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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.

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.

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.

Completion of remedial mathematics has been identified as one of the keys to college success. However, completion rates in remedial mathematics have been low and are of much debate across America. This study leverages machine learning techniques in trying to predict and understand completion rates in remedial mathematics. The purpose of this study is to build machine learning models that can predict students that are least likely to complete remedial mathematics and identify which factors are most influential when computing those predictions. The study discovers random forest as the highest performing model. Furthermore, the study reveals that the remedial course a student begins with, credit completion rate, math placement score, and high school G.P.A as the most influential predictors of completion rates. The study also offers future research directions, especially in how to improve the performance of the machine learning models.

This study examined the heterogeneity of literacy profiles for adolescents with and without a specific learning disability (SLD) in the U.S. state of Florida Student subgroups displaying common patterns of performance in word-level skills and reading comprehension were identified through latent profile analysis. Results indicate most of the total sample demonstrated below-average performance in one or both areas with word-level skill difficulties being more common than difficulties in reading comprehension alone. Changes in reading performance by profile over time (Grades 6-8) were examined through a latent transition analysis revealing consistent patterns in the SLD sample and variable patterns in the typically developing sample. Resulting profiles were utilized to predict performance on an end-of-year broad reading comprehension measure indicating very little change in performance over time. Findings suggest large numbers of adolescents with concurrent word-level and reading comprehension difficulties likely need sustained intervention in word-level skills to support their reading comprehension.

We collected data on students' self-assessment behavior from four sections of a Calculus II course. Students were asked to write their expected scores on each of the weekly in-class quizzes and problems in the exams, immediately after they completed them. They were then asked to justify their expectation in writing. One-on-one interviews were conducted with a purposefully selected sample of students. During the interviews, they were asked to explain their perceived reasons for their self-assessment behaviors. While the results from quantitative analysis seemed to partially reinforce the findings of existing research that low performers generally overestimate, high performers underestimate their performance, and those in-between performers were (almost) accurate predictors, results from qualitative analysis provided additional insights into their self-assessment behaviors. After analyzing qualitative data, we identified five categories of student behavior: knowing about knowing (KK), not knowing about knowing (NKK), knowing about not knowing (KNK), knowing something is not known but not sure what (KBNKW), and not knowing about not knowing (NKNK). The quantitative analysis showed that students exhibited greater accuracy in assessing their performance when they belonged to the categories KK, KNK, and KBNKW, while their accuracy was lower when they fell into the categories NKNK and NKK. In other words, students who had greater awareness of their level of knowledge were more accurate in predicting their scores compared to their peers, irrespective of their actual performance levels. The logistic regression model revealed a substantial increase in the likelihood of underperforming students overestimating their performance compared to their high-performing counterparts.

Various studies have been done on shifting toward technology-based second language (L2) education. However, the influence of psycho-emotional factors on students' technology acceptance is overlooked. To fill this gap, the present quantitative study examined the role of students' achievement emotions and technological self-efficacy in predicting their technology acceptance in China. To this end, 380 Chinese students were invited to complete three questionnaires. The results of structural equation modeling revealed that Chinese L2 students' achievement emotions and technological self-efficacy are significant predictors of their technology acceptance; technological self-efficacy uniquely could explain 59% of its variance and students' achievement emotions could explain 75% of its variance. The study also draws some conclusions and offers implications for L2 teachers, students, and school managers to foster the acceptance of technology in L2 education.

With the spread of online education, many studies highlighted the crucial role of digital literacy in second/foreign language (L2) learning. However, the influence of such a competency on L students' perceived emotions has remained under-research. To fill this lacuna, this study used a quantitative design via three questionnaires to investigate the predicting role of students' digital literacy (DL) in their perceived enjoyment and online learning self-efficacy (OLSE). To do so, a sample of 987 Chinese English as a foreign language students, at university level, were invited to partake in the study. The results of structural equation modeling demonstrated that DL could predict about 95% of changes in the participants' FLE ([beta] = 0.949, p < 0.001). Furthermore, it was found that about 92% of changes in Chinese students' OLSE could be significantly predicted by their DL ([beta] = 0.916, p < 0.001). Drawing on the obtained results, the study presents discussions and implications for L2 educators regarding the role of digital literacy in students' perceived emotions.

Empirical research on number sense to date has been conducted with preschoolers and elementary school students. Despite their contributions to the literature, these studies have used variable-centered analytic approaches that may prevent distinct number sense profiles for preschoolers and first graders from being identified. This study aimed to determine the number and proportions of distinct number sense profiles, the variables influencing these profiles, and the characteristics of the children with different profiles in the transition from preschool to elementary school. We conducted a quantitative study using a cross-sectional design for this purpose and used latent profile analysis to identify and describe distinct number sense profiles for preschoolers and first-grade students. A total of 1688 children, 848 from preschool and 840 from the first grade of elementary school, took part in the study. A Number Sense Screener was used to determine number sense profiles. The data collection tool used in the study was found to be reliable. Based on the findings of the study, we identified three profiles for both the preschoolers and first graders, namely, moderate very low, moderate low, and moderate high. We examined family and household income variables to help predict the profiles. We then took the characteristics and components of number sense that caused the differentiation in the profiles as revealed by the results of the study and discussed them in light of the literature.

This mixed-methods research study aimed to observe the changes in relational conceptual changes and achievement in photosynthesis and cellular respiration in 15 seventh-grade students using the variation theory of learning, a framework for contextual distinctions, and supports the Common Knowledge Construction Model (CKCM) for science education. The strategy used was prediction-observation-explanation, where students wrote and drew to express their macroscopic and sub-microscopic concepts, depicting any changes. Pre- and post-assessment tests were given to measure achievement in science. Through phenomenography analysis, five descriptive categories were generated for photosynthesis and nine for cellular respiration, revealing variations within and between students' ideas. Statistical tests showed significant improvements (p < 0.001) in photosynthesis and cellular respiration knowledge after implementing the CKCM learning sequence, with scores increasing from 37 to 77% and 33% to 72%, respectively. These findings suggest that inquiry-based learning based on relational conceptual change, guided by variation theory and an understanding of the nature of science, can lead to a deeper understanding of scientific explanations and improve overall science achievement.

While extensive studies on informal online learning have been well documented to afford teachers' collaborative learning and knowledge sharing, little is still known about their motivational factors regarding the continuance intention of informal online learning. To this end, an extended expectation confirmation model (ECM) was proposed including intrinsic and extrinsic motivation. The proposed research model and several hypotheses were empirically evaluated using questionnaire surveys with the valid data collected from 231 Chinese in-service teachers in the shared mobile learning community. The results consolidate the appropriateness of the extended ECM to explain teachers' informal online learning continuance. Specifically, satisfaction is the major determinant of continuance intention, followed by perceived usefulness and intrinsic motivation. In addition, extrinsic motivation positively predicts perceived usefulness and confirmation. The results of this study provide some theoretical and practical implications into in-service teachers' continuance intention of informal online learning.

This research explores preservice classroom teachers' perceived importance, self-efficacy beliefs, participation frequencies and conceptions related to new literacies. The research is framed using a dual-level theory of new literacies. The participants of the study consisted of 364 preservice teachers studying in the department of primary education at two state universities in Turkey. The findings revealed that (i) while preservice teachers displayed high participation in new literacies requiring the consumption of information, they displayed low participation in information production activities; (ii) the importance preservice teachers attached to online or digital literacy activities and their self-efficacy beliefs significantly predicted their participation in these activities; (iii) preservice teachers' conceptual understandings with regard to new literacies were compatible with their participation frequencies and perceived importance with regard to online or digital literacy activities. Some suggestions for preservice teachers' skills, strategies, dispositions and practices related to new literacies are discussed.

Project-based Learning (PBL) provides an effective environment for collaborative engineering design education. However, it is difficult to assess students' engagement and provide process-oriented feedback on their collaboration due to limited resources and scalability challenges. This paper presents an empirical study examining the application of Machine Learning (ML) to evaluate student engagement in PBL. A supervised ML model (SML) was developed using Natural Language Processing (NLP) to predict engagement levels using conversation data in online chat groups and comparing them to the ground truth metrics. Eleven teams of engineering students participated in the PBL courses on systems engineering while employing text-based discussions. Communication messages were manually labelled for active engagement metrics collected in our prior research. The SML was constructed with Gradient Boosting to classify engagement levels. The model was trained using more than 5,000 manually classified text messages, and it demonstrated an accuracy range of 0.75 to 0.81 on held-out test data. The model's predictions for complete team communication showed a meaningful association with the actual levels of engagement as determined by log data and surveys. Moreover, the model reproduces general patterns of team engagement and collaboration. According to the findings, conversational data can be a scalable source for ML-driven engagement assessment. This shows how AI has the potential to scale up process-oriented assessment in PBL. Future research should look towards instructor-AI dashboards and individualized feedback systems. The work adds an empirical evaluation of AI for automated engagement analysis, extending earlier research on feedback systems to tackle PBL issues.

Self-regulated learning (SRL) is an essential skill to achieve one's learning goals. This is particularly true for online learning environments (OLEs) where the support system is often limited compared to a traditional classroom setting. Likewise, existing research has found that learners often struggle to adapt their behaviour to the self-regulatory demands of OLEs. Even so, existing SRL analysis tools have limited utility for real-time or individualised support of a learner's SRL strategy during a study session. Accordingly, we explore a reinforcement learning based approach to learning optimal SRL strategies for a specific learning task. Specifically, we utilise the sequences of SRL processes acted by 44 participants, and their assessment scores for a prescribed learning task, in a purpose-built OLE to develop a long short-term memory (LSTM) network based reward function. This is used to train a reinforcement learning agent to find the optimal sequence of SRL processes for the learning task. Our findings indicate that the developed agents were able to effectively select SRL processes so as to maximise a prescribed learning goal as measured by predicted assessment score and predicted knowledge gains. The contributions of this work will facilitate the development of a tool which can detect sub-optimal SRL strategy in real-time and enable individualised SRL focused scaffolding.

Educational science has consistently examined what drives adolescents to aspire to occupations in math-related fields -- and which factors, such as gender stereotypes, may drive them away. In this study, we utilized a large longitudinal data set (N = 1,092) to test whether past grades, gender, and the math level of mothers' and fathers' occupations predicted the math-relatedness of German adolescents' occupational aspirations 1 year later in 10th grade controlling for mathematical competence, intelligence, and socioeconomic status, among other factors. First, we examined whether motivational beliefs -- math intrinsic value and math ability self-concept -- mediated these relationships. Second, we used a latent interaction approach to test whether intrinsic value and ability self-concept interacted to predict the math-relatedness of aspirations, an important yet understudied prediction of expectancy-value theories. The intrinsic value of math, but not ability self-concept fully mediated the effect of math grade on aspirations. Gender differences could partly be explained via intrinsic value, but a substantial effect remained. The math level of mothers', but not fathers' occupations was positively associated with the math-relatedness of their children's aspirations, but this effect was not mediated by motivational beliefs. Exploratory analyses examined these relations separately for boys and girls. Regarding our second aim, there was a significant interaction showing that intrinsic value only predicted the math-relatedness of aspirations among students with a high-ability self-concept. This latter finding points to the importance of continuing to investigate not only main effects but also interaction effects within the framework of the situated expectancy-value theory.

In this conceptual paper, we aim to revisit key research themes in contemporary organizational institutionalism and by doing this, redirect attention of scholars in public administration towards the most promising domains of application of institutional theory. We propose to shift attention from enabling and power-induced framing of institutional theory towards understanding it as a theory that helps recognize and analyse institutional pressures, constraints and inertia that influence administrative reforms and other organizational change attempts in public organizations. We sharpen the focus of institutional theory, specify the boundaries of institutional explanations by analysing questions that lie beyond institutional theory domain of application and suggest most promising research directions. Thus, we warn against unnecessary inflation of the scope of a theoretical apparatus and "explaining" how actors create, change and disrupt institutions and propose how institutional theory can be applied to predict the patterns of success or failure of changes in the public sector.

In many domains, learners extract recurring units from continuous sequences. For example, in unknown languages, fluent speech is perceived as a continuous signal. Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism is statistical learning, whereby learners track how predictive syllables (or other items) are of one another. Syllables within the same word predict each other better than syllables straddling word boundaries. But does statistical learning lead to memories of the underlying words--or just to pairwise associations among syllables? Electrophysiological results provide the strongest evidence for the memory view. Electrophysiological responses can be time-locked to statistical word boundaries (e.g., N400s) and show rhythmic activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences (and when the underlying words are not excessively long), the network activation is rhythmic with the periodicity of a word duration and activation maxima on word-final syllables. This is because word-final syllables receive more excitation from earlier syllables with which they are associated than less predictable syllables that occur earlier in words. The network is also sensitive to information whose electrophysiological correlates were used to support the encoding of ordinal positions within words. Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. Learners might thus need to rely on cues beyond statistical associations to learn the words of their native language.

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non-randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta-regression model combining AD, IPD randomized clinical trial to estimate heterogeneous treatment effects. We illustrated the approach in the re-analysis of a network of studies comparing three drugs for relapsing--remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non-randomized evidence.

The primary socializing agents have been found to play a key role in shaping teachers' attitudes. In the realm of inclusive education (IE), one of the most extensively researched themes is teachers' attitudes regarding inclusion. Contrary to a plethora of attitudinal studies that are solely concentrating on describing teacher attitudes, the present study sought to investigate the perceived social support (PSS) perceptions of pre-service special education teachers in terms of their attitudes towards inclusive education and their self-efficacy beliefs (TSE). The population of this correlational research design study consists of 525 pre-service special education teachers from six different colleges of education in Egypt's northern, central and southern big cities. Using a correlation technique and structural equation modeling (SEM) analysis, the research data were analysed. The findings revealed that the PSS scales' sub-dimensions indicate having a significant and positive relationship with TSE as well as attitudes towards IE. In addition, research revealed that pre-service special education teachers' PSS significantly predicted their TSE beliefs and attitudes towards IE. The study's findings reflect crucial implications for teacher education policymakers in addressing pre-service teachers' needs of professional learning programmes that should include a career development stage targeted at generating socially supportive role models.

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."