Since schools increasingly use digital platforms that provide educational data in digital formats, teacher data use, and data literacy have become a focus of educational research. One main challenge is whether teachers use digital data for pedagogical purposes, such as informing their teaching. We conducted a survey study with N = 1059 teachers in…

Improving educational practice through reflection is one important focus of teacher professional development approaches. However, such teacher reflection operates under practical classroom constraints that make it happen infrequently, including the reliance on disruptive peer/supervisor observations or recordings. This article describes three…

Purpose: The general goal of this paper is to help educators understand the importance of MOOC training to school teachers and their hypothetical value for predicting the use of teaching strategies in the face-to face-classroom teaching. With this purpose, the study is guided by two research questions: (1) Are there different patterns of…

The purpose of this article is to describe the possibilities of using worksheets in the teaching of lipids and proteins. The worksheet includes a concept map, suggestions for three safe experiments that can be performed with available household chemicals, and observation results. The worksheets were implemented into action research in two classes…

Teacher-level factors are theoretically linked to student outcomes in data-based instruction (DBI; Lembke et al., 2018). Professional development and ongoing support can increase teachers' knowledge, skills, and beliefs related to DBI, as well as their instructional fidelity (McMaster et al., 2020). However, less is known about how each of these…

Through a series of explorations, this article will demonstrate how the Kentucky Derby winning times dataset provides various opportunities for introductory and advanced topics, from data processing to model building. Although the final goal may be a prediction interval, the dataset is rich enough for it to appear in several places in an…

Humans have always been lured by the idea that they can use data to understand a phenomenon and make predictions about it. Learning analytics, in this sense, promise to understand and optimize learning and the environments in which it occurs by collecting data from learners and learning contexts. In this regard, this study systematically examines…

In addition to providing a set of techniques to analyze educational data, I claim that data science as a field can provide broader insights to education research. In particular, I show how the bias-variance tradeoff from machine learning can be formally generalized to be applicable to several prominent educational debates, including debates around…

In Applied AI, or 'machine learning', methods such as neural networks are used to train computers to perform tasks without human intervention. In this article, we question the applicability of these methods to education. In particular, we consider a case of recent attempts from data scientists to add AI elements to a handful of online learning…

The increasing prevalence of learner-centred forms of learning as well as an increase in the number of learners actively participating on a wide range of digital platforms and devices give rise to an ever-increasing stream of learning data. Learning analytics (LA) can enable learners, teachers, and their institutions to better understand and…

There has been extensive research using centrality measures in educational settings. One of the most common lines of such research has tested network centrality measures as indicators of success. The increasing interest in centrality measures has been kindled by the proliferation of learning analytics. Previous works have been dominated by…

In the age of big data and analytics, it is important that students learn about artificial intelligence (AI) and machine learning (ML). Machine learning is a discipline that focuses on building a computer system that can improve itself using experience. ML models can be used to detect patterns from data and recommend strategic marketing actions.…

Prediction of student performance is one of the most important subjects of educational data mining. Artificial neural networks are seen to be an effective tool in predicting student performance in e-learning environments. In the studies carried out with artificial neural networks, performance predictions based on student scores are generally made,…

The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. We focus on building models to find the characteristics and features involved in…

Serious games have proven to be a powerful tool in education to engage, motivate, and help students learn. However, the change in student knowledge after playing games is usually measured with traditional (paper) prequestionnaires-postquestionnaires. We propose a combination of game learning analytics and data mining techniques to predict…