Systematic reviews are resource-intensive. The machine learning tools being developed mostly focus on the study identification process, but tools to assist in analysis and categorization are also needed. One possibility is to use unsupervised automatic text clustering, in which each study is automatically assigned to one or more meaningful…

Simulation studies involving mixture models inevitably aggregate parameter estimates and other output across numerous replications. A primary issue that arises in these methodological investigations is label switching. The current study compares several label switching corrections that are commonly used when dealing with mixture models. A growth…

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…

Purpose: The purpose of this study is, First, to leverage the limitation of annotated data and to identify the cognitive level of learning objectives efficiently, this study adopts transfer learning by using word2vec and a bidirectional gated recurrent units (GRU) that can fully take into account the context and improves the classification of the…

The purpose of this research was to evaluate the effect of item pool and selection algorithms on computerized classification testing (CCT) performance in terms of some classification evaluation metrics. For this purpose, 1000 examinees' response patterns using the R package were generated and eight item pools with 150, 300, 450, and 600 items…

The web is the largest world-wide communication system of computers. The web has local, academic, commercial and government sites. As the types of websites increases in numbers, the cost and accuracy of manual classification became cumbersome and cannot satisfy the increasing internet service demands, thereby automated classification became…

Learning techniques have proven their capacity to treat large amount of data. Most statistical learning approaches use specific size learning sets and create static models. Withal, in certain some situations such as incremental or active learning the learning process can work with only a smal amount of data. In this case, the search for algorithms…

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…

Over the past decade, machine learning has become an integral part of educational technologies. With more and more applications such as students' performance prediction, course recommendation, dropout prediction and knowledge tracing relying upon machine learning models, there is increasing evidence and concerns about bias and unfairness of these…

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…

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…

We present two types of validity evidence to support inferences and decisions about use of easyCBMs in relation to state testing programs. The first type involves the use of Benchmarks in reading to use in making predictions of performance on the Smarter Balanced (SB) test. These predictions can be made both well in advance (several months) or…