Affect and metacognition play a central role in learning. We examine the relationships between students' affective state dynamics, metacognitive judgments, and performance during learning with MetaTutorIVH, an advanced learning technology for human biology education. Student emotions were tracked using facial expression recognition embedded within MetaTutorIVH and transitions between emotions theorized to be important to learning (e.g., confusion, frustration, and joy) are analyzed with respect to likelihood of occurrence. Transitions from confusion to frustration were observed at a significantly high likelihood, although no differences in performance were observed in the presence of these affective states and transitions. Results suggest that the occurrence of emotions have a significant impact on students' retrospective confidence judgments, which they made after submitting their answers to multiple-choice questions. Specifically, the presence of confusion and joy during learning had a positive impact on student confidence in their performance while the presence offrustration and transition from confusion to frustration had a negative impact on confidence, even after accounting for individual differences in multiple-choice confidence.

Learning by drawing can be an effective strategy for supporting science text comprehension. However, drawing can also be cognitively demanding and time consuming, and students may not create quality drawings without sufficient guidance. Furthermore, evidence for drawing is often based on comparisons to weak control conditions, such as students who only read the text without provided illustrations. In this review, we synthesize past research to help draw boundary conditions for learning by drawing, focusing on the role of comparison conditions and drawing guidance. First, we analyze how drawing compares to each of four control conditions: reading only, text-focused strategies (e.g., summarizing), other model-focused strategies (e.g., imagining), or viewing instructor-provided illustrations. Next, we distinguish among four levels of drawing guidance: minimal guidance, drawing training, partially provided illustrations, and comparison to instructor-provided illustrations. Our findings indicate that when compared to only reading the text or using text-focused strategies, creating drawings is consistently more effective at fostering comprehension and transfer, regardless of the level of drawing guidance provided. However, when compared to other model-focused strategies or to viewing instructor-provided illustrations, effects of creating drawings are mixed and may depend on the level of drawing guidance provided, among other factors. We discuss the theoretical and practical considerations of our findings and suggest several directions for broadening research on drawing.

Social isolation is broadly associated with poor mental health and risky behaviors in adolescence, a time when peers are critical for healthy development. However, expectations for isolates' substance use remain unclear. Isolation in adolescence may signal deviant attitudes or spur self-medication, resulting in higher substance use. Conversely, isolates may lack access to substances, leading to lower use. Although treated as a homogeneous social condition for teens in much research, isolation represents a multifaceted experience with structurally distinct network components that present different risks for substance use. This study decomposes isolation into conceptually distinct dimensions that are then interacted to create a systematic typology of isolation subtypes representing different positions in the social space of the school. Each isolated position's association with cigarette, alcohol, and marijuana use is tested among 9(th) grade students (n = 10,310, 59% female, 83% white) using cross-sectional data from the PROSPER study. Different dimensions of isolation relate to substance use in distinct ways: unliked isolation is associated with lower alcohol use, whereas disengagement and outside orientation are linked to higher use of all three substances. Specifically, disengagement presents risks for cigarette and marijuana use among boys, and outside orientation is associated with cigarette use for girls. Overall, the adolescents disengaged from their school network who also identify close friends outside their grade are at greatest risk for substance use. This study indicates the importance of considering the distinct social positions of isolation to understand risks for both substance use and social isolation in adolescence.

Author name ambiguity in a digital library may affect the findings of research that mines authorship data of the library. This study evaluates author name disambiguation in DBLP, a widely used but insufficiently evaluated digital library for its disambiguation performance. In doing so, this study takes a triangulation approach that author name disambiguation for a digital library can be better evaluated when its performance is assessed on multiple labeled datasets with comparison to baselines. Tested on three types of labeled data containing 5000 to 6 M disambiguated names, DBLP is shown to assign author names quite accurately to distinct authors, resulting in pairwise precision, recall, and F1 measures around 0.90 or above overall. DBLP's author name disambiguation performs well even on large ambiguous name blocks but deficiently on distinguishing authors with the same names. Compared to other disambiguation algorithms, DBLP's disambiguation performance is quite competitive, possibly due to its hybrid disambiguation approach combining algorithmic disambiguation and manual error correction. A discussion follows on strengths and weaknesses of labeled datasets used in this study for future efforts to evaluate author name disambiguation on a digital library scale.

In supervised machine learning for author name disambiguation, negative training data are often dominantly larger than positive training data. This paper examines how the ratios of negative to positive training data can affect the performance of machine learning algorithms to disambiguate author names in bibliographic records. On multiple labeled datasets, three classifiers-Logistic Regression, Naive Bayes, and Random Forest-are trained through representative features such as coauthor names, and title words extracted from the same training data but with various positive-to-negative training data ratios. Results show that increasing negative training data can improve disambiguation performance but with a few percent of performance gains and sometimes degrade it. Logistic and Naive Bayes learn optimal disambiguation models even with a base ratio (1:1) of positive and negative training data. Also, the performance improvement by Random Forest tends to quickly saturate roughly after 1:10 similar to 1:15. These findings imply that contrary to the common practice using all training data, name disambiguation algorithms can be trained using part of negative training data without degrading much disambiguation performance while increasing computational efficiency. This study calls for more attention from author name disambiguation scholars to methods for machine learning from imbalanced data.

Initial research has shown that simulating data from models created with computer software may enhance students' understanding of concepts in introductory statistics; yet, there is little research investigating students' development of statistical models. The research presented here examines small groups of students as they develop a model for a situation where a music teacher plays ten notes for a student who tries to guess each of the notes correctly. As students constructed their models and described their thinking, their descriptions were narrative in nature, focusing on the story of notes played and guessed. In this context, their focus on narrative appeared to support the development of productive statistical models. In addition, when students investigated pre-built TinkerPlots models, they preferred models that they perceived as more communicative or narrative in nature. These results have important pedagogical implications in terms of designing modeling curriculum.

Real estate property value analysis is used for municipal taxation and budgeting. Commercial properties make up a large percentage of the property tax base in many, if not most, taxing jurisdictions. Data constraints limit the number of analyses conducted on commercial property value patterns. This study employs a fairly extensive data set to address that problem in the context of El Paso, Texas, a large metropolitan economy located on the United States border with Mexico. The sample contains data for 105,611 commercial real estate parcels. Empirical analysis is conducted using geographically weighted regression analysis. Results confirm that parameter estimation for the commercial property data in this sample should be conducted using methodologies that allow for spatial autocorrelation and heteroscedasticity.

The reciprocal relations of motivation with affective, behavioral, and cognitive engagement were tested. Engagement, conceptualized as processes that indicate productive participation in learning activities, was measured using the Activity Engagement Survey with students participating in a variety of activities in both schools and a museum. The multifaceted nature of engagement and the consistency of this structure across contexts and activities was examined over six different science activities on six different days within classrooms (Study 1, sixth graders from 10 different schools) and over two different science museum exhibits in one day (Study 2, fifth graders). These age groups were chosen because they are a pivotal time in science motivation. A series of confirmatory factor analyses were conducted to investigate the nature of affective, behavioral, cognitive, and overall engagement. A bifactor model with both affective and combined behavioral-cognitive factors along with an overall engagement factor had the best fit across all eight activities. Reciprocal relations between motivation (measured at Time 1 and Time 3) and engagement (at Time 2) were tested using Structural Equation Modeling. Results indicate that, in school settings (Study 1), self-efficacy was negatively related and mastery goals were positively related to affective engagement, whereas overall engagement predicted all forms of motivation. In the museum exhibits (Study 2), self-efficacy was positively related to overall engagement and performance-approach goal orientations were positively related to behavioral-cognitive engagement.

In this commentary, we examine the papers in a special issue on Developments and Trends in Learning with Instructional Video. In particular, we focus on basic findings concerning which instructional features improve learning with instructional video (i.e., breaking the lesson into segments paced by the learner; recording from both first- and third-person perspectives) and which features or learner attributes do not (i.e., matching the instructor's gender to the learner's gender; having the instructor's face on the screen; adding practice without feedback; inserting pauses throughout the video; and spatial ability). In addition, we offer recommendations for future work on designing effective video lessons.

Research is needed to understand how to best design video lectures that foster learning. We tested whether instructor presence is better afforded through methods that increase students' access to the instructor's eye gaze, thereby enhancing learning through increased social agency. Specifically, we compared the eye-gaze behavior of college students who viewed an organic chemistry video lecture with the instructor using either a conventional whiteboard or a transparent whiteboard. These lecture methods differ in the degree to which they allow students to view the instructor's eye gaze. Using eye-tracking methods, we compared students' attention to the instructor's head during direct gaze events (i.e., when the instructor looked into the camera) and gaze guidance events (i.e., when the instructor looked at the whiteboard), and to the written and drawn information on the whiteboard. Results show that students who viewed a transparent whiteboard lecture attended more to the instructor and less to the material drawn on the board than students who viewed a conventional whiteboard lecture. The transparent group also performed equivalently to the conventional group on learning performance. Overall, this study demonstrates that the instructor's presence can compete with words and visuals drawn on the board by the instructor for students' attention.