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Mastery of mathematical literacy is essential for developing life skills in the 21st century. Mathematical literacy is even more critical in elementary schools as it forms the basis for mastery at the junior and senior high school levels. Elementary school students differ in their characteristics from students at the higher education level. They, therefore, require an appropriate learning model to improve their mathematical literacy. This research aims to develop a learning model, termed Realistic Mathematics Engineering (RMEng), that combines the Realistic Mathematics Education approach with the steps of the Engineering Design Process and determines the model's effectiveness. The model was validated by seven experts from three universities in Indonesia and had an Aiken validity index value of 0.786, indicating that it was valid. The RMEng model contains the following steps: understanding realistic problems, solving problems in informal ways, developing formal mathematics, developing products, and drilling. Discussions and presentations can be incorporated into each of these five steps. The RMEng model was subjected to the stages of preliminary and main field testing and was revised based on the suggestions of teachers and observers. Through experimental research compared to a control group, the RMEng model was proven more effective in increasing elementary school students' mathematical literacy at the 0.000 significance level.

The rapid advancement of artificial intelligence technologies, exemplified by systems including Open AI's ChatGPT, Microsoft's Bing AI, and Google's Bard (now Gemini 1.5Pro), present both challenges and opportunities for the academic world. Higher education institutions are at the forefront of preparing students for this evolving landscape. This paper examines the current state of AI education in universities, highlighting current obstacles and proposing avenues of exploration for researchers. This paper recommends a holistic approach to AI integration across disciplines, fostering industry collaborations and emphasizing the ethical and social implications of AI. Higher education institutions are positioned to shape an educational environment attuned to the twenty-first century, preparing students to be informed and ethical contributors in the AI-driven world.

The background of this research is the low interest of children learning social studies, meanwhile, the purpose of this study was to increase interest and social studies learning achievement with the subject of growth and development of the national spirit with the Think Pair Share Pedagogic Criticle (TPSPC) learning model in class VIII students. This study used a class action research approach (classroom action research). The research was conducted at Kasihan 1 Public Middle School, Yogyakarta, with 34 students in class VIII A as the subjects. The data analysis technique in this study was to examine all available data, both qualitative and quantitative data from various sources, namely from observations, questionnaires, field notes, practice questions, quizzes, interviews, achievement tests, and documentation. The results of the study show that the TPSPC learning model can improve; (1) Students' interest in learning material grows and develops the national spirit of class VIII students from the percentage of students' initial interest before the action of 56% in the moderate category to 71.30% in the high category in cycle I, and increases to 74.70% in the middle category high in cycle II; (2) Student achievement in the matter of growing and developing the national spirit of class VIII students from the pre-action achievement average score of 63.5 with completeness reaching 44.12% (low category). After conducting research in cycle I, the average value of students became 72.66 with completeness reaching 65.62% (high category). In cycle II the average score of students was 89.35 with completeness reaching 94.12% (very high category); (3) The implementation of learning from observations of teacher activities was 88.88% in the very high category in cycle I to 97.22% in very high category in cycle II.

The 2013 Curriculum emphasizes the importance of incorporating ICT to improve learning effectiveness. One framework that is useful in this context is TPACK, which stands for Technological Pedagogical Content Knowledge. This research aims to examine the ability of geography teachers to utilize TPACK and to support teachers in implementing technology-based learning models with the guidance of 21st-century education. One specific type of online learning platform that can be used in this context is MOOC or Massive Open Online Course. The research employed a development research methodology and used the ADDIE model. The sample for need analysis consisted of 30 geography teachers from SMA/MA schools in West Sumatra, with the experiment school being SMA Negeri 1 Pariaman and SMA Negeri 1 Bukittingi. The results of the research indicate that the development of a TPACK-based MOOC learning model for SMA/MA Geography teachers in West Sumatra is both feasible and appropriate. This model effectively helped teachers to integrate the three main aspects of learning, namely content, pedagogy, and technology. The results of the geography teacher TPACK mapping show that the teachers are in the medium category. The implementation of the TPACK-based MOOC learning model also showed an increase in learning outcomes, with an N-gain score of 0.71 at SMA Negeri 1 Pariaman and 0.62 at SMA Negeri 1 Bukittinggi. The teachers and students involved in the study reported that the use of TPACK and MOOC learning facilitated asynchronous learning and virtual learning.

This study investigates the influence of coping strategies (problem-solving, emotion-focused, purpose-oriented) on academic resilience, considering gender and domicile differences. By analyzing data from 398 Indonesian students using MGA PLS-SEM, the study confirms satisfactory validity and reliability of the measurement models. The results reveal significant relationships between coping strategies and academic resilience. The study contributes to understanding the interplay between coping strategies and academic resilience across diverse groups, informing the development of targeted interventions and support systems for student well-being and success.

One of the essential goals of science learning is to lead students to master scientific concepts or ideas and apply them to explain relevant everyday phenomena. Such mastery should help students to work with various representations. The objective of this study was to determine the effectiveness of the Learning Cycle Multiple Representation (LCMR) model in students' mastery of concepts in plant physiology. This research was done using a quasi-experimental pretest-posttest design involving 62 students as the respondents. The concept mastery instrument consisted of 11 essay tests. The instrument met the value of validity and reliability. The data analysis was done using the one-way analysis of covariant set at a significance level of 5%. The results show that the LCMR model significantly affected students' mastery levels. The students who learned using the LCMR model showed significantly better mastery of the tested concept than the ones who used the LC model. The mastery score for the student learned using the LCMR model was 80.67, significantly higher (p=0.00 <[alpha]) than that obtained from the LC model of 54.00. Apart from enhancing mastery in plant physiology, the LCMR method can be expanded for other topics, and its effectiveness can be evaluated in future studies.

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.

When it comes to mastering the digital world, the education system is more and more facing the task of making students competent and self-determined agents when interacting with digital artefacts. This task often falls to computing education. In the traditional fields of computing education, a plethora of models, guidelines, and principles exist, which help scholars and teachers identify what the relevant aspects are and which of them one should cover in the classroom. When it comes to explaining the world of digital artefacts, however, there is hardly any such guiding model. The ARIadne model introduced in this paper provides a means of explanation and exploration of digital artefacts which help teachers and students to do a subject analysis of digital artefacts by scrutinizing them from several perspectives. Instead of artificially separating aspects which target the same phenomena within different areas of education (like computing, ICT or media education), the model integrates technological aspects of digital artefacts and the relevant societal discourses of their usage, their impacts and the reasons behind their development into a coherent explanation model.

In 2020, Science, Technology, Engineering and Math (STEM) faculty within a United Kingdom university implemented an online personal tutoring model. This model aimed to deliver a consistent experience to ~1000 students, supporting learning and fostering a sense of belonging during a global pandemic. Here we describe and evaluate a Curriculum Integrated personal tutor model, designed to provide a structured and dialogic student experience. We provide details on the timetabled activities, aligned to student development needs, and reflect on the model's effectiveness in supporting student learning, building belonging, and providing accessible delivery. We highlight strengths and explore weaknesses, providing recommendations for implementation of this type of model at other institutions.

Educators within post-secondary institutions receive input in the form of course evaluations from their students. The aim of receiving student input is to improve the teaching and learning experience for all. There are, however, inherent problems with the current methods of obtaining students' views through course evaluations. In this pilot study, the researchers focused on two problems: universally low response rates of 20% or less of student input in formal course evaluations and the problematic bias associated with anonymous course evaluations. Implementing practices that encourage students to provide course feedback, thus moving away from the term course evaluation was a first step to address these problems. A process was piloted in this study with 16 domestic undergraduate Bachelor of Science students whereby the researchers encouraged reflection, dialogue, and accountability in the new model and compared the differences against the problematic original model of receiving course evaluation input from students. [Articles in this journal were presented at the University of Calgary Conference on Postsecondary Learning and Teaching.]

The paper deals with the investigation of theoretical and practical issues on the organization and management of EFL peer learning. Lack of understanding of the mechanism of peer learning as a system of relationships and interactions hinders creating the conditions for effective education. The study aims to identify and systematize the key components of interactive learning of the English language, substantiate their significance in modeling peer learning. The scientific approach to the analysis of the concept "interactive learning" is applied. The systematic approach to the synthesis of the components of interactive peer learning is used as a basis for educational modeling. As a result, the components of interactive learning were identified and systematized in the form of peer learning principles, including student activity; educational material selected in compliance with student's knowledge; cyclical stages of a learning process; teacher's preparatory work; classroom peer tutoring. The principles were integrated into a dynamic model of peer learning whose driving factors are learning objectives, content, participants, design and management, control and assessment. The model ensures maximum student involvement in a learning process, increases motivation, develops language proficiency, manages activities and leads to efficient results.

This study was conducted to determine the opinions of elementary teachers about the child abuse and neglect education program developed according to the quantum learning model. This study was carried out with the participation of 20 elementary teachers working in the Yozgat province of Turkey. Quantitative and qualitative research methods were used together. Within the scope of the quantitative research, the "In-Service Training Programs Evaluation Scale" developed by Tekin and Yaman in 2008 was used. This scale had two factors and 28 items. The Cronbach's alpha coefficient of the scale in the original study was 0.95. Within the scope of the qualitative research, open-ended structured questions were asked to determine the opinions of elementary teachers on child abuse and neglect and the quantum learning model. Descriptive statistics and descriptive analysis were used in the analysis of the data. As a result of the analysis, it was determined that the opinions of teachers on the in-service training program were positive; they considered the quantum learning model as a physics-related model that developed the student in all aspects; they did not plan to implement the quantum learning model in the classroom; and they found it positive that the quantum learning model and child abuse and neglect were handled together. Teachers also reported that parents should participate in the training program on child abuse and neglect. The teachers acquired the learning outcome "Knows that he/she should call the Alo 183 social support line in case of child abuse and neglect" at the end of the training program. As a result of the study, recommendations were made regarding the nature of the courses to be organized for teachers, the arrangements that can be made in schools for the implementation of the quantum learning model, and the need to organize educational programs for parents.

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.

With the aim to provide teachers with more specific, frequent, and actionable feedback about their teaching, we explore how Large Language Models (LLMs) can be used to estimate "Instructional Support" domain scores of the CLassroom Assessment Scoring System (CLASS), a widely used observation protocol. We design a machine learning architecture that uses either zero-shot prompting of Meta's Llama2, and/or a classic Bag of Words (BoW) model, to classify individual utterances of teachers' speech (transcribed automatically using OpenAI's Whisper) for the presence of Instructional Support. Then, these utterance-level judgments are aggregated over a 15-min observation session to estimate a global CLASS score. Experiments on two CLASS-coded datasets of toddler and pre-kindergarten classrooms indicate that (1) automatic CLASS Instructional Support estimation accuracy using the proposed method (Pearson R up to 0.48) approaches human inter-rater reliability (up to R = 0.55); (2) LLMs generally yield slightly greater accuracy than BoW for this task, though the best models often combined features extracted from both LLM and BoW; and (3) for classifying individual utterances, there is still room for improvement of automated methods compared to human-level judgments. Finally, (4) we illustrate how the model's outputs can be visualized at the utterance level to provide teachers with explainable feedback on which utterances were most positively or negatively correlated with specific CLASS dimensions.

A common diagnosis in the literature is that the writing of Spanish speakers is generally a structural problem. The writing of 81 university students was analysed by classifying the teacher's comments based on 11 variables that were recorded once during a continuous evaluation that supported the assessment. The techniques used were Content Analysis, Cluster Analysis and Self-Study. Four Clusters were identified in which the dominant conception of communication is broken down, while one Cluster was identified with the alternative conception of communication. This expresses compliance with the task instructions, while the remaining Clusters show progressive non-compliance in various measures. The five Clusters form a typology and, with the theoretical assumption of the identification of the respective conceptions, correspond to a situated communication model. It could be applied in the planning of teaching workloads, as well as in the semi-automatic checking of written productions through the implementation of AI solutions.

Distance education institutions worldwide are adopting online learning to take advantage of its benefits. However, online learning is often seen as a mode of delivery that will work for any distance education institution in any context. The increased relevance of online learning during and after the COVID-19 pandemic has further exaggerated the adoption of online learning, which requires ongoing research on the actual use of online learning, and this study responds to this need. Therefore, this study aimed to determine how online learning is used in an open distance learning (ODL) context. A case study on the implementation of online learning was conducted at an ODL university in South Africa. Data were collected through open-ended questionnaires, interviews, and non-participant online observation. Participants were purposefully selected academics from various colleges in the university. Findings reveal that awareness of educational choices in the online environment appeared to be neglected. Based on the findings, the study presents a framework for online learning in ODL. It is built on the concept of awareness, and we argue that the integration of online learning should be carefully curated and planned with intentional institutional, lecturer, and student awareness. The principles of the theories of transactional distance, the community of inquiry, social constructivism and heutagogy should guide the operationalisation of each type of awareness presented in the framework.

Purpose: This study aims to develop a teaching model of the Malay language for non-native and foreign speakers in schools. Implementing this model can assist educators in teaching the Malay language to students who do not use it as their first language in everyday use. Methodology: This study used a qualitative approach to develop teaching models for non-native and foreign Malay speakers. Interviews were conducted with 27 teachers and 23 stakeholders who are experts in teaching the Malay language, especially in teaching non-native speakers. A prototype model was developed thematically as a result of interviews using ATLAS.ti 22. Further, the prototype model was used by ten teachers from primary, secondary, and international schools in their teaching. Findings: This study has succeeded in developing a model for teaching the Malay language to non-native speakers and foreigners that includes five elements: teaching strategies, individual differences, input, social, and motivation. This model strongly emphasises instruction involving interaction, communication, and bilateral relations as a medium for students' language acquisition and meaningful input. Verbal interaction between teachers and students can stimulate and improve students' language skills in learning Malay. Significance: This model can help teachers teach the Malay language to non-native speakers and foreigners with a greater focus on achieving learning objectives. This model is expected to expand the usage, cultivate interest, and facilitate teaching the Malay language in educational institutions.

This text critically examines traditional methodologies for teaching grammar in the context of Portuguese as a Foreign Language (PFL). It proposes an alternative, eclectic approach, which can be used in any foreign language. Drawing on evidence from research and pedagogical practices, it highlights areas for improvement in current approaches, such as the decontextualization of linguistic structures and insufficient gradual progression in exercises. The paper suggests a teaching framework based on Motivating-Grammaring-Applying (MGA), aiming to embed grammar learning in meaningful, communicative contexts and develop grammatical proficiency beyond mere rule memorization. The MGA model foregrounds the importance of authentic texts, student-centric discovery learning, and the application of learned structures in diverse communicative tasks. The paper further emphasizes the potential of digital tools and the importance of integrating grammar learning with other linguistic and cultural skills. Ultimately, this study encourages PFL teachers to diversify their pedagogical strategies and reconsider the role of grammar teaching in language learning, aiming at empowering students for effective communication in real-world contexts.

This research aimed to: 1) investigate the components and performance indicators for organizing experiential activities that foster life skills among early childhood students, particularly those taught by teachers under the Office of the Basic Education Commission of Thailand; 2) examine the current state, desired state, and the needs of supervision models for teachers to develop experiential competency that promotes life skills in early childhood students; and 3) enhance the supervision models for developing experiential competency, specifically focusing on promoting life skills among early childhood students taught by teachers under the Office of the Basic Education Commission of Thailand. The research is divided into 3 phrases. Phase 1: examine the components and indicators, targeting a qualified group of 9 experts selected through purposive sampling. This selection is based on the appropriateness assessment using the components and indicators evaluation form. Phase 2: investigate the needs of the supervision models. The sample group comprises 320 early childhood teachers, selected through multi-stage random sampling using a questionnaire with a 5-level Likert scale. Phase 3: developing the supervision models. Nine qualified experts selected through purposive sampling examined the appropriateness of the supervision models. The data were collected through questionnaires, interviews, assessments, and observations. Average, standard deviation and Priority Needs Index (PNImodified) were used to analyze the data. The findings of the study revealed the following: (1) The components and indicators of life skills serve as standards for teachers in organizing experiences that promote life skills for early childhood students. There are 5 components and 23 indicators, categorized as follows: 1) Decision-making with 5 indicators, 2) Problem-solving with 5 indicators, 3) Analytical thinking with 4 indicators, 4) Empathy with 4 indicators, and 5) Communication with 5 indicators. Overall, the proficiency level is rated as the highest. (2) The current state is rated as the highest, and the desired state is also at a high level. When assessing the needs of the supervision models, it is found that the component with the highest need is Component 3--Analytical Thinking. This is followed by Component 2--Problem-Solving, Component 4--Empathy, Component 1--Decision Making, and Component 5--Communication, with the lowest index of essential needs respectively. (3) The results of enhancing the supervision models for teachers to develop experiential competency that promotes life skills in early childhood students reveal that the supervision models encompasses five formats: 1) Preparation: Planning and development, 2) Strengthen Relationships and Increase Knowledge: Building relationships and expanding knowledge, 3) Knowledge into Practice: Applying knowledge into practice, 4) Reflection: Reflecting on outcomes for understanding, and 5) Evaluation: Quality measurement and assessment. These formats are deemed highly suitable, appropriate, and effective.

Despite extensive research on feedback models, there is still sparse empirical evidence of their validity and application in higher education learning settings, whether online, hybrid, or face-to-face. Understanding how a feedback framework--integrated in the instructional cycle--is perceived by the learners can provide empirical support about its intended purpose and effectiveness. Recent reviews of research on student perceptions of feedback in different learning environments have revealed the need to research feedback as a process with the use of a more solid methodological approach. The aim of the present descriptive survey study is to investigate learners' perceptions of an established feedback model, a Matrix of Feedback for Learning (Brooks et al., 2019), in the asynchronous online component of an undergraduate blended course. More specifically, the impact of its application on learners' encounters with three types of feedback (feed-up, feed-back and feed-forward) and three levels of feedback (task, process and self-regulatory) is explored. A 36-item survey, previously piloted and preliminarily validated, was used to explore learners' perceptions (N = 135) of the three feedback types and levels. Approximately 68% of responses showed that students recognized feedback and the possibilities to use and act on feedback as helpful to their learning. This present survey supports a line of research on feedback model validation in online learning environments while offering meaningful insights from the learners that can inform both course design and interventions to support engagement with feedback.

The arrival of generative artificial intelligence (AI) is fundamentally different from prior technologies used in educational settings. Educators and researchers of online, blended, and in-person learning are still coming to grips with how to employ current AI technologies in the learning experience, let alone understanding the potential consequences that future and unknown developments in AI will produce. Despite potential risks, AI may revolutionize previous models of teaching and learning, and perhaps create opportunities to realize progressive educational goals. Given the longstanding tradition of using philosophy to examine questions surrounding ethics, ontology, technology, and education, the purpose of this critical reflection paper is to draw from prominent philosophers across these disciplines to address the question: how can AI be employed in future educational contexts in a humanizing and ethical manner? Drawing from the work of Gunther Anders, Michel Foucault, Paolo Freire, Benjamin Bloom, and Hannah Arendt, we propose a framework for assessing the use and ethics of AI in modern education contexts regarding human versus AI generated textual and multimodal content, and the broader political, social, and cultural implications. We conclude with applied examples of the framework and implications for future research and practice.

Student satisfaction is a key performance indicator in evaluating any degree program's performance. In light of the vast difference between online and traditional degree programs, factors that may significantly affect student satisfaction and thus contribute to the success of online degree programs still need to be explored. Previous literature on student satisfaction either focused on the course level or researched the factors for traditional face-to-face degree programs. This study shifts the focus to online degree programs by integrating the existing literature and proposing a new conceptual framework for evaluating online degree programs. The proposed conceptual framework includes six big categories of factors and three outcome variables related to student satisfaction. The theoretical underpinning of the conceptual framework was supported by a comprehensive literature review regarding each of the six factors. Data were collected from two online engineering degree programs in a large public university to assess the underlying relationships and identify the key factors affecting student satisfaction. This research contributes to the existing literature in the following four aspects: 1) it integrates the existing literature and proposes a new framework for evaluating online degree programs; 2) it identifies critical factors for evaluating online degree programs through student satisfaction; 3) it extends the definition and construct of student satisfaction, and assesses the construct from three dimensions; and 4) it provides suggestions to the policymakers such as school administrators and accreditation bodies. [Note: The page range (1-37) shown on the PDF is incorrect. The correct page range is 202-238.]

We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most influential in dropout prediction. To evaluate its effectiveness, we compared the predictive performance of our hybrid model with two other survival analysis models: the piecewise exponential model and a hybrid model combining a fully-connected neural network with a piecewise exponential model. Additionally, we compared it to five other cross-sectional models: Ridge Logistic Regression, Lasso Logistic Regression, CART decision tree, Random Forest, and XGBoost decision tree. Our findings demonstrate that the hybrid model outperforms or performs comparably to the other models when predicting dropout among students at the University of Delaware in Spring 2020, Spring 2021, and Spring 2022. Moreover, by categorizing predictors into three distinct groups--academic, economic, and social-demographic--we discovered that academic predictors play a prominent role in distinguishing between dropout and retained students, while other predictors may indirectly influence predictions by impacting academic variables. Consequently, we recommend implementing an intervention program aimed at identifying at-risk students based on their academic performance and activities, with additional consideration for economic and social-demographic factors in customized intervention plans.

In higher education, academic advising is crucial to students' decision-making. Data-driven models can benefit students in making informed decisions by providing insightful recommendations for completing their degrees. To suggest courses for the upcoming semester, various course recommendation models have been proposed in the literature using different data mining techniques and machine learning algorithms utilizing different data types. One important aspect of the data is that usually, courses taken together in a semester fit well with each other. If there is no correlation between the co-taken courses, students may find it more difficult to handle the workload. Based on this insight, we propose using session-based approaches to recommend a set of well-suited courses for the upcoming semester. We test three session-based course recommendation models, two based on neural networks (CourseBEACON and CourseDREAM) and one on tensor factorization (TF-CoC). Additionally, we propose a post-processing approach to adjust the recommendation scores of any base course recommender to promote related courses. Using metrics capturing different aspects of the recommendation quality, our experimental evaluation shows that session-based methods outperform existing popularity-based, association-based, similarity-based, factorization-based, neural networks-based, and Markov chain-based recommendation approaches. Effective course recommendations can result in improved student advising, which, in turn, can improve student performance, decrease dropout rates, and a more positive overall student experience and satisfaction.

Research about the optimal methodologies for guiding the training of primary school instructors in the instruction of early algebra remains in the process of determination. This manuscript delineates the methodology involved in developing and validating an assessment tool to evaluate the mathematical acumen of prospective primary educators in the domain of early algebra during their initial pedagogical training, drawing upon the constructs delineated in the Mathematical Knowledge for Teaching (MKT) model. To this end, an instrumental inquiry comprising four distinct phases has been executed: an exhaustive review of extant literature concerning the mathematical proficiency of primary school instructors and the pedagogy of early algebra; formulation of the preliminary version of the assessment instrument; validation of said instrument via expert appraisal and a preliminary application involving ten pre-service primary educators enrolled in a Spanish academic institution; and subsequent refinement and finalization of the assessment tool. This endeavor's outcome culminated in the MKT-Early Algebra Questionnaire (6-12-year-olds), comprising six open-ended inquiries meticulously designed to explore diverse facets of early algebra while aligning with the various sub-domains delineated in the MKT model. It is deduced that the resultant instrument holds promise as an effective diagnostic apparatus, serving dual purposes: elucidating the mathematical proficiency of primary school educators in the context of early algebra and fostering introspection regarding pedagogical strategies conducive to the cultivation of algebraic cognition at this developmental juncture. By furnishing a comprehensive questionnaire that systematically addresses all facets of pedagogical knowledge requisite for the effective instruction of early algebra, this study furnishes invaluable insights into the specific components that should be integrated into teacher education curricula in the domain of algebraic didactics.

Motivation to learn mathematics decreased due to the inability of teachers to implement innovative learning models and techniques. Therefore, this study aimed to investigate the effects of teachers' ability on students' motivation to learn mathematics by using quantitative methods and survey approaches. There were 32 mathematics teachers and 542 students in the 24 schools within the Depok region, selected as respondents through a stratified random sampling method. The research instruments of two questionnaires of teachers' competence and students' learning motivation were distributed to the respondents. Data analysis was conducted to test the random effect of teachers' ability on students' motivation to learn mathematics by using the effect of teachers' random intercepts and competence as models 1 and 2, respectively. These two models were analyzed using the n-level Structural Equation Model (nSEM), and the result showed that model 2 was the best one to investigate the random effect of teachers' ability and students' learning motivation. The data analysis showed that the variance among teachers' ability (0,0027) was less than learning motivation among students (0.0597). These findings indicated that the motivation levels of students taught by the same teacher varied significantly, whereas the effects of the teachers were relatively homogeneous. In other words, teachers' ability was somewhat the same in increasing students' learning motivation. Based on these findings, this research work suggests teachers keep improving their teaching techniques. Hence, students will be well motivated to learn so that the learning objectives will be well achieved.

The aim of the study is to identify variables that explain students' academic performance, determine their relative importance, and consequently, develop an index to distinguish advantaged and disadvantaged schools in pursuit of educational equality. By using this index, we intend to build a model for evaluating schools' overall performance based on their equality of opportunity. The research is structured within the quantitative research paradigm and the relational research design has been adopted. The research was carried out in 52 secondary schools and analysis were performed on data collected from 1143 students, 1600 teachers and 141 school administrators. While collecting the data, a monitoring exam was used to measure student achievement, which is the dependent variable. Data on independent variables were collected with student, teacher, and administrator surveys. Hierarchical linear modelling and ratio analysis were employed while analysing the data. It was found that student-level variables explain success more than school-level variables. Twenty different variables in total at the student and school level were found to be effective on student achievement. Based on these variables, a school advantages index was created. It has been observed that schools with a high school advantage index are also better off in terms of average school achievement. However, when evaluated together with the school advantages index, it was revealed that the effectiveness order of the schools changed. Our suggestion is that education systems need to have a more holistic approach to evaluate school performances. To provide both fair and radical equality of opportunity, each school should chart a course considering its own conditions regarding the education of disadvantaged students.

Identifying motivation for enrollment in MOOCs has been an important way to predict participant success rates. But themes for motivation have largely centered around themes for enrolling in any MOOC, and not ones specific to the course being studied. In this study, qualitatively coding discussion forums was combined with topic modeling to identify participants' motivation for enrolling in two successive statistics education professional development online courses. Computational text mining, such as topic modeling, is a learning analytics field that has proven effective in analyzing large volumes of text to automatically identify topics or themes. This contrasts with traditional qualitative approaches, in which researchers manually apply labels (or codes) to parts of text to identify common themes. Combining topic modeling and qualitative research may prove useful to education researchers and practitioners in better understanding and improving online learning contexts that feature asynchronous discussion. Three topic modeling approaches were used in this study, including both unsupervised and semi-supervised modeling techniques. The three topic modeling approaches were validated and compared to determine which participants were assigned motivation themes that most closely aligned to their posts made in an introductory discussion forum. A discussion of how each technique can be useful for identifying topical themes within discussion forum data is included. Though the three techniques have varying success rates in identifying motivation for enrolling in the MOOCs, they do all identify similar themes for motivation that are specific to statistics education.

Counselors and educators must re-imagine social-emotional wellness as an essential part of the academic curriculum. The authors present a collaborative, sustainable model that focuses both on academic rigor and emotional wellness. The authors outline how this model can be integrated in any academic content area and how school counselors and teachers can support each other to meet academic and social-emotional learning outcomes. The authors demonstrate how this model can be integrated into high school math.

This meta-synthesis reviewed and synthesized the findings of 53 studies on supervisory post-conferences and feedback with a total of 807 participants published between 2004-2023. The aim of this meta-synthesis was to illustrate the trends in the reviewed studies and synthesize the results of the studies on supervisory discourse and models of supervision in the field of English language teaching. The criteria for eligibility for selection were being empirical, peer-reviewed, and published in English. Articles were scanned through the Web of Science, ERIC, SCOPUS, and Google Scholar till July, 2023. The studies lacking a report of detailed and clear-cut data collection and analysis phases were removed in the appraisal phase to avoid a risk of bias. The results indicated that supervisors implemented directive supervisory styles as well as collaborative approaches exploiting power dynamics such as expert power. The findings also showed that the use of conversational techniques such as mediation, mitigators, and elicitation in supervisory talk play a central role in supervising English language teachers. The results suggested that supervised teachers demonstrated confronting, autonomous, and fluid identities when faced with a directive style of supervision. The limitations of evidence for this study related the search strategy, participants and variations in educational settings.

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.

Open and distance learning (ODL) activities aim to meet the expectations and needs of different individuals, societies, and systems by ensuring the continuation of learning with a lifelong learning philosophy and an egalitarian policy for everyone, regardless of time and place. Support services, which address the differentiated expectations and needs in all learning-related activities, serve the learning processes and the various stakeholders involved. Therefore, technical support services are included in support activities that require the use of technology as a tool, mediator, or technique in learning processes. In this study, ODL systems that are situated in digital space are examined within the scope of technical support services in line with the threats and dangers of the developing and expanding digital space. The aim is to create a technical support service model based on Herbert Simon's Bounded Rationality Theory and Otto Peters' Industrialization Theory, to ensure cybersecurity in ODL systems. In this study, which adopted a case study as one of the qualitative research methods, documents in the literature were examined first. Considering the literature, the opinions of eight field experts were gathered by ten interview questions. After the semi-structured interviews were transcribed, the data obtained were divided into codes and themes through thematic analysis. The findings stressed the importance of institutional culture, distribution of tasks and responsibilities, administrative support, institutional awareness, cybersecurity training, and the use of different technologies to ensure cybersecurity in ODL systems. Additionally, the experts emphasized a common view that smart systems should be used in the provision and maintenance of cybersecurity.

Various areas of educational research are interested in the transitions between different states--or events--in sequential data, with the goal of understanding the significance of these transitions; one notable example is affect dynamics, which aims to identify important transitions between affective states. Unfortunately, several works have uncovered issues with the metrics and procedures commonly used to analyze these transitions. As such, our goal in this work is to address these issues by outlining an alternative procedure that is based on the use of marginal models. We begin by looking at the specific mechanisms responsible for a recently discovered statistical bias with several metrics used in sequential data analysis. After giving a theoretical explanation for the issue, we show that the marginal model procedure appears to adjust for this bias. Next, a related problem is that the common practice of removing transitions to repeated states has been shown to have unintended side-effects--to account for this issue, we develop a method for extending the marginal model procedure to this specific type of analysis. Finally, in a recent study evaluating the problem of multiple comparisons and sequential data analysis, the Benjamini-Hochberg (BH) procedure, a commonly used approach to control for false discoveries, did not perform as expected. By applying a technique from the biostatistics and epidemiology literature, we show that the performance of the BH procedure, when used with the marginal model method, can be brought back to its expected level. In all of our analyses, we evaluate the proposed method by both running simulations and using actual student data. The results indicate that the marginal model procedure seemingly compensates for the problems observed with other transition metrics, thus resulting in more accurate estimates of the importance of transitions between states.

Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge concepts and linguistic skills are hard to define, and diagnosis must be well-designed. Traditional approaches are widely applied for modeling knowledge in science or mathematics, where skills or knowledge concepts are easy to associate with each item. However, only a few works focus on defining knowledge concepts and skills using linguistic characteristics for language knowledge proficiency diagnosis. In addressing this, we propose a framework for language proficiency diagnosis based on neural networks. Specifically, we propose a series of methods based on our framework that uses different linguistic features to define skills and knowledge concepts in the context of the language learning task. Experimental results on a real-world second-language learning dataset demonstrate the effectiveness and interpretability of our framework. We also provide empirical evidence with comprehensive experiments and analysis to prove that our knowledge concept and skill definitions are reasonable and critical to the performance of our model.

Individuals from diverse minority backgrounds in the United States represent a one-of-its-kind collaboration in understanding diversity, equity, inclusion, and belonging (DEIB) challenges in workforce staffing and representation per Equal Opportunity norms. This study involved primary and secondary research in the realm of DEI to develop an implementable model from lessons gleaned from the findings of the research and gaps found in contemporary hiring and staffing practices. The study and model development are grounded in Rawls's 1971 theory of justice and predicated on the premise that opportunities are usually based on merit, and are generally well-intended, although often constrained by the frailties of human nature. The United States is in dire need of practical solutions, for despite well-intended U.S. laws and mandates on employment, equitable hiring practices are far from satisfactory. In a changing world order, many employers in the United States, and even to some extent globally, acknowledge they are duty-bound to ensure a harmonious, equitable, and safe work environment, devoid of any form of discrimination, harassment, or intimidation. Although easier said than done, achieving this equality requires a systematic approach to investigative organizational/institutional auditing, mapping, and diagnosing the ills that constrain collaboration and reduce productivity. The model is based on research and strategic practitioner design in its orientation, with practical implementation utility, flexible and customizable specifically for DEIB initiatives change, and suitable for educational and other industry settings. A skilled and scientific approach is needed to ensure equitable practices in professional and academic spheres.

The models of E-learning Readiness (ELR) are basically designed to understand the process of obtaining the basic information necessary for measuring ELR among participants. They help organizations to identify the requirements for designing, developing and implementing E-learning. These models not only help the organizations to identify the degree of ELR but also to identify the factors or areas where the organization is weak. The present study reviews the models of E-learning Readiness (ELR) with the help of a small-scale literature review on Models of ELR. The purpose of the current study was to make an analysis of the existing ELR models in the literature. The researcher visited various online databases to collect studies. In addition to that, the researcher also did a manual search in the reference list of various studies in the related field for the addition of additional studies. By following an appropriate strategy, the researchers selected 07 ELR models for the current study. The current study reveals that Technology, Skills, Attitude, resources and human resources, Infrastructure, Flexibility, Support, Pedagogy, Management, Evaluation, Continual Improvement, Culture, Budgeting, Curriculum, Awareness, Law and Regulation, Content and Policy have been used on a larger scale for the development of the ELR models. Further, majority of reviewed studies have used various statistical techniques like regression analysis, Confirmatory Factor Analysis (CFA) and Exploratory Factor Analysis (EFA) for developing the models.

This study investigated the effects of 3D model building activities with block codes on students' spatial thinking and computational thinking skills. The study group consists of 5th grade students in a secondary school in the Central Anatolia region of Turkey. For the study, a pretestposttest control group was utilized within the experimental design. A total of 66 students participated, 23 in the experimental group and 43 in the control group. While the activities prepared on the Tinkercad platform were applied in the experimental group, the courses were taught using the traditional teaching method in the control group. The study covers a period of three-weeks in the course information technologies and software. The study used the computational thinking levels scale and spatial thinking test scales as data collection instruments. The data was analyzed using both descriptive statistics and independent samples t-tests. Based on the study findings, there were no significant differences observed in the levels of computational thinking skills levels and spatial thinking test scores between the experimental and control groups.

This research aims to evaluate the Inspirational School Program using the CIPP (Context, Input, Process, Product) model, focusing on providing insights and recommendations for enhancing its effectiveness and sustainability. Data collection methods in qualitative descriptive research include interviews, observation, and documentation. The study's sample population comprises individuals associated with SMA Negeri 5 in Banjarmasin, including educators, school leaders, and grade XI students. In-person interviews were conducted with the school principal, homeroom teacher, counselling teacher, and students. Data analysis techniques encompass data collection, reduction, presentation, and verification/conclusion. The results indicate that the school's needs align with the Ministry of Education and Culture guidelines. Despite limited local government support, the program planning and evaluation processes have been implemented effectively despite obstacles. The Product Evaluation Stage reflects positive outcomes, including literacy, numeracy, character development, and school safety climate improvements. Educator and staff competence have also seen enhancements. It is recommended to align implementation with the Independent Curriculum, bolster human resources through educator and staff training, enhance infrastructure support, and seek increased government assistance to sustain and improve the program. These measures aim to enhance productivity further and ensure the continued success of the Inspirational School Program.

BACKGROUND: School discipline has potential life-long consequences for students. Disabled youth can be misunderstood and experience harsh discipline and are at increased risk for negative outcomes, yet little research includes their voices. The aim of this study was to explore past school discipline experiences among disabled adults. METHODS: Disabled adult perspectives (N = 9) regarding past school discipline experiences were explored employing qualitative descriptive methodology framed by the Peace and Power Conceptual Model. Peace-Power versus Power-Over-Powers involve actions/behaviors reflecting critical emancipation or oppression of those in power. RESULTS: Eight peace-power versus power-over themes emerged: "humiliating," "threatening," "escaping," "observing," "avoiding," "diverging," "isolating," and "failing." IMPLICATIONS FOR SCHOOL HEALTH POLICY, PRACTICE, AND EQUITY: Disabled students can experience oppressive power-over dynamics separating them from others because they are unable to meet rigid classroom expectations. Educators may lack awareness of structural biases shaping reactions warranting need for school policies and practices promoting restorative justice, social-emotional learning, and inclusion. CONCLUSIONS: Classroom discipline can represent emancipatory peace-power or oppressive power-over powers. Not all teacher interactions represent 1 power or the other. Future research should focus on including disabled student voices informing school discipline practices. Educators' perspectives of their role fostering inclusive classrooms should also be considered.

This paper studies the knowledge developed by a group of sixty-two pre-service primary teachers during a training session on the definition of a polygon and the mathematical practice of defining. We explore the knowledge developed by pre-service teachers when they carry out a series of training tasks oriented around the practice of defining with primary pupils, within the theoretical framework of the Mathematics Teachers' Specialised Knowledge model, and using videos of an authentic lesson as prompt. Data were collected by means of video recordings of the implementation of the training tasks, in conjunction with the pre-service teachers' own written observations as they watched the recorded lesson. A content analysis was carried out on the collected data using the Mathematics Teachers' Specialised Knowledge model. The results highlight the way in which knowledge of the mathematical practice of defining is constructed, along with the mathematical objects involved in this practice, and pedagogical aspects of defining with young pupils. They also illustrate how a sequence of tasks oriented around a model of teachers' knowledge can contribute to the development of this knowledge in initial training programmes, and more especially provide orientation about the training of pre-service teachers in the mathematical practice of defining.

Discrete-time survival analysis (DTSA) models are a popular way of modeling events in the social sciences. However, the analysis of discrete-time survival data is challenged by missing data in one or more covariates. Negative consequences of missing covariate data include efficiency losses and possible bias. A popular approach to circumventing these consequences is multiple imputation (MI). In MI, it is crucial to include outcome information in the imputation models. As there is little guidance on how to incorporate the observed outcome information into the imputation model of missing covariates in DTSA, we explore different existing approaches using fully conditional specification (FCS) MI and substantive-model compatible (SMC)-FCS MI. We extend SMC-FCS for DTSA and provide an implementation in the smcfcs R package. We compare the approaches using Monte Carlo simulations and demonstrate a good performance of the new approach compared to existing approaches.

Deep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt and valuable feedback on their teaching practices. However, these deep learning models often have intricate structures with numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding their classroom dialogue analysis likely leads teachers to distrust and underutilize these AI-powered models. To tackle this issue, we leveraged explainable AI to unravel classroom dialogue analysis and conducted an experiment to evaluate the effects of explanations. Fifty-nine pre-service teachers were recruited and randomly assigned to either a treatment (n = 30) or control (n = 29) group. Initially, both groups learned to analyse classroom dialogue using AI-powered models without explanations. Subsequently, the treatment group received both AI analysis and explanations, while the control group continued to receive only AI predictions. The results demonstrated that teachers in the treatment group exhibited significantly higher levels of trust in and technology acceptance of AI-powered models for classroom dialogue analysis compared to those in the control group. Notably, there were no significant differences in cognitive load between the two groups. Furthermore, teachers in the treatment group expressed high satisfaction with the explanations. During interviews, they also elucidated how the explanations changed their perceptions of model features and attitudes towards the models. This study is among the pioneering works to propose and validate the use of explainable AI to address interpretability challenges within deep learning-based models in the context of classroom dialogue analysis.

Connections between prosociality and antisocial behaviors have been recognized; however, little research has studied their developmental links longitudinally. This is important to illuminate during early adolescence as a sensitive period for social development in which prosociality could protect against the development of later antisocial behaviors. This study investigates the within-person developmental links between prosociality and antisocial behaviors, as well as a potential mediating role of peer relationships, across ages 11, 13, and 15 (N = 1526; 51% male) using random-intercept cross-lagged panel models. Results indicated that neither self-reported nor teacher-reported prosociality was associated with reduced aggressive behaviors but suggested a direct protective ('promotive') effect of teacher-reported prosociality on bullying perpetration. These findings suggest that promoting prosociality in early adolescence may help reduce some antisocial behaviors over early to mid-adolescent development. Improving prosociality could be explored as a target in intervention approaches such as school-based anti-bullying interventions.

Design thinking dispositions are essential for students to understand why design thinking knowledge should be applied to perform specific tasks. However, few studies are focused on teaching design thinking dispositions. This study proposes a conceptual model that supports teaching design thinking dispositions to address this gap. The model was instantiated in an undergraduate course. Students' reflections about the course were collected to evaluate the model. The use of the model is also demonstrated by considering different teaching scenarios. This study contributes to better teaching and learning design thinking dispositions based on a unique model that helps educators organize their design thinking courses. The study also derives some implications for educators. While teaching design thinking knowledge and skills is essential, developing students' design thinking dispositions is equally essential.

Selecting undergraduate majors or specializations is a crucial decision for students since it considerably impacts their educational and career paths. Moreover, their decisions should match their academic background, interests, and goals to pursue their passions and discover various career paths with motivation. However, such a decision remains challenging are unfamiliar with the job market, the demand for the required skills, and being in the proper placement in a major is not straightforward. Thus, an automatic recommendation system can be helpful for students to assist and guide them in the right decision. In this context, we developed a machine learning model to predict and recommend suitable specializations for undergraduate students according to the job market and student's academic history. Two hundred twenty-five records of students are considered to establish this work. The proposed approach encompasses four major steps, including data preprocessing to clean, scale, and prepare the data for training to avoid obtaining suboptimal results, accompanied by an oversampling process to equal the samples' distribution to prevent the model from being biased or poorly generalized. Furthermore, we conducted a feature selection step using Sequential Backward Selection (SBS) to extract the relevant features to improve the outcomes and reduce the risk of noise. The selected subset is used to train the model using the AdaBoost classifier. We deployed the Genetic algorithm to optimize the classifier's hyperparameters to maximize results. As a result, the findings of this study exhibit noticeable results compared to existing models, with an accuracy of 98.1%. The proposed model can be reliable in guiding undergraduate students through proper decisions regarding selecting their major.

We propose a simple computational model that describes potential mechanisms underlying the organization and development of the lexical-semantic system in 18-month-old infants. We focus on two independent aspects: (i) on potential mechanisms underlying the development of taxonomic and associative priming, and (ii) on potential mechanisms underlying the effect of Inter Stimulus Interval on these priming effects. Our model explains taxonomic priming between words by "semantic feature overlap," whereas associative priming between words is explained by Hebbian links between semantic representations derived from "co-occurrence relations" between words (or their referents). From a developmental perspective, any delay in the emergence of taxonomic priming compared to associative priming during infancy seems paradoxical since feature overlap "per se" need not be learned. We address this paradox in the model by showing that "feature overlap" itself is an emergent process. The model successfully replicates infant data related to Inter Stimulus Interval effects in priming experiments and makes testable predictions.

Dropout refers to the phenomenon of students leaving school before completing their degree or program of study. Dropout is a major concern for educational institutions, as it affects not only the students themselves but also the institutions' reputation and funding. Dropout can occur for a variety of reasons, including academic, financial, personal, and social factors. Therefore, understanding the factors that contribute to dropout and developing effective strategies to prevent it is a critical challenge for educational institutions. In this study, we propose a hybrid deep learning model based on Long Short-Term Memory and Deep Neural Network algorithms for school dropout prediction. The proposed model was compared with previous works and several other machine learning algorithms, including Deep Neural Network (DNN), K-Nearest Neighbors (KNN), Naive Bayes (NB), Multi-Layer Perceptron (MLP), Decision Trees (DT), Support Vector Machine (SVM), and Random Forest (RF). The results showed that the proposed DNN-LSTM model outperforms the other models in terms of accuracy and efficiency.