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This paper will examine the development of a data warehouse aimed at improving decision-making in higher education, which focuses on the identification of students at-risk of academic failure through machine learning techniques. This research utilizes South East European University (SEEU) as a case study to show how data warehousing can integrate various student data—including demographics, academic performance, grades, attendance, and engagement—into an integrated framework that enables predictive analytics.The overall approach allows SEEU decision-makers, administrators, and faculty to proactively identify and assist at-risk students, which improves student retention and their academic success. The results underscore the crucial role of data warehousing in enhancing student achievement and facilitating informed decision-making in higher education.The paper concludes with concrete suggestions for leveraging data to enhance decision-making processes in a digital educational institution.

The attitudes and behaviors of teachers directly influence the cognitive, affective, and social development of students. These effects may be positive or negative and may last a long time. The aim of this study is to examine teacher behaviors and attitudes that can negatively or positively affect student progress. Using a qualitative research approach, the study adopted a basic interpretative research design. The study involved 229 undergraduate students studying at two state universities in Türkiye. Under the headings of positive and negative themes, participants explained the most influential teacher behaviors and attitudes they encountered in primary, secondary, or high school. A total of 183 positive and 187 negative narratives were analyzed. Positive teacher behaviors and attitudes were grouped under three categories: effective communication and ethical attitude, professional competence and dedication, and individual support and trust. Students have been found to be more confident, motivated, and satisfied with their learning, and are more likely to trust their teachers when they exhibit these behaviors. Negative behavior and attitudes were classified into discrimination and injustice, classroom management and communication problems, and occupational incompetence and irresponsibility. In addition to reducing students' motivation to learn, self-confidence, and respect for teachers, these negative behaviors impede their social development.

Competency-based education is an educational paradigm with the primary goal of combining theoretical knowledge and practical skills, giving students the opportunity to effectively apply learning outcomes in real-life situations. This approach focuses on preparing students for life’s challenges by nurturing them as independent, critical, and creative thinkers who can adapt to an ever-changing environment. This article examines the process of competency-based assessment and progress monitoring in primary grades. The study conducted a qualitative content analysis of observed lessons and teacher interviews to reveal how assessment and continuous progress monitoring can contribute to comprehensive student development. Ten primary school teachers participated in the study, responding to researchers’ questions about competency-based education and their practices in assessment and progress monitoring. The article presents the results of a qualitative study aimed at evaluating the process of competence-based assessment and progress monitoring in primary school classrooms. The study results revealed that, in assessing student progress, teachers find it important for students to not only demonstrate knowledge in different situations but also gain a clearer understanding of their learning goals and have opportunities for growth. Such an assessment system not only helps students develop self-reflection but also encourages them to take responsibility for their learning process, continually improve, and strive for higher competency achievement.

Expansion in higher education and changes in student profiles have led to an increase in non-linear trajectories that do not fit into a time frame considered standard. However, universities continue to establish success and failure parameters relating to performance indicators that do not consider the heterogeneity of trajectories. The theoretical perspective on which we base our work is the complexity approach, which resituates what we understand as a trajectory of success (and failure), incorporating a broader view to better understand how students navigate through higher education. Based on research on university dropout and changes of trajectory, we present the results of a study carried out using sequence analysis in face-to-face and online universities. The research question on which this study focuses is whether, given the context of greater heterogeneity and complexity, there could be a greater similarity between the students’ trajectories in face-to-face and online modalities in relation to complex trajectories. The results show that complex trajectories are very present in both modalities, and differences are observed according to students’ gender and age. A key conclusion is the discussion on how we consider success and failure student trajectories in the contemporary university taking into account the perspective of complexity.

Although the importance of formative assessment of student progress has been well covered in previous studies, implementing formative assessment in the classroom requires targeted tools and educational policies. Therefore, we examined the factors that affect teachers' use of formative assessment practices and analyzed the moderating effect of the school's mastery goal structure in the relationship between teachers' self-efficacy and their use of formative assessment practices. Participants were 507 Chinese primary school teachers, who completed a survey. Structural equation modeling results reveal that teachers' selfefficacy regarding formative assessment and perception of a school mastery goal structure each positively predicted the use of formative assessment practices. The moderating effect of the school mastery goal structure in the relationship between teachers' self-efficacy and their use of formative assessment practices was also statistically significant. Our findings have implications for policy making and practice as well as for further studies regarding formative assessment of students.

As much as 80% of children on the autism spectrum exhibit challenging behaviors (ie, behaviors dangerous to the self or others, behaviors that interfere with learning and development, and behaviors that interfere with socialization) that can have a devastating impact on personal and family well-being, contribute to teacher burnout, and even require hospitalization. Evidence-based practices to reduce these behaviors emphasize identifying triggers (events or antecedents that lead to challenging behaviors); however, parents and teachers often report that challenging behaviors surface with little warning. Exciting recent advances in biometric sensing and mobile computing technology allow the measurement of momentary emotion dysregulation using physiological indexes. We present the framework and protocol for a pilot trial that will test a mobile digital mental health app, the KeepCalm app. School-based approaches to managing challenging behaviors in children on the autism spectrum are limited by 3 key factors: children on the autism spectrum often have difficulties in communicating their emotions; it is challenging to implement evidence-based, personalized strategies for individual children in group settings; and it is difficult for teachers to track which strategies are successful for each child. KeepCalm aims to address those barriers by communicating children's stress to their teachers using physiological signaling (emotion dysregulation detection), supporting the implementation of emotion regulation strategies via smartphone pop-up notifications of top strategies for each child according to their behavior (emotion regulation strategy implementation), and easing the task of tracking outcomes by providing the child's educational team with a tool to track the most effective emotion regulation strategies for that child based on physiological stress reduction data (emotion regulation strategy evaluation). We will test KeepCalm with 20 educational teams of students on the autism spectrum with challenging behaviors (no exclusion based on IQ or speaking ability) in a pilot randomized waitlist-controlled field trial over a 3-month period. We will examine the usability, acceptability, feasibility, and appropriateness of KeepCalm as primary outcomes. Secondary preliminary efficacy outcomes include clinical decision support success, false positives or false negatives of stress alerts, and the reduction of challenging behaviors and emotion dysregulation. We will also examine technical outcomes, including the number of artifacts and the proportion of time children are engaged in high physical movement based on accelerometry data; test the feasibility of our recruitment strategies; and test the response rate and sensitivity to change of our measures, in preparation for a future fully powered large-scale randomized controlled trial. The pilot trial will begin by September 2023. Results will provide key data about important aspects of implementing KeepCalm in preschools and elementary schools and will provide preliminary data about its efficacy to reduce challenging behaviors and support emotion regulation in children on the autism spectrum. ClinicalTrials.gov NCT05277194; https://www.clinicaltrials.gov/ct2/show/NCT05277194. PRR1-10.2196/45852.

Background Dropout and poor academic performance are persistent problems in medical schools in emerging economies. Identifying at-risk students early and knowing the factors that contribute to their success would be useful for designing educational interventions. Educational Data Mining (EDM) methods can identify students at risk of poor academic progress and dropping out. The main goal of this study was to use machine learning models, Artificial Neural Networks (ANN) and Naïve Bayes (NB), to identify first year medical students that succeed academically, using sociodemographic data and academic history. Methods Data from seven cohorts (2011 to 2017) of admitted medical students to the National Autonomous University of Mexico (UNAM) Faculty of Medicine in Mexico City were analysed. Data from 7,976 students (2011 to 2017 cohorts) of the program were included. Information from admission diagnostic exam results, academic history, sociodemographic characteristics and family environment was used. The main dataset included 48 variables. The study followed the general knowledge discovery process: pre-processing, data analysis, and validation. Artificial Neural Networks (ANN) and Naïve Bayes (NB) models were used for data mining analysis. Results ANNs models had slightly better performance in accuracy, sensitivity, and specificity. Both models had better sensitivity when classifying regular students and better specificity when classifying irregular students. Of the 25 variables with highest predictive value in the Naïve Bayes model, percentage of correct answers in the diagnostic exam was the best variable. Conclusions Both ANN and Naïve Bayes methods can be useful for predicting medical students’ academic achievement in an undergraduate program, based on information of their prior knowledge and socio-demographic factors. Although ANN offered slightly superior results, Naïve Bayes made it possible to obtain an in-depth analysis of how the different variables influenced the model. The use of educational data mining techniques and machine learning classification techniques have potential in medical education.

Personalized learning (PL) has been internationally promoted as a future direction of educational reform efforts. While there is growing evidence of PL enhancing learning outcomes, teachers reported having difficulty envisioning PL in practice. This national survey study investigated how PL is practiced in K-12 learner-centered schools in the U.S. to inform educators of learner-centered teachers’ PL practice and identify gaps between their practice and research. Five essential components were identified: PL plans, competency-based student progress, criterion-referenced assessment, project- or problem-based learning, and multi-year mentoring. Based on the five components, we identified 308 learner-centered schools and received 272 teacher responses from 41 schools. The five components were implemented with different levels of implementation fidelity. We uncovered several areas in need of improvement. Career goals were not often considered when creating PL plans. A misalignment between student progress and assessment practice was found. There was a lack of community involvement during the PBL process. Teachers were not able to build a close relationship with all students. These findings from learner-centered schools revealed that paradigm change demands continuous effort to transform all aspects of the educational system. Suggestions are made for practice and future research.

Math anxiety negatively relates to math performance. This negative relationship may be exacerbated in low-progress math learners. However, there are limited studies on math anxiety among low-progress learners in a paradoxically high-performing education system like Singapore. To fill this research gap, this research analyzed the anxiety profiles of 151 students who were in the math learning support intervention program administered by the Ministry of Education, Singapore (MOE). We examined the complex relationship centered in math anxiety with relevant variables such as demographic characteristics, working memory, and math performance. The results indicated that (1) math anxiety only vary significantly between children with very low Early Numeracy Indicator (ENI) and high ENI levels; (2) a negative relationship between math anxiety and math performance exists; (3) there was no significant interaction between math anxiety and working memory; (4) a further examination on moderating effect found that only children who have been identified as being at risk for developmental dyscalculia and those with average or high working memory performed poorer in math at higher levels of math anxiety. Limitations and future directions are discussed.

This autoethnography was conducted at an elementary school not far from the South Texas border. I documented how my journey as a kindergarten, first-grade, and second-grade student has impacted me to become a better educator. Through this qualitative study, I planned to determine if my childhood experiences as an emergent bilingual learner have made an impact in my teaching and the connections I have formed with my students. I analyzed observations of my current teaching practices and my elementary report cards and test scores. After observing my students’ interactions among their peers and my reaction towards their conversations, I found that emergent bilinguals can reach academic achievement, and their learning can be enhanced. Embracing more than one language can be a powerful resource that binds students to a variety of cultures.