Based on the theory of integrated domains in epistemology, the question of domain specificity of epistemic beliefs was investigated from a comprehensive perspective. We examined intraindividual differences in epistemic beliefs about the natural, mathematical, social, and linguistic sciences that represented almost the entire spectrum of subjects…

Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The…

Regression Tree Method is not yet a mainstream method in Education, despite of being a traditional approach in Machine Learning. We advocate that this method should become mainstream in Education, since, in our point of view, it is the most suitable method to analyse complex datasets, very common in Education. This is, for example, the case of…

This paper outlines the development of a comparative research framework in subject didactics and applies this in the process of analysing the transformations from academic disciplines across different school subjects. The theoretical framework builds on the concepts of 'powerful knowledge' and 'transformation' and 'epistemic quality' within which…

Can sentiment analysis be used in an educational context to help teachers and researchers evaluate students' learning experiences? Are sentiment analyzing algorithms accurate enough to replace multiple human raters in educational research? A dataset of 333 students evaluating a learning experience was acquired with positive, negative, and neutral…

Peer assessment is a method that has shown a positive impact on learners' cognitive and metacognitive skills. It also represents an effective alternative to instructor-provided assessment within computer-based education and, particularly, in massive online learning settings such as MOOCs. Various platforms have incorporated this mechanism as an…

We encounter variables with little variation often in educational data mining (EDM) due to the demographics of higher education and the questions we ask. Yet, little work has examined how to analyze such data. Therefore, we conducted a simulation study using logistic regression, penalized regression, and random forest. We systematically varied the…

Techniques from the branch of artificial intelligence known as machine learning (ML) have been applied to a wide range of problems in chemistry. Nonetheless, there are very few examples of pedagogical activities to introduce ML to chemistry students in the chemistry education literature. Here we report a computational activity that introduces…

Among community college leaders and others interested in reforms to improve student success, there is growing interest in adopting machine learning (ML) techniques to predict credential completion. However, ML algorithms are often complex and are not readily accessible to practitioners for whom a simpler set of near-term measures may serve as…

One of the reported methods of cheating in online environments in the literature is CAMEO (Copying Answers using Multiple Existences Online), where harvesting accounts are used to obtain correct answers that are later submitted in the master account which gives the student credit to obtain a certificate. In previous research, we developed an…

The purpose of this study is to compare decision trees obtained by data mining algorithms used in various areas in recent years according to different criteria. In the study, similar and different aspects of the decision trees obtained by different methods for classifying the students as successful and unsuccessful in terms of science literacy…

The role of affective states in learning has recently attracted considerable attention in education research. The accurate prediction of affective states can help increase the learning gain by incorporating targeted interventions that are capable of adjusting to changes in the individual affective states of students. Until recently, most work on…

Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decision-making reasoning to motivate the reasons…

This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…

This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…