Research studies in Educational Data Mining (EDM) often involve several variables related to student learning activities. As such, it may be necessary to run multiple statistical tests simultaneously, thereby leading to the problem of multiple comparisons. The Benjamini-Hochberg (BH) procedure is commonly used in EDM research to address this…

Background: Scientific thinking is an essential learning goal of science education and it can be fostered by inquiry learning. One important prerequisite for scientific thinking is procedural understanding. Procedural understanding is the knowledge about specific steps in scientific inquiry (e.g. formulating hypotheses, measuring dependent and…

In light of the replication crisis in psychology, null-hypothesis significance testing (NHST) and "p"-values have been heavily criticized and various alternatives have been proposed, ranging from slight modifications of the current paradigm to banning "p"-values from journals. Since the physics education research community…

The acceptance of Educational Data Mining (EDM) technology is on the rise due to, its ability to extract new knowledge from large amounts of students' data. This knowledge is important for educational stakeholders, such as policy makers, educators, and students themselves to enhance efficiency and achievements. However, previous studies on EDM…

Multilevel models are often used to evaluate hypotheses about relations among constructs when data are nested within clusters (Raudenbush & Bryk, 2002), although alternative approaches are available when analyzing nested data (Binder & Roberts, 2003; Sterba, 2009). The overarching goal of this article is to suggest when it is appropriate…

Online education is rapidly becoming a significant method of course delivery in higher education. Consequently, instructors analyze student performance in an attempt to better scaffold student learning. Learning analytics can provide insight into online students' course behaviors. Archival data from 167 graduate level education students enrolled…

This article discusses the application of logit models for the analyses of 2-way categorical observations. The models described are generalized linear models using the logit link function. One of the models is the Rasch model (Rasch, 1960). The objective is to test hypotheses of marginal and conditional independence between explanatory quantities…

Purpose: Public schools operate in a changing and dynamic environment evident in technological innovations, increased social heterogeneity and competition, all contributing to school leaders' uncertainty. Such changes inevitably influence schools' inner dynamic and may therefore undermine schools' organizational health. School leaders have a…

Learning analytics and educational data mining are introducing a number of new techniques and frameworks for studying learning. The scalability and complexity of these novel techniques has afforded new ways for enacting education research and has helped scholars gain new insights into human cognition and learning. Nonetheless, there remain some…

When laws change the rules of the game, it is important to observe the effects on the players' behavior. Some effects can be anticipated while others are difficult to enunciate before the law comes into force. In this paper we have analyzed articles authored by Spanish accounting academics between 1996 and 2005 to assess the impact of a change in…

This paper outlines a novel approach to engineering education research that provides three dimensions of learning through an experiential class activity. A simulated decision activity brought current research into the classroom, explored the effect of experiential activity on learning outcomes and contributed to the research on innovation decision…

When validating assessment models built with data mining, generalization is typically tested at the student-level, where models are tested on new students. This approach, though, may fail to find cases where model performance suffers if other aspects of those cases relevant to prediction are not well represented. We explore this here by testing if…

A common mistake in analysis of cluster randomized experiments is to ignore the effect of clustering and analyze the data as if each treatment group were a simple random sample. This typically leads to an overstatement of the precision of results and anticonservative conclusions about precision and statistical significance of treatment effects.…

This study tests a process model of learning in which trainer and trainee traits are simultaneously considered as endogenous variables of learning outcomes. The article builds on a social view of training and similarity-attraction paradigms. In this context, the authors hypothesize that trainer-trainee similarity in personality (agreeableness)…

Outlines concerns that must be addressed by those who advocate replacing statistical hypothesis-testing with alternative data-analysis strategies. Suggests that commonly recommended alternatives are not perfect and that various hypothesis-testing modifications can be implemented to make the process and its conclusions more credible. Hypothesis…