This exploratory paper highlights how problem-based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from Crystal Island: EcoJourneys, a collaborative game-based learning environment centred on supporting science inquiry. In Crystal Island: EcoJourneys, students work in teams of four, investigate the problem individually and then utilize a brainstorming board, an in-game PBL whiteboard that structured the collaborative inquiry process. The paper addresses a central question: how can PBL support the interpretation of the observed patterns in individual actions and collaborative interactions in the collaborative game-based learning environment? Drawing on a mixed method approach, we first analyzed students' pre- and post-test results to determine if there were learning gains. We then used principal component analysis (PCA) to describe the patterns in game interaction data and clustered students based on the PCA. Based on the pre- and post-test results and PCA clusters, we used interaction analysis to understand how collaborative interactions unfolded across selected groups. Results showed that students learned the targeted content after engaging with the game-based learning environment. Clusters based on the PCA revealed four main ways of engaging in the game-based learning environment: students engaged in low to moderate self-directed actions with (1) high and (2) moderate collaborative sense-making actions, (3) low self-directed with low collaborative sense-making actions and (4) high self-directed actions with low collaborative sense-making actions. Qualitative interaction analysis revealed that a key difference among four groups in each cluster was the nature of verbal student discourse: students in the low to moderate self-directed and high collaborative sense-making cluster actively initiated discussions and integrated information they learned to the problem, whereas students in the other clusters required more support. These findings have implications for designing adaptive support that responds to students' interactions with in-game activities.

We examined 6- to 9-year-olds' (N = 60, 35 girls, 34% White, 23% Hispanic, 2% Black/African American, 2% Asian/Asian American, 22% Mixed Ethnicity/Race, 17% Unavailable, collected April–September 2019 in Providence, RI, USA) first-person perspectives on their exploration of museum exhibits. We coded goal setting, goal completion, and behaviors that reflected changes to how goals were accomplished. Whether children played collaboratively related to how often they revised behaviors to accomplish goals (OR = 2.14). When asked to reflect on their play, older children related talk about goals with behavioral revisions, demonstrating that children develop the ability to reflect on their goals when they watch their behaviors change (OR = 1.23). We discuss how these results inform the development of metacognitive reflection on learning through exploration. © 2022 The Authors. Child Development © 2022 Society for Research in Child Development.

This study examined physics major retention to degree at two institutions with substantially different admissions selectivity. Two modes of leaving the physics major were examined: leaving college and changing to another major while staying in college. The risk of leaving college while still enrolled as a physics major was highest in the spring freshman semester. The changing major risk was substantially different between the two institutions. For the less selective institution, the students changed major at the highest rate in the fall sophomore semester. For the more selective institution, the risk of changing major was high through the first two years of college with highest risk in the fall freshman semester and the fall junior semester. Different features were important in predicting the two modes of leaving; these also differed between institutions. For the less selective institution, math readiness (being academically prepared to enroll in Calculus 1 in the fall freshman semester) was the most predictive feature for leaving the physics major while staying in college; high school GPA was the most important feature for predicting both leaving college and graduating with a physics degree. For the more selective institution, ACT composite scores were the only significant predictor of retention. The role of math readiness was dramatic at the less selective institution with 41% of students not math ready upon enrolling in college as physics majors; 59% of these students failed to enroll in the first required physics class.

Implicit learning refers to learning without conscious awareness of the content acquired. Theoretical frameworks of human cognition suggest that intuitions develop based on incomplete perceptions of regularity during implicit learning and, in turn, lead to the development of more explicit, consciously-accessible knowledge. Surprisingly, however, this putative information processing pathway (i.e., implicit learning. intuition. explicit knowledge) has yet to be empirically demonstrated. The present study investigated the relationship between implicit learning, intuitions, and explicit knowledge using a modified Serial Reaction Time Task. Results indicate that intuitions of implicitly-learned patterns emerge prior to the development of explicit knowledge. Moreover, intuition timing and accuracy were significantly associated with accuracy of explicit reports. We did not, however, find that stronger implicit learners developed more accurate intuitions. Our findings suggest a crucial role of intuition in the formation of explicit knowledge from implicit learning.

Analogy is a central component of human cognition. Analogical mapping of similarities between pieces of information present in our experiences supports cognitive and social development, classroom learning, and creative insights and innovation. To date, analogical mapping has primarily been studied within separate modalities of information (e.g., verbal analogies between words, visuo-spatial analogies between objects). However, human experience, in development and adulthood, includes highly variegated information (e.g., words, sounds, objects) received via multiple sensory and information-processing pathways (e.g., visual vs. auditory pathways). Whereas cross-modal correspondences (e.g., between pitch and height) have been observed, the correspondences were between individual items, rather than between relations. Thus, analogical mapping (characterized by second-order relations between relations) has not been directly tested as a basis for cross-modal correspondence. Here, we devised novel cross-modality analogical stimuli (lines-to-sounds, lines-to-words, words-to-sounds) that explicated second-order comparisons between relations. In four samples across three studies-participants demonstrated well-above-chance identification of cross-modal second-order relations, providing robust evidence of analogy across modalities. Further, performance across all analogy types was explained by a single factor, indicating a modality-general analogical ability (i.e., an analo-g factor). Analo-g explained performance overand-above fluid intelligence as well as verbal and spatial abilities, though a stronger relationship to verbal than visuo-spatial ability emerged, consistent with verbal/semantic contributions to analogy. The present data suggests novel questions about our ability to find/learn second-order relations among the diverse information sources that populate human experience, and about cross-modal human and AI analogical mapping in developmental, educational, and creative contexts.

IntroductionThe Framework for K-12 Science Education (the Framework) and the Next- Generation Science Standards (NGSS) define three dimensions of science: disciplinary core ideas, scientific and engineering practices, and crosscutting concepts and emphasize the integration of the three dimensions (3D) to reflect deep science understanding. The Framework also emphasizes the importance of using learning progressions (LPs) as roadmaps to guide assessment development. These assessments capable of measuring the integration of NGSS dimensions should probe the ability to explain phenomena and solve problems. This calls for the development of constructed response (CR) or open-ended assessments despite being expensive to score. Artificial intelligence (AI) technology such as machine learning (ML)-based approaches have been utilized to score and provide feedback on open-ended NGSS assessments aligned to LPs. ML approaches can use classifications resulting from holistic and analytic coding schemes for scoring short CR assessments. Analytic rubrics have been shown to be easier to evaluate for the validity of ML-based scores with respect to LP levels. However, a possible drawback of using analytic rubrics for NGSS-aligned CR assessments is the potential for oversimplification of integrated ideas. Here we describe how to deconstruct a 3D holistic rubric for CR assessments probing the levels of an NGSS-aligned LP for high school physical sciences. MethodsWe deconstruct this rubric into seven analytic categories to preserve the 3D nature of the rubric and its result scores and provide subsequent combinations of categories to LP levels. ResultsThe resulting analytic rubric had excellent human- human inter-rater reliability across seven categories (Cohen's kappa range 0.82-0.97). We found overall scores of responses using the combination of analytic rubric very closely agreed with scores assigned using a holistic rubric (99% agreement), suggesting the 3D natures of the rubric and scores were maintained. We found differing levels of agreement between ML models using analytic rubric scores and human-assigned scores. ML models for categories with a low number of positive cases displayed the lowest level of agreement. DiscussionWe discuss these differences in bin performance and discuss the implications and further applications for this rubric deconstruction approach.

Stronger metacognition, or awareness and regulation of thinking, is related to higher academic achievement. Most metacognition research has focused at the level of the individual learner. However, a few studies have shown that students working in small groups can stimulate metacognition in one another, leading to improved learning. Given the increased adoption of interactive group work in life science classrooms, there is a need to study the role of social metacognition, or the awareness and regulation of the thinking of others, in this context. Guided by the frameworks of social metacognition and evidence-based reasoning, we asked: 1) What metacognitive utterances (words, phrases, statements, or questions) do students use during small-group problem solving in an upper-division biology course? 2) Which metacognitive utterances are associated with small groups sharing higher-quality reasoning in an upper-division biology classroom? We used discourse analysis to examine transcripts from two groups of three students during breakout sessions. By coding for metacognition, we identified seven types of metacognitive utterances. By coding for reasoning, we uncovered four categories of metacognitive utterances associated with higher-quality reasoning. We offer suggestions for life science educators interested in promoting social metacognition during small-group problem solving.

A new parent-report measure was used to examine parents' person and process responses to children's math performance. Twice over a year from 2017 to 2020, American parents (N = 546; 80% mothers, 20% other caregivers; 62% white, 21% Black, 17% other) reported their responses and math beliefs; their children's (M-age = 7.48 years; 50% girls, 50% boys) math adjustment swas also assessed. Factor analyses indicated parents' person and process responses to children's math success and failure represent four distinct, albeit related, responses. Person (vs. process) responses were less common and less likely to accompany views of math ability as malleable and failure as constructive (|r|s = .16-.23). The more parents used person responses, the poorer children's later math adjustment (|beta|s = .06-.16).

This research examined parents' involvement in children's math homework and activities. During 2017 to 2019, American parents (N = 483; 80% mothers; 67% white) of young elementary school children (M-age = 7.47 years; 50% girls) reported on their math helping self-efficacy; they also reported on their involvement in children's math homework and activities daily for 12 days. At this time and a year later, children's math motivation and achievement were assessed. Parents' involvement in homework (vs. activities) was more affectively negative (d = .34), particularly among parents low in self-efficacy (d = .23). The more affectively negative parents' involvement, particularly in homework, the poorer children's later math motivation and achievement (beta s = -.09 to .20).