Teacher Perceptions of Learning Motivation and Classroom Behavior: The Role of Student Characteristics

A Hierarchical Bayesian Model of Adaptive Teaching

How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N = 312 adults). In Experiment 1, we show that teachers select examples that account for learners' background knowledge, and adjust their examples based on learners' feedback. In Experiment 2, we show that learners strategically provide more feedback when teachers' examples deviate from their background knowledge. These findings provide a foundation for extending computational accounts of pedagogy to richer interactive settings.

The longitudinal associations among student externalizing behaviors, teacher-student relationships, and classroom engagement

Personal characteristics and classroom environment features both play important roles in predicting students' levels of classroom engagement. The present study took a person-environment transaction perspective to investigate how factors at both the personal (i.e., student externalizing behaviors) and relational (i.e., teacher-student relationships) levels jointly predict the development of classroom engagement behaviors in a sample of 784 elementary school students. Using a longitudinal cross-lagged model spanning across Grade 3 to Grade 5, we found a negative reciprocal association between teacher-student relationships and externalizing behaviors, such that a more positive teacher-student relationship predicted fewer externalizing behaviors in the subsequent academic year, and fewer externalizing behaviors predicted a more positive teacher-student relationship 1 year later. In addition, externalizing behaviors directly negatively predicted subsequent classroom engagement, whereas teacher-student relationships indirectly predicted subsequent classroom engagement by way of externalizing behaviors. Overall, students with more externalizing behaviors experienced more conflicts with and received less support from their teachers, which predicted the development of more externalizing behaviors and lower subsequent classroom engagement.

Automated Student Classroom Behaviors' Perception and Identification Using Motion Sensors

With the rapid development of artificial intelligence technology, the exploration and application in the field of intelligent education has become a research hotspot of increasing concern. In the actual classroom scenarios, students' classroom behavior is an important factor that directly affects their learning performance. Specifically, students with poor self-management abilities, particularly specific developmental disorders, may face educational and academic difficulties owing to physical or psychological factors. Therefore, the intelligent perception and identification of school-aged children's classroom behaviors are extremely valuable and significant. The traditional method for identifying students' classroom behavior relies on statistical surveys conducted by teachers, which incurs problems such as being time-consuming, labor-intensive, privacy-violating, and an inaccurate manual intervention. To address the above-mentioned issues, we constructed a motion sensor-based intelligent system to realize the perception and identification of classroom behavior in the current study. For the acquired sensor signal, we proposed a Voting-Based Dynamic Time Warping algorithm (VB-DTW) in which a voting mechanism is used to compare the similarities between adjacent clips and extract valid action segments. Subsequent experiments have verified that effective signal segments can help improve the accuracy of behavior identification. Furthermore, upon combining with the classroom motion data acquisition system, through the powerful feature extraction ability of the deep learning algorithms, the effectiveness and feasibility are verified from the perspectives of the dimensional signal characteristics and time series separately so as to realize the accurate, non-invasive and intelligent children's behavior detection. To verify the feasibility of the proposed method, a self-constructed dataset (SCB-13) was collected. Thirteen participants were invited to perform 14 common class behaviors, wearing motion sensors whose data were recorded by a program. In SCB-13, the proposed method achieved 100% identification accuracy. Based on the proposed algorithms, it is possible to provide immediate feedback on students' classroom performance and help them improve their learning performance while providing an essential reference basis and data support for constructing an intelligent digital education platform.

The unique relevance of executive functions and self-regulation behaviors for understanding early childhood experiences and preschoolers' outcomes in rural Pakistan

Performance-based measures of children's executive functions (EFs) do not capture children's application of these skills during everyday emotionally-laden and socially-mediated interactions. The current study demonstrates the value of using assessor report of self-regulation behaviors (inhibitory control and positive affect/engagement) in addition to EF tasks when studying early childhood experiences and development in a rural lower-middle-income country setting. In a sample of 1302 disadvantaged 4-year-olds living in rural Pakistan, we found that directly assessed EFs were significantly related to assessor observations of children's inhibitory control and positive affect/engagement during a structured assessment protocol. However, EFs and two types of self-regulation behaviors demonstrated unique associations with children's (1) contextual experiences, as indexed by family socio-economic resources, participation in parenting interventions, and children's physical growth; and (2) age-salient developmental outcomes, as indexed by direct assessment of pre-academic skills and maternal report of prosocial behaviors and behavior problems. First, family wealth uniquely predicted only observed positive affect/engagement, whereas maternal education uniquely predicted only EFs. Second, children's antecedent linear growth was a significant predictor of both EFs and positive affect/engagement, but exposure to an enhanced nutrition intervention during the first 2 years of life and preschoolers' hair cortisol concentration were associated only with observed self-regulation behaviors. Finally, both EFs and observed positive affect/engagement uniquely predicted children's pre-academic skills. In contrast, only assessors' ratings of positive affect/engagement uniquely predicted maternal report of prosocial behaviors and only assessors' ratings of inhibitory control uniquely predicted maternal report of behavioral problems.

A model of classroom social climate, foreign language enjoyment, and student engagement among English as a foreign language learners

With the advent of positive psychology in second language (L2) learning, some researchers have undertaken empirical studies to explore emotional variables affecting L2 learning and how positive emotions can enhance the engagement of L2 learners. As an attempt to contribute to this research domain, this project sought to test a model of student engagement based on classroom social climate (CSC) and foreign language enjoyment (FLE) among English language learners in Iran. A sample of 386 intermediate English as a foreign language (EFL) learners took part in this survey by completing the online battery of questionnaires. Structural equation modeling (SEM) was employed for the analysis of the gathered data. The results showed that both CSC and FLE were significant predictors of student engagement, with FLE acting as a stronger predictor. Furthermore, CSC exerted a slight influence on FLE. The findings of the present study verify the contributions of positive psychology to L2 pedagogy, implying that pleasant perceptions of learning context and positive emotions can lead to further student engagement.

Language Ability Accounts for Ethnic Difference in Mathematics Achievement

The mathematics achievement of minority students has always been a focal point of educators in China. This study investigated the differences in mathematics achievement between Han and minority pupils to determine if there is any cognitive mechanism that can account for the discrepancy. We recruited 236 Han students and 272 minority students (including Uygur and Kazak) from the same primary schools. They were tested on mathematics achievement, language abilities, and general cognitive abilities. The results showed that Han pupils had better mathematics achievement scores and better Chinese language ability than minority students. After controlling for age, gender, and general cognitive abilities, there were still significant differences in mathematics achievement between Han and minority students. However, these differences disappeared after controlling for language ability. These results suggest that the relatively poor levels of mathematics achievement observed in minority students is related to poor Chinese language skills.

Die Qualität des Lehrkraftverhaltens in Lehrkraft-Kind-Dyaden

Zusammenfassung. Das Verhalten von Lehrkräften in Lehrkraft-Kind-Dyaden kann auf den beiden universalen orthogonalen Dimensionen interpersonalen Verhaltens beschrieben werden: Communion (z.B. Wärme, Bedürfnisbefriedigung) und Agency (z.B. Lenkung, Kontrolle). Bestimmte Kombinationen auf diesen Dimensionen haben sich als günstig erwiesen: Schülerinnen und Schüler lernen besonders motiviert und viel, wenn ihre Lehrkraft starke Communion zeigt. Weniger eindeutig ist, ob eine moderat starke oder starke Agency der Lehrkraft besonders vorteilhaft ist. Wir untersuchen unter Verwendung des neu konstruierten Fragebogens zum Lehrkraftverhalten in dyadischen Lehrkraft-Lernenden-Beziehungen, ob Lehrkräfte ihre Agency komplementär zur Kompetenz des Kindes ausrichten und ob sich dies auch in stärkerer Agency gegenüber Gruppen von Kindern niederschlägt, deren mittlere Kompetenz geringer ist, nämlich (a) Kindern mit sonderpädagogischen Förderbedarf, (b) Kindern mit nichtdeutscher Erstsprache und (c) Jungen. Zweiundsiebzig Lehrkräfte beschrieben ihr Verhalten gegenüber fünf Kindern ihrer Klasse ( N = 302) auf jeweils 13 Items, die in einem Zirkumplex acht Facetten unterschiedlicher Kombinationen von Communion und Agency erfassen. Noten wurden als Proxy für Kompetenzen genutzt. Wie erwartet korrelierten Facetten mit sehr starker Agency positiv ( r = .54 und .65) und Facetten mit sehr schwacher Agency negativ ( r = –.46 und –.59) mit den Noten des Kindes. Nach Aggregation der Angaben über die Gruppen von Kindern zeigte sich, dass Lehrkräfte gegenüber Kindern mit sonderpädagogischem Förderbedarf oder mit nichtdeutscher Erstsprache auf Verhaltensfacetten mit starker Agency höhere Ausprägungen aufwiesen als gegenüber Kindern ohne das entsprechende Merkmal, unabhängig von der Stärke der Communion der Facetten. Diese Unterschiede verschwanden meist nach Kontrolle der Noten. Gegenüber Jungen (relativ zu Mädchen) gaben die Lehrkräfte höhere Ausprägungen auf Facetten mit (moderat) starker Agency bei gleichzeitig nur (moderat) schwacher Communion an, auch nach der Berücksichtigung der Noten. Die Ergebnisse werden bzgl. der Forschung (a) zum Zusammenhang zwischen Lehrkraftverhalten und Motivation der Lernenden und (b) zu den Ursachen des geringeren Bildungserfolgs von Jungen diskutiert.

Professors' Expectations About Online Education and Its Relationship With Characteristics of University Entrance and Students' Academic Performance During the COVID-19 Pandemic

Due to COVID-19, universities have been facing challenges in generating the best possible experience for students with online academic training programs. To analyze professors' expectations about online education and relate them to student academic performance during the COVID-19 pandemic, and considering the socio-demographic, entry, and prior university performance variables of students. A prospective longitudinal design was used to analyze the expectations of 546 professors (54.8% male) in T1. In T2, the impact of the expectations of 382 of these professors (57.6% men) was analyzed, who taught courses during the first semester to a total of 14,838 university students (44.6% men). Professors' expectations and their previous experience of online courses were obtained during T1, and the students' academic information was obtained in T2. A questionnaire examining the Expectations toward Virtual Education in Higher Education for Professors was used. 84.9% of the professors were considered to have moderate to high skills for online courses. Differences in expectations were found according to the professors' training level. The professors' self-efficacy for online education, institutional engagement, and academic planning had the highest scores. The expectations of professors did not directly change the academic performance of students; however, a moderating effect of professor's expectations was identified in the previous student academic performance relationship on their current academic performance.

Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic?

The U.S. has taken multiple measures to contain the spread of COVID-19, including the implementation of lockdown orders and social distancing practices. Evaluating social distancing is critical since it reflects the risk of close human interactions. While questionnaire surveys or mobility data-based systems have provided valuable insights, social media data can contribute as an additional instrument to help monitor the risk of human interactions during the pandemic. For this reason, this study introduced a social media-based approach that quantifies the pro/anti-lockdown ratio as an indicator of the risk of human interactions. With the aid of natural language processing and machine learning techniques, this study classified the lockdown-related tweets and quantified the pro/anti-lockdown ratio for each state over time. The anti-lockdown ratio showed a moderate and negative correlation with the state-level social distancing index on a weekly basis, suggesting that people are more likely to travel out of the state where the higher anti-lockdown level is observed. The study further showed that the perception expressed on social media could reflect people's behaviors. The findings of the study are of significance for government agencies to assess the risk of close human interactions and to evaluate their policy effectiveness in the context of social distancing and lockdown.