Teachers' language ideologies shape their classroom language policy-making, which in turn shapes students' opportunities to learn. Attention to language ideologies is therefore critical for teacher educators who seek to support pre- and in-service teachers in becoming pro-multilingual policymakers. This mixed methods, survey-based study explores…

This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students' current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to…

Utilizing facial recognition technology, the current study has attempted to predict the likelihood of student conceptual change with decision tree models based on the facial micro-expression states (FMES) students exhibited when they experience conceptual conflict. While conceptual change through conceptual conflicts in science education is a…

Considering the large volume of PISA data, it is expected that data mining will often be assisted in making PISA data more meaningful. Studies show that different dimensions of ICT may reveal different relationships for mathematics achievement. The purpose of this article is to evaluate the success of the decision tree classification algorithms in…

Technological advances such as Learning Management System (LMS) are changing the teaching-learning conditions, organization of school activities and functions of educators. In particular, the use of LMS in the educational field is necessary due to the appearance of the SARS-CoV-2 virus. This quantitative research analyzes the teachers' perception…

In the identification process, there may be gifted students who may be unnoticed or students who are misdiagnosed and are disappointed. In this context, this study is a step that may solve these two problems about the identification of mathematically gifted students with the help of data mining, which is data analysis methodology that has been…

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…

This paper analyzes the dataset collected from students participating in the Boy With A Ball (BWAB) program, a faith-based community outreach group, through the Hemingway Measure of Adult Connectedness©, a questionnaire measuring the social connectedness of adolescents. This paper first approaches the data in the conventional method provided by…

This study aimed to predict the culturally responsive teacher roles based on cultural intelligence and self-efficacy using machine learning classification algorithms. The research group consists of 415 teachers from different branches. The Bayes classifier (NaiveBayes), logistic-regression (SMO), lazy-classifier (KStar), meta-classifier…

Individualization and iterative design are essential components of the assessment and treatment of challenging behavior. Currently, there are few validated frameworks for engaging in iterative processes. Due to the nature of single-case design, empirically rigorous evaluations of decision-tree processes are particularly prohibitive.…

Learners' adaptation to academic trajectory is shaped by several influencing factors that ought to be considered while attempting to design an intervention towards improving academic performance. Emotion is one factor that influences students' academic orientation and performance. Tracking emotions in text by psychologists have long been a subject…

Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decisions. Identifying potential at-risk students may help instructors and academic guidance to improve the students' performance and the achievement of learning outcomes. The aim of this research study is…

This research focuses on students' satisfaction and on how students' satisfaction relates to their performance and involvement in study activities in the e-classroom. Our research is a case study at the course level of a business and economics study programme at a private higher education institution in Slovenia. The study is based on…

Classification and Regression Tree (CART) analysis is a statistical modeling approach that uses quantitative data to predict future outcomes by generating decision trees. CART analysis can be useful for educators to inform their decision-making. For example, educators can use a decision tree from a CART analysis to identify students who are most…

This case uses data analytic techniques to expose students to the context of the initial coin offering marketplace. The exercise is well-suited as a group activity in an undergraduate or graduate business analytics course in which students have been taught analytic techniques such as word cloud, descriptive statistics, basic visualizations, and…