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How do teachers perceive and experience the impact of evaluation feedback across various domains of their work? This study investigates whether there are qualitatively distinct groups of teachers in the United States based on teacher perceived impact of evaluation feedback using the 2013 Teaching and Learning International Survey (TALIS 2013). Applying latent class analysis to the U.S. sample of lower-secondary school teachers (n=1,824), we find that there are four distinctive groups of teachers: Receptive (25%), Compliant (15%), Motivated (27%), and Indifferent (33%). Our analysis shows feedback sources and teacher beliefs about the evaluation's purpose are significant predictors of teacher class assignment. Finally, controlling for various individual and school-level characteristics, these teacher groups vary significantly in their intention to change school.

Relational goals newly added to the teachers' goal orientation framework have been identified as one of the central features of effective teaching. However, little research has taken such goals into account, specifically in higher education contexts. Concerning the association between self-efficacy and goal orientations, this study was undertaken to investigate the predictive power of instructors' relational goals over teaching-related outcomes with teaching self-efficacy as a mediator. Three hundred thirty-eight Iranian EFL university instructors participated in the study. Their relational goals and teaching-related cognitive, affective, and behavioral outcomes were assessed through four scales (Instructors' Achievement Goals for Teaching, Attitudes towards Help-Seeking, Positive Affect, and SEEQ (Students' Evaluations of Educational Quality) Self-Reported Teaching Quality). Through a Structural Equation Modeling method, the proposed model was tested. The results confirmed the mediating effect of teaching self-efficacy on the relationship between instructors' relational goals and their teaching-related outcomes. Relational goal-oriented instructors with teaching self-efficacy viewed help-seeking (cognitive outcomes) as a beneficial performance rather than a threat. Regarding their positive affect (affective outcomes), the results indicated instructors' great enthusiasm for more effective teaching. Considering instructors' self-reported teaching quality (behavioral outcomes), their engagement and commitment in teaching were shown. In conclusion, the results emphasized the importance of instructors' relational goals in establishing high-quality learning and teaching environments.

The e-learning domain has witnessed a shift from the traditional behavioral approach to an individual centered learning approach based on learning analytics, with the aim of creating personalized and learner sensitive designs. A systematic literature review of 284 articles published between 2011 and 2022 in 133 different journals was conducted to investigate this trend. Bibliometric analyses were performed. The results of the analysis show that there is an increasing trend towards the implementation of learning analytics and the use of these analytics for personalized learning environments. The results also show that the output levels of learning analytics have progressed from description and diagnosis to prediction and perspective building. This has the potential to improve the fields of learning analytics and personal learning environments.

The main purpose of this study is to investigate the impact of English as a Foreign Language (EFL) teachers' instructional innovativeness on student engagement, with specific reference to behavioral and emotional aspects of engagement as perceived by the teachers. Given the ever-changing demands of contemporary education, this study underscores the importance of innovativeness and its influence on student engagement in L2 learning and teaching. The study adopted a cross-sectional design, utilizing a predictive moderated model, also assessing whether various teacher background variables such as educational status, gender, and field of graduation moderate the relationship between instructional innovativeness and student engagement. Data from 144 EFL teachers across Turkiye underwent analysis through partial least squares structural equation modeling (PLS-SEM). It was shown that instructional innovativeness significantly predicts both behavioral and emotional student engagement. However, only educational status, particularly differences between BA and PhD degrees, was found to moderate the impact on behavioral engagement, with no significant moderating effects identified for emotional engagement. In conclusion, this study highlights the merit of cultivating and fostering a culture of innovation among ELT practitioners to benefit students.

Grit, the passion for achieving long-term goals, has been conceived as a two-dimensional construct (Consistency of interest and Perseverance of effort). The construct is well known for its easy measurement and its relationship with performance, including academic performance. However, there have been different criticisms, such as the overlap of grit with other personality characteristics, the variability in the psychometric structure of the Grit-O and Grit-S tests, and the fact that some work reports a weak influence of grit on academic performance. Within this framework, this study contrasts different psychometric structures of the Grit-S scale and its relationship with the academic performance of higher education students. So, with this purpose, a dichotomous model of high and low grit, a K-medias clustering model, and three structural equation models have been tested. The results indicate that (a) there is a statistically significant relationship between grit--mainly determined by the consistency dimension--and academic performance, although it decreases when controlling for contextual variables, and (b) Consistency mediates the relationship between Perseverance and academic performance. The instability of grit to predict performance is discussed, and the thesis of a two-sub-dimensional structure is supported.

The study aimed to assess the effectiveness of the Predict-Explain-Enact-Observe-Reflect (PEEOR) instructional strategy on general science students' conceptual understanding and motivation in the topic of motion and force. The research employed a pre-test post-test quasi-experimental design. The sample consisted of 107 general science summer, year II students from Woldia College of Teacher Education. Data collection involved a conceptual understanding test (comprising 15 questions) and a post-motivation questionnaire. The KR-20 reliability value for the conceptual understanding test was 0.75. The Cronbach Alpha (a) reliability coefficient for the scale was determined to be 0.84. Descriptive statistics and ANCOVA revealed the following: The PEEOR group (adjusted mean = 6.787) significantly outperformed the traditional group (adjusted mean = 5.134). The POE group (adjusted mean = 6.472) did not differ significantly from either PEEOR or the traditional group. Regarding student motivation: There was a statistically significant difference (p < 0.05) in post-test motivational questionnaire (PMQ) scores among the three groups. The F-statistic for this difference was 4.753. The study recommends that college physics teachers adopt the PEEOR instruction strategy. Additionally, the college and department heads of natural science should promote the potential benefits of this novel strategy among science educators.

Course-based undergraduate research experiences (CUREs) provide opportunities for undergraduate students to engage in authentic research and generally increase the participation rate of students in research. Students' participation in research has a positive impact on their science identity and self-efficacy, both of which can predict integration of students in Science, Technology, Engineering, and Math (STEM), especially for underrepresented students. The main goal of this study was to investigate instructor-initiated CUREs implemented as upper-level elective courses in the Biomedical Sciences major. We hypothesized that these CUREs would (i) have a positive impact on students' scientific identity and self-efficacy and (ii) result in gains in students' self-assessed skills in laboratory science, research, and science communication. We used Likert-type surveys developed by Estrada et al. (14) under the Tripartite Integration Model of Social Influence to measure scientific identity, self-efficacy, and scientific value orientation. When data from all CUREs were combined, our results indicate that students' self-efficacy and science identity significantly increased after completion. Students' self-assessment of research and lab-related skills was significantly higher after completion of the CUREs. We also observed that prior to participation in the CUREs, students' self-assessment of molecular and bioinformatic skills was low, when compared with microbiological skills. This may indicate strengths and gaps in our curriculum that could be explored further.

This study focuses on evaluating the quality of competency transfer through various assessment methods and results, considering diverse stakeholder perspectives. The research aims to introduce an innovative approach for validating assessment outcomes, leveraging predicted sub-measurements, and transforming Boolean parameters' symbols into a binary coding system. This transformation simplifies the validation process by employing logical equations. The study's sample involves the adaptation of a competency transfer model, which combines internal parameters with the novel logical assessment method. The research findings indicate that the binary 2x system effectively simplifies quantitative and qualitative data representation within the validation process. This system facilitates the early detection of potentially ambiguous results, enabling the creation of validation procedures grounded in organizational cultural dimensions, outcomes, reports, and assessments. The proposed Quality Assessment Model (QAM) serves as a powerful tool for prediction, enhancing the quality of both quantitative and qualitative data outcomes. This approach generates distinct values, precise predictive measurements, and valuable result quality suitable for informed decision-making in various contexts. Ultimately, the study contributes to the advancement of assessment methodologies, enabling stakeholders to make more accurate and reliable judgments based on the quality of competency transfer.

This longitudinal, mixed-methods study reports on the development and implementation of an early phonological awareness screening and intervention program for struggling emergent readers in a French immersion school in Manitoba. The program was created by a professional learning community made up of the school administrator, teachers, and clinical support staff. This paper describes the process of developing the phonological awareness program and the intervention itself. Forty-two children participated in the phonological awareness intervention that lasted 10 weeks. The intervention was given in English in kindergarten. Significant gains were found in the phonological awareness skills of the children who participated in the intervention. Results also indicated that children's phonological awareness skills in English predicted their French reading levels in Grade 1. In addition, we provide insight into the roles played by key members of the school's PLC through qualitative analysis of a series of semi-structured interviews. The work of the school's professional learning community offers a model that may be implemented by other school teams to promote equity and accessibility for all learners in FI programs.

The present study employs regulatory focus theory (Higgins, 1997) to investigate the effects of L2 speakers' chronic regulatory focus on their L2 pragmatic versus grammatical awareness. It involved the participation of 121 Chinese students, who are English language learners, at a university in the United States. Haws et al.'s (2010) questionnaire was used to examine the participants' regulatory dispositions, and a judgment task was adapted from Bardovi-Harlig and Dörnyei (1998) to assess participants' awareness of grammatical and pragmatic errors, as well as the severity of each type of error. Multiple regression results largely confirmed the predictions. Prevention regulatory focus, concerned with obligations, responsibilities, and negative outcomes, predicted L2 speakers' recognition of grammatical errors and their severity. Conversely, promotion focus, which emphasizes growth, accomplishments, and positive outcomes, positively predicted L2 speakers' perceptions of pragmatic error severity. These findings contribute to the understanding of how differences in chronic motivational orientations can lead to different L2 use patterns and characteristics.

Adopting a sequential mixed-methods approach, the current inquiry examined English major students' perceptions of the role of teacher confirmation and teacher credibility in enhancing their academic engagement in the Chinese context. In doing so, through WeChat messenger, three scales were provided to 1168 English major students chosen from different English as a foreign language (EFL) classes. For the sake of triangulation, 40 participants were invited to take part in interview sessions as well. The inspection of the correlations between the constructs indicated a strong association between student academic engagement and teacher confirmation as well as a close connection between student academic engagement and teacher credibility. This showed that the academic engagement of Chinese EFL students is tied to these teacher interpersonal behaviors. The contribution of teacher confirmation and credibility to Chinese EFL students' academic engagement was also examined using path analysis, which demonstrated that Chinese EFL students' academic engagement was predicted by teacher credibility and confirmation. Additionally, the interview outcomes proved the integral role of these two communication behaviors in increasing Chinese students' engagement. Findings may have some noteworthy implications for teacher educators and language instructors.

This paper argues that engaging students in informal statistical reasoning from early school years is essential for the development of statistical understanding. We investigated if and how children aged six-seven years old identified variation in a table of data and made predictions through the design of a teaching experiment. The classroom teaching experiment was comprised of four 45 minutes lessons addressing the understanding and interpretation of data sets. In order to describe students' informal predictive reasoning, we used the framework of "data lenses". More specifically, we analyzed the different types of answers the students produced as they engaged in predictive reasoning during an interview given before and after the teaching experiment. The participation of students in (classroom) and out-of-school (family) communities of practice was also taken into consideration. Our results demonstrate that the students benefited from their learning experience and developed data understanding.

Although the issue of learning transfer has been given much attention in English-for-academic-purposes (EAP) research, the factor of transfer climate could be investigated further. The current study examines what the instructors of EAP perceive can be done to prepare students for the transfer climate. The transfer climate refers to the nature of the target context of instruction and the support for learning transfer perceived by a learner in that target context (Burke & Baldwin, 1999). The target context in EAP education is the discipline courses that students take alongside or after EAP courses. The transfer climate may be supportive or unsupportive depending on whether such discipline courses accommodate for students' transfer of EAP skills. Data was gathered through semi-structured interviews with 22 EAP instructors and, subsequently, three of them were observed to see what actually happens in their EAP courses (Borg, 2015). The findings outline eight steps that can be taken to prepare students for the transfer climate, seven within EAP courses, and one within discipline courses. From what was observed, few of the steps that participants mentioned in the interviews were actually implemented in the classes. Both practical implications and implications for future research are outlined.

Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first student pass-fail prediction approach proposed by Van Petegem and colleagues (2022) in a different CS1 course. Using student submission and self-report data, we investigated the reproducibility of the original approach, the effect of adding self-reports to the model, and the interpretability of the model features. The results showed that the original approach for student dropout prediction could be successfully reproduced in a different course context and that adding self-report data to the prediction model improved accuracy for the first four weeks. Wealso identified relevant features associated with dropout in the CS1 course, such as timely submission of tasks and iterative problem solving. When analyzing student behaviour, submission data and self-report data were found to complement each other. The results highlight the importance of transparency and generalizability in learning analytics and the need for future research to identify other factors beyond self-reported aptitude measures and student behaviour that can enhance dropout prediction.

Science identity, encompassing perceptions of competence, interest, and recognition in science, alongside learning self-efficacy reflecting confidence to master science skills, are key drivers of outcomes. However, developmental patterns likely vary across contexts. Participants were 512 Chinese students spanning grades 1-3 who completed the Science Identity Scale and Science Learning Self-Efficacy Scale, with physics, chemistry, and biology achievement scores gathered. A Partial Least Squares Structural Equation Model assessed relationships. Results substantiated psychometrics for motivational measures. The model indirectly predicting achievement via first-order discipline-specific paths explained more variance than direct second-order effects. Effects significantly varied across groups stratified by grade and region. Interest and conceptual knowledge drove physics and chemistry success, while higher-order skills enhanced biology achievement overall. However, relationships differed within subgroups, suggesting personalized motivational support needs--self-belief/competence foundations for struggling learners, conceptual development for those with high prior achievement, and intrinsic enrichment for disinterested students. Results detail complex motivational profiles underlying science achievement requiring tailored identity safety and self-efficacy scaffolding alongside conceptual and skill-building for excellence across scientific disciplines. Motivational support systems may spur more equitable and optimal science outcomes among diverse adolescent learners.

In education, understanding the determinants of learners' achievement is crucial. This study aimed to explore the correlation between emotional intelligence (EI) and academic achievement in primary schools. Using Goleman's Emotional Intelligence Assessment Scale adapted for local use, 444 primary school students were assessed via random sampling. Inferential statistical methods, especially multinomial logistic regression, were employed for data analysis. The research revealed that self-regulation, self-awareness, empathy, and motivation significantly influenced academic outcomes compared to high-achieving peer. Additionally, demographic factors played a pertinent role in students' performance. The study's implications highlight the necessity of interventions to nurture EI in students. Recommendations include revising educational policies to cultivate resilient learners, raising awareness among educators, parents, and implementing strategies to enhance children's EI, emphasizing self-regulation, self-awareness, empathy, and motivation. These findings underscore the importance of EI in academic achievement and provide valuable insights for educational practitioners and parents, emphasizing the need for targeted interventions to support children's holistic development.

The article is in the second phase of research is about "the big data architecture for pre-teacher preparation supply chain with prescriptive analytics of higher education in Thailand". The objectives of the study were (1) to study the pre-teacher preparation supply chain in Thailand, (2) to develop a model the big data system for the pre-teacher preparation supply chain management with prescriptive analytics of higher education in Thailand, (3) to design the big data architecture for the pre-teacher preparation supply chain management with prescriptive analytics of higher education in Thailand, (4) to develop the big data system for the pre-teacher preparation supply chain prescriptive of higher education in Thailand, (5) to assess accuracy of the predictive analytics in the pre-teachers needs of higher education in Thailand, and (6) to assess accuracy of the prescriptive analytics in the pre-teacher preparation of higher education in Thailand. In the study, the research procedures were divided into 6 steps according to the objectives, and all steps were assessed on the system suitability by 25 experts throughout the procedures.

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, current metrics primarily focus on predictive performance comparisons between groups, without considering the behavior of the models or the severity of the biases in the outcomes. To address this gap, we proposed a novel metric in a previous work (Verger et al., 2023) named "Model Absolute Density Distance" (MADD), measuring algorithmic unfairness as the difference of the probability distributions of the model's outcomes. In this paper, we extended our previous work with two major additions. Firstly, we provided theoretical and practical considerations on a hyperparameter of MADD, named "bandwidth," useful for optimal measurement of fairness with this metric. Secondly, we demonstrated how MADD can be used not only to measure unfairness but also to mitigate it through postprocessing of the model's outcomes while preserving its accuracy. We experimented with our approach on the same task of predicting student success in online courses as our previous work, and obtained successful results. To facilitate replication and future usages of MADD in different contexts, we developed an open-source Python package called maddlib (https://pypi.org/project/maddlib/). Altogether, our work contributes to advancing the research on fair student models in education.

The rapid development of education informatization has accumulated a large amount of data for learning analytics, and adopting educational data mining to find new patterns of data, develop new algorithms and models, and apply known predictive models to the teaching system to improve learning is the challenge and vision of the education field in the era of big data. Learning intervention, as a core concept of learning analytics, refers to the purposeful and planned adoption of direct or indirect strategies or behaviors based on tracking learning behaviors and integrating information about learners' characteristics to give learners personalized guidance and assistance in order to help learners break through the status quo of learning difficulties and improve their learning abilities, so as to achieve tailored teaching. In this study, data mining was conducted on the performance records of students on math problems in an online learning system, and a learning intervention suggestion prediction model was constructed on the basis of decision tree algorithm using Python, with a view to understanding the effectiveness, willingness, style, and other characteristics of the learners' online learning through the analysis results, providing personalized guidance to students, and enabling teachers to intervene with at-risk students and successfully complete the teaching goals. It was found that the most significant impact of the learning intervention advice provided to learners was the number of hints they sought during the learning process, and that learners who needed to be "intervened" or "monitored" could be categorized into two groups: independent inefficient and dependent inefficient according to the model. Therefore, teachers or adaptive learning systems should intervene in a timely and appropriate way for different types of learning crisis groups to solve the problems of poor learning performance, insufficient commitment to learning, poor learning habits, low participation in learning, low self-efficacy and other problems of learners in different learning scenarios.

The capacity to generate prediction is indispensable in daily existence, particularly amidst the swift transformations that are occurring on a global scale. Therefore, this study aimed to analyze the levels of prediction ability among mathematics students when presented with data in graphs. A qualitative approach was adopted, involving 37 mathematics students, using taskbased tests and interviews as data collection techniques. The results showed that the ability of most students to make a prediction based on the COVID-19 graph was at a multi-structural level of 35.14%. This level was characterized by students making predictions based on the trends of the pandemic graph patterns, but they tended to overlook the overall patterns. The prediction generated at the unistrutural, multistructural, relational, and extended abstract levels was considered reasonable because of the graph or data provided. These findings indicated the existence of predictive reasoning conducted by students in making predictions of problems related to the COVID-19 graph. The insights gained into the prediction ability prompted teachers to enhance graphic literacy instruction, to equip students with the skills needed to thrive in the 21st century.

Predicting and improving the academic achievement of university students is a multifactorial problem. Considering the low success rates and high dropout rates, particularly in open education programs characterized by mass enrollment, academic success is an important research area with its causes and consequences. This study aimed to solve a classification problem (successful or unsuccessful), predict students' academic success, and identify those at risk. The primary objective was to predict the academic success status with 26,708 students enrolled in Istanbul University open and distance education programs between 2011 and 2017. Predictions were based demographic data and success grades in Turkish, Atatürk's Principles and History of Revolution, English, and Disaster Culture courses. The study utilized classification models from supervised learning algorithms and was conducted using the SPSS Modeler 18 program. Initially, the data was divided into 70% training and 30% test data. Then, models were constructed by using Random Forest, Tree-AS, C&RT, C5.0, CHAID, QUEST, Naive Bayes, Logistic Regression, NeuralNet, and SVM algorithms. Model performances were compared according to accuracy, sensitivity, specificity, F1 score, positive predictive value, negative predictive value, and Matthews Correlation Coefficient criteria. The C&RT model demonstrated the best performance, achieving the highest specificity value of 0.915.

This study aimed to study the association between diglossic reading skills and reading comprehension in first grade students with and without learning disabilities. In this study population, students presented diglossia in literary and colloquial Arabic: 30 first grade students with learning disabilities and 30 first grade students without learning disabilities were assessed using a reading test (accuracy and fluency in both languages: literary and colloquial) and a reading comprehension test. This study's findings show significantly better measures of reading skills and reading comprehension in the students without learning disabilities, and significant interaction between reading and reading comprehension in the entire study population. In view of the marked gaps between the two populations of students, it is important to pay special attention to the population with learning disabilities and strive to help these students improve on the measures tested in this study (reading skills and reading comprehension). This could have a positive impact on a variety of other pedagogical-related phenomena, such as academic achievements in school subjects.

This study aims to research the phenomena of Racism and Xenophobia in pre-school education in the context of Greek reality through the attitudes and views of teachers who are the communicators of educational practice. The radical change in the racial and religious homogeneity of Greek education in recent decades as a result of globalization and the high mobility of populations has created significant challenges in the educational scene, giving rise to the need to investigate the phenomenon. For the study, we employed a qualitative methodology. The teachers stated that issues such as xenophobia, religious differences, and the economic hardship in which many families have fallen due to the economic crisis make the phenomenon difficult, making the predictions for its development ominous.

The purpose of this study was to examine the degree to which impostor feelings and selected demographic variables predict burnout among undergraduate music education majors. Participants (N = 143) completed the Clance Impostor Phenomenon Scale (CIPS) and the Maslach Burnout Inventory for Students. Overall, participants reported relatively high levels of impostor feelings and burnout. Results of multiple regression analyses revealed that CIPS score and year in school significantly predicted exhaustion and cynicism. Participants with higher CIPS scores (more frequent impostor feelings) and more years in school experienced greater exhaustion and cynicism. Descriptive analyses indicated that upper-division and band students experienced more cynicism than lower-division and choral/string/general music education majors, respectively. CIPS score was the only variable that significantly predicted professional efficacy. Participants with lower CIPS scores rated themselves higher in professional efficacy.

Objective: ADHD is subject to stigma from the general population. Exposure to stigma poses the risk of developing self-stigma of youth and parents, but few studies have focused on self-stigma of ADHD. Furthermore, parental factors have been implicated in self-stigma of youth, but no previous research has assessed the association between self-stigma of parents and youth. Therefore, the objective of this study was to better understand the experience of self-stigma of youth and their parents in the context of ADHD. Method: Fifty-five youth with ADHD (aged 8-17) and one parent reporter per youth completed surveys to report their experiences. Results: The results of this study found that both youth and parents reported significantly lower self-stigma scores compared to most previously published research. Parents of boys reported higher self-stigma scores compared to parents of girls. Youth who reported higher self-stigma also reported lower self-esteem. Self-stigma scores in youth were predicted by inattentive symptoms but not hyperactive/impulsive symptoms or parental self-stigma. Conclusion: Results emphasize the importance of understanding self-stigma of ADHD, symptom severity, and the need for interventions for families with ADHD.

The question of precollege physics access and performance has been a persistent concern when considering the goal of diversifying participation in post-secondary STEM study and careers. This observational study examined school-level academic and demographic predictors of high school physics enrollment and performance in the USA. Due to the sequential and hierarchical nature of high school science and mathematics coursework, related predictors were included in multivariable regression and structural equation models to understand whether selectivity constrains the STEM pipeline. Descriptive and inferential analyses of state-level data were conducted (N = 663 high schools enrolling 559,044 students) to define the scope of physics enrollment and performance, and the predictive value of ethnicity, socioeconomic status, and science and mathematics course enrollments and performance. Physics was taken by fewer students when compared to biology, earth science, and chemistry. Correlations, multiple regression, and structural equation modeling indicated that although socioeconomic status was the main predictor of student "enrollment" in physics, this was mediated by schoolwide enrollment in chemistry and algebra II as well as schoolwide performance in chemistry and geometry. School-level "performance" in physics was negatively predicted by the percentage of students traditionally underrepresented in STEM, yet the predictive value was mediated by chemistry and algebra II performance. Results suggest that the prevalent science and mathematics sequence correlates to diminished physics enrollment, particularly with regard to students traditionally marginalized in STEM. US school leaders and policy makers should implement more proactive interventions earlier in the STEM pipeline to promote diverse physics participation and more equitable performance outcomes.

The use of Artificial Intelligence (AI) in education is transforming various dimensions of the education system, such as instructional practices, assessment strategies, and administrative processes. It also plays an active role in the progression of science education. This systematic review attempts to render an inherent understanding of the evidence-based interaction between AI and science education. Specifically, this study offers a consolidated analysis of AI's impact on students' learning outcomes, contexts of its adoption, students' and teachers' perceptions about its use, and the challenges of its use within science education. The present study followed the PRISMA guidelines to review empirical papers published from 2014 to 2023. In total, 74 records met the eligibility for this systematic study. Previous research provides evidence of AI integration into a variety of fields in physical and natural sciences in many countries across the globe. The results revealed that AI-powered tools are integrated into science education to achieve various pedagogical benefits, including enhancing the learning environment, creating quizzes, assessing students' work, and predicting their academic performance. The findings from this paper have implications for teachers, educational administrators, and policymakers.

In recent years, Sweden has been struggling with issues of educational inequity as the influence of students' socioeconomic status on their academic achievements has amplified. Nonetheless, academically resilient students who demonstrate high achievement despite socioeconomic disadvantages offer hope for a more equitable future. Previous research has primarily focused on the relationship between well-being and academic achievement, with less emphasis on the connection between academic resilience and well-being. Thus, this study investigates the extent to which students' well-being predicts their academic achievement and resilience, with a special focus on the social well-being of socioeconomically disadvantaged students. Using the Swedish PISA 2018 dataset and structural equation modeling technique, the measurement properties of social well-being were first tested, and its dimensions were then related to students' academic resilience and achievement. The findings reveal that student-reported teacher support positively predicts their academic resilience and achievement, whereas exposure to bullying is detrimental to their academic achievement.

Early detection of and relevant information on children's mathematical difficulties is important to initiate targeted teaching and intervention. This study investigated the extent to which strategy use in single-digit addition provides additional predictive information about 61 grade one children's (6-year-old) mathematical achievement 3 years later that is not available from a standardised mathematics achievement test. Four predictors available in year one (arithmetic strategy use, mathematical achievement, non-verbal reasoning skills and sex) explained 54% of the variation in grade four mathematics achievement. Arithmetic strategy use was the most important single predictor of year four mathematics achievement (R[superscript 2] = 30%) and explained an additional 12% variation if added to a model comprised by the three other year one predictors. This result suggests that systematically obtained measures of how young children solve single-digit arithmetic problems might provide useful information about their foundational number knowledge, which in turn may reveal how well they achieve later in school.

Studying texts constitutes a significant part of student learning in health professions education. Key to learning from text is the ability to effectively monitor one's own cognitive performance and take appropriate regulatory steps for improvement. Inferential cues generated during a learning experience typically guide this monitoring process. It has been shown that interventions to assist learners in using comprehension cues improve their monitoring accuracy. One such intervention is having learners to complete a diagram. Little is known, however, about how learners use cues to shape their monitoring judgments. In addition, previous research has not examined the difference in cue use between categories of learners, such as good and poor monitors. This study explored the types and patterns of cues used by participants after being subjected to a diagram completion task prior to their prediction of performance (PoP). Participants' thought processes were studied by means of a think-aloud method during diagram completion and the subsequent PoP. Results suggest that relying on comprehension-specific cues may lead to a better PoP. Poor monitors relied on multiple cue types and failed to use available cues appropriately. They gave more incorrect responses and made commission errors in the diagram, which likely led to their overconfidence. Good monitors, on the other hand, utilized cues that are predictive of learning from the diagram completion task and seemed to have relied on comprehension cues for their PoP. However, they tended to be cautious in their judgement, which probably made them underestimate themselves. These observations contribute to the current understanding of the use and effectiveness of diagram completion as a cue-prompt intervention and provide direction for future research in enhancing monitoring accuracy.

This study examines the factors that influence the intention and actual use of e-learning in entrepreneurship education by undergraduate students. The paper relies on a predictive study design and the partial least squares structural equation modelling to analyse data from a cluster sample of 599 students from the University of Cape Coast, Ghana. The results demonstrate that all the independent variables, assessed within a framework of an extended unified theory of acceptance and use of technology, determine students' intention to use e-learning in entrepreneurship education. Of particular importance are the preponderant effects of hedonic motivation, facilitating conditions and social influence on students' intention as well as the effect of intention and facilitating conditions on actual use of e-learning. A key implication of the findings is that students' continuous acceptance and use of e-learning in undergraduate entrepreneurship course delivery is contingent upon adequate presence of all the examined predictors of technology usage.

Advocates often predict that school choice policies will expand access to high-quality schools, particularly for marginalized communities. To interrogate this assumption, we employed a sequential mixed-methods analysis examining the state of charter reform in the District of Columbia. We observed that stakeholders consistently defined equity as uniform processes, evident in data distribution and enrollment practices. We also uncovered persistent disparities in where students live versus learn. We conclude that race-neutral conceptions of equity may hinder attempts to improve school access by ignoring structural inequalities tied to race, place, and income and by overlooking privileged households' efforts to remain separate.

Even highly motivated undergraduates drift off their STEM career pathways. In large introductory STEM classes, instructors struggle to identify and support these students. To address these issues, we developed co-redesign methods in partnership with disciplinary experts to create high-structure STEM courses that better support students and produce informative digital event data. To those data, we applied theory- and context-relevant labels to reflect active and self-regulated learning processes involving LMS-hosted course materials, formative assessments, and help-seeking tools. We illustrate the predictive benefits of this process across two cycles of model creation and reapplication. In cycle 1, we used theory-relevant features from 3 weeks of data to inform a prediction model that accurately identified struggling students and sustained its accuracy when reapplied in future semesters. In cycle 2, we refit a model with temporally contextualized features that achieved superior accuracy using data from just two class meetings. This modelling approach can produce durable learning analytics solutions that afford scaled and sustained prediction and intervention opportunities that involve explainable artificial intelligence products. Those same products that inform prediction can also guide intervention approaches and inform future instructional design and delivery.

Retractions have traditionally been reserved for correcting the scientific record and discouraging research misconduct. Nonetheless, the potential for actual societal harm resulting from accurately reported published scientific findings, so-called information hazards, has been the subject of several recent article retractions. As these instances increase, the extent of support for such decisions among the scientific community and lay public remains unclear. Undergraduates (Study 1) and federally funded researchers (Study 2) reported their support for retraction decisions described as due to misconduct, honest errors, or potential information hazards. Participants supported retraction on the former two grounds more than the latter. Despite limited support, women remained more receptive to retractions based on information hazards. Activist tendencies additionally predicted undergraduate men's receptivity. Receptivity toward retraction due to information hazards was unrelated to scientists' engagement in activism, suggesting that formal scientific training affords researchers an ability to separate personal and professional values in scientific discourse. Findings could inform the development of educational materials that may aid less experienced scientists and the lay public in understanding retraction ethics.

We investigated the role of cue weighting, second language (L2) proficiency, and L2 daily exposure in L2 learning of suprasegmentals different from the first language (L1), using eye-tracking. Spanish monolinguals, English-Spanish learners, and Mandarin--Spanish learners saw a paroxytone and an oxytone verb (e.g., "FIRma-firMÓ" "s/he signs-signed"), listened to a sentence containing one of the verbs, and chose the one that they heard. The three languages have contrastive lexical stress, but suprasegmentals have a greater functional load in Mandarin than in English. Monolinguals predicted suffixes accurately with both stress conditions and favored oxytones, but learners predicted suffixes accurately only with oxytones, the condition activating fewer lexical competitors. Monolinguals predicted suffixes accurately sooner but at a slower rate than did learners. L2 proficiency, but not L1 or L2 exposure, facilitated L2 predictions. In conclusion, learners of a contrastive-stress L1 rely on L2 suprasegmentals to the same extent as monolinguals, regardless of their L1. Lower L2 proficiency and higher cognitive load (more lexical competitors) reduce learners' reliance on suprasegmentals.

Instructional leadership has begun to be examined as an essential factor in school development research. It has received considerable attention, especially in developing countries, in recent years. This study examined the relationship between school principals' instructional leadership and teacher instructional practices and the mediating role of teacher professional learning. A cross-sectional survey design was adopted in the study, and the participants were 385 elementary and middle school teachers working in a city in western Türkiye. The data were analysed through structural equation modelling. Findings demonstrated that instructional leadership significantly predicted teacher instructional practices and teacher professional learning. Findings also highlighted teacher professional learning as a significant mediator between instructional leadership and teacher instructional practices. This study confirmed that instructional leadership can strengthen teacher instructional practice and professional learning in a centralized education system.

Within the framework of Self-Determination Theory (SDT), basic psychological need satisfaction predicts quality of motivation which in turn predicts study efforts. Although studies focusing on interpersonal differences have repeatedly shown this sequence of relations, only a few have examined its stability at the intra-personal level. In this diary study, we recruited 141 university students (M[subscript age] = 20.80, SD = 2.20 years) to investigate the degree of confluence among week-to-week need satisfaction, quality of motivation, learning strategies, and procrastination for four weeks. Multilevel structural equation modelling showed that need satisfaction covaried positively with autonomous motivation. In turn, week-to-week autonomous motivation predicted positively week-to-week critical thinking and effort regulation and negatively procrastination. These relations emerged even after controlling for gender, age, and study hours per week. Further, contextual autonomous motivation predicted higher mean levels of critical thinking and effort regulation and lower ones of procrastination. Interestingly, a cross-level interaction supported the sensitivity hypothesis as the negative relation between need satisfaction and controlled motivation was only true among students who were high in contextual (pre-diary assessed) controlled motivation. These findings highlight the importance of contextual motivation and the need to establish academic environments that consistently satisfy students' psychological needs, thus promoting the quality of motivation and adaptive learning strategies.

Background: In the past decades, there has been a growing concern to understand why boys struggle in school. One of the turning points in students' educational trajectories likely to exacerbate boys' academic difficulties is students' enrolment in private or enriched school programmes, as boys are underrepresented in such programmes. Method: To better understand this gender imbalance, our research draws on a longitudinal design to examine whether grade 6 students' externalizing behaviours, school engagement and school grades in mathematics and language arts relate to secondary school programme attendance, among a sample size of 577 students (277 boys). Results: Path analysis showed that only language arts grades predicted enrolment in private or selective public programmes and contributed to boys' underrepresentation in these programmes. Conclusions: Such findings have important implications for understanding boys' underachievement and low persistence in school as well as to guide interventions to promote gender and overall educational equity in school.

The ability to predict upcoming events is essential in infancy because it enables babies to process information optimally and have successful goal-directed interactions with their environment. In this article, we examine how infants generate predictions in perception, cognition, and action, and address whether and how their predictions are motivated and affected by their motor development. Our synthesis of research demonstrates that infants form predictions in the perception, cognition, and action domains based on perceived statistical information, pre-existing and newly generated knowledge, and internal motor models. Our analysis reveals that infants' increasing fine and gross motor experiences have a moderating impact on the elaboration of the different bases for predictions. Based on this, we conclude that new motor experiences enable infants to constantly improve the bases from which they generate and update their predictions.

Science scores among US fourth-grade students have declined compared to their international counterparts in recent years. Recent results show that teachers are the most impactful influence on student success and accountability. Teacher preparedness and professional development are two key areas that serve as indicators of providing relevant and essential information for students' success. A correlational quantitative study was conducted to assess the relationship between teacher preparedness and professional development on fourth-grade students' science achievement. The TIMSS 2019 data were secured from the Boston College, TIMSS, and PIRLS International websites. The data was evaluated using the SPSS 27 Hierarchical Linear Regression. The study's population included 70 teachers and 1208 students representing schools located in the United States. Results indicated that teachers who graduate with science majors have the greatest impact on students' science outcomes. Specific professional development topics that teachers studied also served as key indicators of student science success. Topics that specify the objectives found in the "Next Generation Science" curriculum had the greatest impact. The topics included future science pedagogy, critical thinking, and past and future student needs. Thus, recruitment of teachers with science majors should be the focus of human resources recruitment officers, and quality, well-developed professional development must focus on the "Next Generation Science Curriculum."

School dropout is a significant issue in distance learning, and early detection is crucial for addressing the problem. Our study aims to create a binary classification model that anticipates students' activity levels based on their current achievements and engagement on a Canadian Distance learning Platform. Predicting student dropout, a common classification problem in educational data analysis, is addressed by utilizing a comprehensive dataset that includes 49 features ranging from socio-demographic to behavioral data. This dataset provides a unique opportunity to analyze student interactions and success factors in a distance learning environment. We have developed a student profiling system and implemented a predictive approach using XGBoost, selecting the most important features for the prediction process. In this work, our methodology was developed in Python, using the widely used sci-kit-learn package. Alongside XGBoost, logistic regression was also employed as part of our combination of strategies to enhance the models predictive capabilities. Our work can accurately predict student dropout, achieving an accuracy rate of approximately 82% on unseen data from the next academic year.

PISA aims to serve as a "global yardstick" for educational success, as measured by student performance. For comparisons to be meaningful across countries or over time, PISA samples must be representative of the population of 15-year-old students in each country. Exclusions and non-response can undermine this representativeness and potentially bias estimates of student performance. Unfortunately, testing the representativeness of PISA samples is typically infeasible due to unknown population parameters. To address this issue, we integrate PISA 2018 data with comprehensive Swedish registry data, which includes all students in Sweden. Utilizing various achievement measures, we find that the Swedish PISA sample significantly overestimates the achievement levels in Sweden. The observed difference equates to standardized effect sizes ranging from d = 0.19 to 0.28, corresponding to approximately 25 points on the PISA scale or an additional year of schooling. The paper concludes with a plea for more rigorous quality standards and their strict enforcement.