We present CPSCoach 2.0, an automated system that provides feedback, instructional scaffolding, and practice to help individuals improve three collaborative problem-solving (CPS) skills drawn from a theoretical CPS framework: construction of shared knowledge, negotiation/coordination, and maintaining team function. CPSCoach 2.0 was developed and tested in the context of computer-mediated collaboration (video conferencing) with an educational game. It automatically analyzes users' speech during a round of collaborative gameplay to provide personalized feedback and to select a target CPS skill for improvement. After multiple cycles of iterative testing and refinement, we tested CPSCoach 2.0 in a user study where 21 dyads (n = 42) completed four rounds of feedback and scaffolding embedded within five rounds of game-play in a single session. Using a quasi-experimental matching procedure, we found that the use of CPSCoach 2.0 was associated with improvement in CPS skill development compared to matched controls. Further, users found the automated feedback to be moderately accurate and had positive perceptions of the system, and these impressions were stronger for those who received higher scores overall. Results demonstrate the use of automated feedback and instructional scaffolds to support the development of CPS skills.

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 paper presents a novel spatiotemporal transformer network that introduces several original components to detect actions in untrimmed videos. First, the multi-feature selective semantic attention model calculates the correlations between spatial and motion features to model spatiotemporal interactions between different action semantics properly. Second, the motion-aware network encodes the locations of action semantics in video frames utilizing the motion-aware 2D positional encoding algorithm. Such a motion-aware mechanism memorizes the dynamic spatiotemporal variations in action frames that current methods cannot exploit. Third, the sequence-based temporal attention model captures the heterogeneous temporal dependencies in action frames. In contrast to standard temporal attention used in natural language processing, primarily aimed at finding similarities between linguistic words, the proposed sequence-based temporal attention is designed to determine both the differences and similarities between video frames that jointly define the meaning of actions. The proposed approach outperforms the state-of-the-art solutions on four spatiotemporal action datasets: AVA 2.2, AVA 2.1, UCF101-24, and EPIC-Kitchens.

The need to prepare students for the workplace, shortage of skilled labor, and fast-paced changes in the industry necessitate improvements in the pedagogical frameworks of educational communities. Practitioners are required to provide practical insights, rigor, and realism to complement academia pedagogic efforts in construction education. However, this is being plagued by several complexities. Leveraging advances in computational techniques, this paper presents the considerations of practitioners and instructors in workforce development collaborations as inputs for a graphical user interface of a technology-driven matching platform for connecting professional and educational communities. Practitioners' considerations are students and specific course-support related, while instructors' considerations are related to practitioner suitability, project, and company characteristics. The study contributes to human factors principles in user interface design as well as user-centered design principles by highlighting information requirements of a collaborative network of instructors and practitioners. The findings of this study also provide insights to enhance industry-academia collaborations.

Modern assessment demands, resulting from educational reform efforts, call for strengthening diagnostic testing capabilities to identify not only the understanding of expected learning goals but also related intermediate understandings that are steppingstones on pathways to learning goals. An accurate and nuanced way of interpreting assessment results will allow subsequent instructional actions to be targeted. An appropriate psychometric model is indispensable in this regard. In this study, we developed a new psychometric model, namely, the diagnostic facet status model (DFSM), which belongs to the general class of cognitive diagnostic models (CDM), but with two notable features: (1) it simultaneously models students’ target understanding (i.e., goal facet) and intermediate understanding (i.e., intermediate facet); and (2) it models every response option, rather than merely right or wrong responses, so that each incorrect response uniquely contributes to discovering students’ facet status. Given that some combination of goal and intermediate facets may be impossible due to facet hierarchical relationships, a regularized expectation–maximization algorithm (REM) was developed for model estimation. A log-penalty was imposed on the mixing proportions to encourage sparsity. As a result, those impermissible latent classes had estimated mixing proportions equal to 0. A heuristic algorithm was proposed to infer a facet map from the estimated permissible classes. A simulation study was conducted to evaluate the performance of REM to recover facet model parameters and to identify permissible latent classes. A real data analysis was provided to show the feasibility of the model. © The Author(s), under exclusive licence to The Psychometric Society 2024. corrected publication 2024.

In this article, the Funding information section was missing and should have read ‘The study is funded by IES R305D200015 and NSF EDU-CORE #2300382’. The original article has been corrected. © The Author(s), under exclusive licence to The Psychometric Society 2024.

A growing body of research suggests that comprehension of expository texts presented digitally is a challenging endeavor, particularly for children. Many reading interventions, both from traditional classroom settings and computer-based contexts, have focused on much needed strategy instruction but have simultaneously neglected a focus on motivation. Alternatively, game-based learning environments (GBLEs) have the potential to simultaneously address both motivation and strategy use. Currently, there are few available GBLEs that target expository text comprehension. For this reason, this study employed a quasi-experimental between-subjects media comparison design to examine the effects of Missions with Monty, a GBLE supporting metacomprehension for expository science texts, on reading comprehension and motivation. Fifth-grade students (N = 234) engaged with either Missions with Monty or a comparison, computer-based version of the program lacking gamified elements for a period of 6 weeks and were assessed on reading comprehension skills and five dimensions of reading motivation. Results indicated that students in the GBLE condition showed significantly greater improvements in reading comprehension (g = 0.56), intrinsic motivation for reading (g = 0.52), and curiosity (g = 1.11) than their comparison-condition peers. Moreover, effects of the intervention on reading motivation were independent of prior reading comprehension for each of the reading motivation dimensions except reading efficacy. These findings support the notion that GBLEs can be an effective tool to foster digital expository text comprehension, particularly for struggling and uninterested readers.

Parents are considered a major resource in children's numeracy development. The relative role of cognitive and motivational parenting practices, however, is unclear given that the two types of practices have largely been studied in isolation. The current study simultaneously estimated the contributions of several cognitive and motivational parenting practices hypothesized to be important, but which may have overlapping effects. To capture parents' cognitive practices, the level and structure (i.e., prompts vs. statements) of 529 American parents' (80% mothers; 65% White, 20% Black; 33% less than a bachelor's degree) numeracy talk was coded during a challenging numeracy activity. Parents' motivational practices were assessed by coding their autonomy support and control in the activity. Children's (M-age = 7.5 years; 49% girls) engagement of numeracy strategies was also coded. Multilevel minute-to-minute modeling predicting children's engagement from both cognitive and motivational parenting practices indicated that parents' cognitive practices, particularly advanced prompts, predicted children's subsequent engagement of numeracy strategies, which were often advanced. Parents' motivational practices, as reflected in their autonomy support (vs. control), also foreshadowed children's engagement. These effects of the two types of practices were independent of one another. Taken together, the findings are consistent with the idea that cognitive and motivational parenting practices provide distinct resources that can benefit children's math learning.

Overparenting—taking over and completing developmentally appropriate tasks for children—is pervasive and hurts children's motivation. Can overparenting in early childhood be reduced by simply framing tasks as learning opportunities? In Study 1 (N = 77; 62% female; 74% White; collected 4/2022), US parents of 4-to-5-year-olds reported taking over less on tasks they perceived as greater learning opportunities, which was most often the case on academic tasks. Studies 2 and 3 (N = 140; 67% female; 52% White; collected 7/2022–9/2023) showed that framing the everyday, non-academic task of getting dressed as a learning opportunity—whether big or small—reduced parents' taking over by nearly half (r = −.39). These findings suggest that highlighting learning opportunities helps parents give children more autonomy. © 2024 The Author(s). Child Development © 2024 Society for Research in Child Development.

Prior research suggests most students do not glean valid cues from provided visuals, resulting in reduced metacomprehension accuracy. Across 4 experiments, we explored how the presence of instructional visuals affects students' metacomprehension accuracy and cue-use for different types of metacognitive judgments. Undergraduates read texts on biology (Study 1a and b) or chemistry (Study 2 and 3) topics, made various judgments (test, explain, and draw) for each text, and completed comprehension tests. Students were randomly assigned to receive only texts (text-only condition) or texts with instructional visualizations (text-and-image condition). In Studies 1b, 2 and 3, students also reported the cues they used to make each judgment. Across the set of studies, instructional visualizations harmed relative metacomprehension accuracy. In Studies 1a and 2, this was especially the case when students were asked to judge how well they felt they could draw the processes described in the text. But in Study 3, this was especially the case when students were asked to judge how well they would do on a set of comprehension tests. In Studies 2 and 3, students who reported basing their judgments on representation-based cues demonstrated more accurate relative accuracy than students who reported using heuristic based cues. Further, across these studies, students reported using visual cues to make their draw judgments, but not their test or explain judgments. Taken together, these results indicate that instructional visualizations can hinder metacognitive judgment accuracy, particularly by influencing the types of cues students use to make judgments of their ability to draw key concepts.