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.

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high dimensional, sparse, and heterogeneous and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning different stages of the singlecell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.

Contextual multi-armed bandits have previously been used to personalize student support messages given to learners by supplying a model with relevant context about the user, problem, and available student supports. In this work, we propose using careful feature selection with relevant domain knowledge to improve the quality of student support recommendations. By providing Bayesian Knowledge Tracing mastery estimates to a contextual multi-armed bandit as user-level context in a simulated environment, we demonstrate that using domain knowledge to engineer contextual features results in higher average cumulative reward, and significant improvement over randomly selecting student supports. The data used to simulate sequential recommendations are available at https: //osf.io/sfyzv/? view_only=351fb8781d2c4f3bbc9d7486762d563a.

Learning experience designers consistently balance the trade-off between open and close-ended activities. The growth and scalability of Computer Based Learning Platforms (CBLPs) have only magnified the importance of these design trade-offs. CBLPs often utilize close-ended activities (i.e. Multiple-Choice Questions [MCQs]) due to feasibility constraints associated with the use of open-ended activities. MCQs offer certain affordances, such as immediate grading and the use of distractors, setting them apart from open-ended activities. Our current study examines the effectiveness of Fill-In problems as an alternative to MCQs for middle school mathematics. We report on a randomized study conducted from 2017 to 2022, with a total of 6,768 students from middle schools across the US. We observe that, on average, Fill-In problems lead to better post-test performance than MCQs; albeit deeper explorations indicate differences between the two design paradigms to be more nuanced. We find evidence that students with higher math knowledge benefit more from Fill-In problems than those with lower math knowledge.

Gaming the system, characterized by attempting to progress through a learning activity without engaging in essential learning behaviors, remains a persistent problem in computer-based learning platforms. This paper examines a simple intervention to mitigate the harmful effects of gaming the system by evaluating the impact of immediate feedback on students prone to gaming the system. Using a randomized controlled trial comparing two conditions - one with immediate hints and feedback and another with delayed access to such resources - this study employs a Fully Latent Principal Stratification model to determine whether students inclined to game the system would benefit more from the delayed hints and feedback. The results suggest differential effects on learning, indicating that students prone to gaming the system may benefit from restricted or delayed access to on-demand support. However, removing immediate hints and feedback did not fully alleviate the learning disadvantage associated with gaming the system. Additionally, this paper highlights the utility of combining detection methods and causal models to comprehend and effectively respond to students ' behaviors. Overall, these findings contribute to our understanding of effective intervention design that addresses gaming the system behaviors, consequently enhancing learning outcomes in computerbased learning platforms.

Numerous studies aim to enhance learning in digital environments through emotionally-sensitive interventions. The D'Mello and Graesser (2012) model of affect dynamics hypothesizes that when a learner encounters confusion, the degree to which it is prolonged (and transitions into frustration) or resolved, significantly affects their learning outcomes in digital environments. However, studies yield inconclusive results regarding relations between confusion, frustration, and learning. More research is needed to explore how confusion and frustration manifest during learning and its relation to outcomes. We go beyond past work looking at the rate, duration, and transitions of confusion and frustration by treating these affective states as non-linear dynamical systems consisting of expressive and behavioral components. We examined the frequency and recurrence of facial expressions associated with basic emotions (as automatically labeled by AffDex, a standard tool for analyzing emotions with video data) during confused and frustrated states (as automatically labeled with BROMP-based detectors applied to students' interaction data). We compare these co-occurring patterns to learning outcomes (pre-tests, post-tests, and learning gains) within a digital learning environment, Betty ' s Brain. Results showed that the frequency and recurrence rate of basic emotions expressed during confusion and frustration are complex and remain incompletely understood. Specifically, we show that confusion and frustration have different relationships with learning outcomes, depending on which basic emotion expressions they co-occur with. Implications of this study open avenues for better understanding these emotions as complex and non-linear dynamical systems, in the long-term enabling personalized feedback and emotional support within digital learning environments that enhance learning outcomes.

AI recommendations influence our daily decisions. The convenience of navigating personalized content goes hand-in-hand with the notorious filter bubble effect, which may decrease people's exposure to diverse options and opinions. Children are especially vulnerable to this due to their limited AI literacy and critical thinking skills. In this study, we propose a novel Augmented Reality (AR) application BeeTrap. It aims to not only raise children's awareness of filter bubbles but also empower them to mitigate this ethical issue through sense-making of AI recommendation systems' inner workings. By having children experience and break filter bubbles in a flower recommendation system, BeeTrap utilizes embodied metaphors (e.g., NEAR-FAR, ITERATION) and analogies (bee pollination) to bridge abstract AI concepts with sensory-motor experiences in familiar STEM contexts. To evaluate our design's effectiveness and accessibility for a broad range of children, we introduced BeeTrap in a four-day summer camp for middle-school students from underrepresented backgrounds in STEM. Results from pre- and post-tests and interviews show that BeeTrap developed students' technical understanding of AI recommendations, empowered them to break filter bubbles, and helped them foster new personal and societal perspectives around AI technologies.

This work investigates relationships between consistent attendance -attendance rates in a group that maintains the same tutor and students across the school year- and learning in small group tutoring sessions. We analyzed data from two large urban districts consisting of 206 9th-grade student groups (3 - 6 students per group) for a total of 803 students and 75 tutors. The students attended small group tutorials approximately every other day during the school year and completed a pre and post-assessment of math skills at the start and end of the year, respectively. First, we found that the attendance rates of the group predicted individual assessment scores better than the individual attendance rates of students comprising that group. Second, we found that groups with high consistent attendance had more frequent and diverse tutor and student talk centering around rich mathematical discussions. Whereas we emphasize that changing tutors or groups might be necessary, our findings suggest that consistently attending tutorial sessions as a group with the same tutor might lead the group to implicitly learn as a team despite not being one.

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.

Generative Artificial Intelligence (GenAI) systems have experienced exponential growth in the last couple of years. These systems offer exciting capabilities for CS Education (CSEd), such as generating programs, that students can well utilize for their learning. Among the many dimensions that might affect the effective adoption of GenAI for CSEd, in this paper, we investigate students' trust. Trust in GenAI influences the extent to which students adopt GenAI, in turn affecting their learning. In this paper, we present results from a survey of 253 students at two large universities to understand how much they trust GenAI tools and their feedback on how GenAI impacts their performance in CS courses. Our results show that students have different levels of trust in GenAI. We also observe different levels of confidence and motivation, highlighting the need for further understanding of factors impacting trust.