round of data collection in another state, analysis of state-level longitudinal data, and interviews with Black women.

Teachers' knowledge of the subject matter is considered an important component of their expertise in teaching mathematics. Yet how teachers' understanding of one content area is related to other content areas has not been investigated in depth. We explored this question by investigating teachers' knowledge of two theoretically related areas: (1) fractions and (2) ratios and proportional relationships. We also investigated the extent to which teachers' educational backgrounds are related to their understanding of these concepts. Based on the results obtained from structural equation modeling and path analysis, we found that teachers' knowledge of these two concepts is highly interdependent, forming a single construct. Furthermore, holding a credential in teaching mathematics, the route teachers took to enter teaching, and their undergraduate majors were associated with their knowledge of these concepts. This study illustrates the importance of attending to the theoretical relationships among different content areas when assessing teachers' subject matter knowledge and provides initial evidence that teachers' subject matter knowledge may be unidimensional for theoretically related domains.

Wealth-based disparities in health care wherein the poor receive undertreatment in painful conditions are a prominent issue that requires immediate attention. Research with adults suggests that these disparities are partly rooted in stereotypes associating poor individuals with pain insensitivity. However, whether and how children consider a sufferer's wealth status in their pain perceptions remains unknown. The present work addressed this question by testing 4- to 9-year-olds from the US and China. In Study 1 (N = 108, 56 girls, 79% White), US participants saw rich and poor White children experiencing identical injuries and indicated who they thought felt more pain. Although 4- to 6-year-olds responded at chance, children aged seven and above attributed more pain to the poor than to the rich. Study 2 with a new sample of US children (N = 111, 56 girls, 69% White) extended this effect to judgments of White adults' pain. Pain judgments also informed children's prosocial behaviors, leading them to provide medical resources to the poor. Studies 3 (N = 118, 59 girls, 100% Asian) and 4 (N = 80, 40 girls, 100% Asian) found that, when evaluating White and Asian people's suffering, Chinese children began to attribute more pain to the poor than to the rich earlier than US children. Thus, unlike US adults, US children and Chinese children recognize the poor's pain from early on. These findings add to our knowledge of group-based beliefs about pain sensitivity and have broad implications on ways to promote equitable health care.Research HighlightsFour studies examined whether 4- to 9-year-old children's pain perceptions were influenced by sufferers' wealth status.US children attributed more pain to White individuals of low wealth status than those of high wealth status by age seven.Chinese children demonstrated an earlier tendency to attribute more pain to the poor (versus the rich) compared to US children.Children's wealth-based pain judgments underlied their tendency to provide healthcare resources to people of low wealth status.

Given math -related fields are still highly racialized and gendered (NCSES, 2021), this study assessed: 1) whether there were racial differences in adolescents' perceived math cost alongside expectancies and values and 2) the extent to which perceived math cost alongside expectancies and values explained yearly changes in achievement differences by race and gender. This study assessed 2,338 Black (39.4 %) and White (60.6 %) adolescents, roughly half girls (47.8 %), in the 6th to 12th grades (M = 14.71 years old, SD = 1.93, 61.7 % qualifying for free or reduced priced lunch). The results indicated that Black adolescents perceived higher costs to learning math than their White peers but value math in similar ways. Perceived math cost was the only motivational belief to explain achievement differences between Black and White girls but not boys after adjusting for socioeconomic status and grade level. In contrast, perceptions of ability beliefs explained achievement differences between Black boys and girls. These findings point to the importance of employing intersectional approaches to understand the relationship between math motivation and achievement.

With growing expectations to use AI-based educational technology (AI-EdTech) to improve students' learning outcomes and enrich teaching practice, teachers play a central role in the adoption of AI-EdTech in classrooms. Teachers' willingness to accept vulnerability by integrating technology into their everyday teaching practice, that is, their trust in AI-EdTech, will depend on how much they expect it to benefit them versus how many concerns it raises for them. In this study, we surveyed 508 K-12 teachers across six countries on four continents to understand which teacher characteristics shape teachers' trust in AI-EdTech, and its proposed antecedents, perceived benefits and concerns about AI-EdTech. We examined a comprehensive set of characteristics including demographic and professional characteristics (age, gender, subject, years of experience, etc.), cultural values (Hofstede's cultural dimensions), geographic locations (Brazil, Israel, Japan, Norway, Sweden, USA), and psychological factors (self-efficacy and understanding). Using multiple regression analysis, we found that teachers with higher AI-EdTech self-efficacy and AI understanding perceive more benefits, fewer concerns, and report more trust in AI-EdTech. We also found geographic and cultural differences in teachers' trust in AI-EdTech, but no demographic differences emerged based on their age, gender, or level of education. The findings provide a comprehensive, international account of factors associated with teachers' trust in AI-EdTech. Efforts to raise teachers' understanding of, and trust in AI-EdTech, while considering their cultural values are encouraged to support its adoption in K-12 education.

For serious games on education, understanding the effectiveness of different learning methods in influencing cognitive processes remains a significant challenge. In particular, limited research addresses the comparative effectiveness of serious games and videos in analyzing brain behavior for graph structure learning, which is an important part of the Science, Technology, Engineering, Math, and Computing (STEM+C) disciplinary education. This study investigates the impact of serious games on graph structure learning. For this, we compared our in-house game-based learning (GBL) and video-based learning (VBL) methodologies by evaluating their effectiveness on cognitive processes by oxygenated hemoglobin levels using functional near-infrared spectroscopy (fNIRS). We conducted a 2×1 between-subjects preliminary study with twelve participants, involving two conditions: game and video. Both groups received equivalent content related to the basic structure of a graph, with comparable session lengths. The game group interacted with a quiz-based game, while the video group watched a pre-recorded video. The fNIRS was employed to capture cerebral signals from the prefrontal cortex, and participants completed pre- and post-questionnaires capturing user experience and knowledge gain. In our study, we noted that the mean levels of oxygenated hemoglobin (ΔHbO) were higher in the GBL group, suggesting the potential enhanced cognitive involvement. Our results show that the lateral prefrontal cortex (LPFC) has greater hemodynamic activity during the learning period. Moreover, knowledge gain analysis showed an increase in mean score in the GBL group compared to the VBL group. Although we did not observe statistically significant changes due to participant variability and sample size, this preliminary work contributes to understanding how GBL and VBL impact cognitive processes, providing insights for enhanced instructional design and educational game development. Additionally, it emphasizes the necessity for further investigation into the impact of GBL on cognitive engagement and learning outcomes. © 2024 IEEE.

Over the past few decades, Piaget's forms of abstraction have proved productive for developing explanatory models of student and teacher knowledge, yet the broader applicability of his abstraction forms to mathematics education remains an open question. In this paper, we adopt the Piagetian forms of abstraction to accomplish two interrelated goals. Firstly, we analyze instructional tasks to develop hypothetical accounts of the abstractions that might occur during students' engagement with them. Secondly, we draw on middle- and secondary-grades classroom data to discuss the abstractions that occurred during the implementation of those instructional tasks. Because this paper represents an initial attempt at extending the applicability of Piagetian forms of abstraction, we close with potential implications of such use and possible avenues for future research. Most notably, we highlight the complexities involved in supporting abstraction through curriculum and instruction.

A typical classroom exercise in hydrogeology is to develop a conceptual model of a contaminated site, identify groundwater flow direction(s), and predict the location and mass of a contaminant plume. This requires knowledge of key hydrogeological concepts and is highly visuospatial in nature. Among multiple discrete spatial thinking skills identified by cognitive science, the combination of visual penetrative ability and working in multiple frames of reference were identified to significantly predict performance on a hydrogeology task and showed that together with hydrogeology knowledge, these spatial thinking skills account for 49% of the variability on task performance. Seventy-two hydrogeology practitioners and students with varying levels of expertise were administered multiple spatial thinking tests and an assessment of hydrogeology knowledge before completing a hydrogeology task that was developed for the study. Using spatial thinking and knowledge test scores as predictor variables, a hierarchical regression analysis was conducted with performance on the hydrogeology task as the outcome variable. The resulting model predicts that at low levels of hydrogeology knowledge, the identified spatial thinking skills account for more than a 25% difference on the hydrogeology task. This study provides empirical evidence that visual penetrative ability and working in multiple frames of reference are important skills in hydrogeology; thus, instructors are encouraged to recognize that underdeveloped spatial thinking skills could present hurdles for students and that targeted spatial thinking training may yield positive results for both weak and strong spatial thinkers.

As online learning becomes increasingly ubiquitous, a key challenge is maintaining learners' sustained attention. Using eye-tracking, together with observing and interviewing learners, we can characterize both 1) whether they are looking at their learning materials, and 2) whether they are thinking about them. Critically, eye-tracking only speaks to the first distinction, not the second. To overcome this limitation, we supplemented eye-tracking with an egocentric camera, a webcam, a retrospective recall, and mind-wandering probes to capture a 2x2 matrix of attentional/cognitive states. We then categorized N=101 learners' attentional/cognitive states while they completed a multimedia physics module. This meets two goals: 1) allowing basic research to understand the relationship between attentional/cognitive states and behavioral outcomes; and 2) facilitating applied research by generating rich ground truth for future use in training machine learning to categorize this 2x2 set of attentional states, for which eye-tracking is necessary, but not sufficient.