In recent years, the rapid growth of Massive Open Online Courses (MOOCs) has attracted much attention for related research. Besides, one of the main challenges in MOOCs is the high dropout or low completion rate. Early dropout prediction algorithms aim the educational institutes to retain the students for the related course. There are several…

Many students persistently misinterpret histograms. Literature suggests that having students solve dotplot items may prepare for interpreting histograms, as interpreting dotplots can help students realize that the statistical variable is presented on the horizontal axis. In this study, we explore a special case of this suggestion, namely, how…

Learning interaction activities are the key part of tracking and evaluating learning behaviors, that plays an important role in data-driven autonomous learning and optimized learning in interactive learning environments. In this study, a big data set of learning behaviors with multiple learning periods is selected. According to the instance…

"Using Sentiment Analysis to Ease Students toward or around Macroanalysis" introduces teachers to the mathematical analytical method currently being promoted by Matthew Jockers which he calls macroanalysis. This method uses an algorithm called "R," originally designed by researchers in sociology. In many ways, macroanalysis…

Current learning platforms generally have problems such as fragmented knowledge, redundant information, and chaotic learning routes, which cannot meet learners' autonomous learning requirements. This paper designs a learning path recommendation system based on knowledge graphs by using the characteristics of knowledge graphs to structurally…

Several studies compared three different types of questions (conceptual, algorithmic, and graphical) across various topics, however, few focused specifically on gifted students. This study addressed this gap. The aim of the study, hence, was to determine whether there were notable differences in gifted students' performance in the three types of…

Structural equation modeling (SEM) is being applied to ever more complex data types and questions, often requiring extensions such as regularization or novel fitting functions. To extend SEM, researchers currently need to completely reformulate SEM and its optimization algorithm -- a challenging and time-consuming task. In this paper, we introduce…

In course design, topic outline organization encompasses the structuring and sequencing of topics to be delivered in a learning environment. Recent studies in topic outline optimization revolve around massive open online courses (MOOCs) due to their abundance but not much has been studied on the traditional courses. This study investigates the…

As a first step toward automatic feedback based on students' strategies for solving histogram tasks we investigated how strategy recognition can be automated based on students' gazes. A previous study showed how students' task-specific strategies can be inferred from their gazes. The research question addressed in the present article is how data…

A majority of research in Computational Thinking (CT) mainly focuses on teaching coding to school students. However, CT involves more than just coding and includes other skills like algorithmic thinking. The current study developed an Online Inquiry-based Learning Platform for Computational Thinking (CT-ONLINQ) that follows Inquiry-Based Learning…

This study shows that residual variation can cause problems related to scaling in exponential random graph models (ERGM). Residual variation is likely to exist when there are unmeasured variables in a model--even those uncorrelated with other predictors--or when the logistic form of the model is inappropriate. As a consequence, coefficients cannot…

Purpose: In order to initialize a student model in intelligent tutoring systems, some form of initial knowledge test should be given to a student. Since the authors cannot include all domain knowledge in that initial test, a domain knowledge subset should be selected. The paper aims to discuss this issue. Design/methodology/approach: In order to…

Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class…

Concepts of ordinal network representation are discussed. Notation (the type of data that can be represented) and the type of representation given are reviewed. The idea of reduced ordinal networks is explored; and the algorithm, uniqueness results, and error handling problems are presented. Examples of data analysis are included. (SLD)

Describes research that investigated a method of automatically generating concept association maps that could be useful to abstractors. The use of word stems as concept surrogates, cooccurrence to define concept links, and a general-purpose graph-drawing algorithm is described; and results of evaluation of two link selection methods are reported.…