This study investigates inter- and intracoder reliability, proposing a new approach based on social network analysis (SNA) and exponential random graph models (ERGM). During a recent exit poll, the responses of voters to two open-ended questions were recorded. A coding experiment was conducted where a group of coders coded a sample of text…

As learning analytics use grows across U.S. colleges and universities, so does the need to discuss the plans, purposes, and paths for the data collected via learning analytics. More specifically, students, faculty, and others who are impacted by learning analytics use should have more information about their campus' learning analytics practices…

Although researchers recognize the importance of discussing support for math learning within online learning communities, there is a lack of relevant network classifying methods and analyses at the group level to understand the behavioral differences between groups with varying levels of activity, including their mathematical literacies. In this…

As Ratnam makes clear, a cultural-historical perspective on teacher/faculty excessive entitlement is indispensable if we are to use this concept to work with, rather than undermine, education practitioners. In this chapter, a networked relational model of activity is proposed as a tool for understanding excessive entitlement from a…

In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently…

Checking for possible inconsistency between direct and indirect evidence is an important task in network meta-analysis. Recently, an evidence-splitting (ES) model has been proposed, that allows separating direct and indirect evidence in a network and hence assessing inconsistency. A salient feature of this model is that the variance for…

Graph neural networks (GNN) are vital for analyzing real-world problems (e.g., network analysis, drug interaction, electronic design automation, e-commerce) that use graph models. However, efficient GNN acceleration faces with multiple challenges related to high and variable sparsity of input feature vectors, power-law degree distribution in the…

Churn in e-learning refers to learners who gradually perform less and become lethargic and may potentially drop out from the course. Churn prediction is a highly sensitive and critical task in an e-learning system because inaccurate predictions might cause undesired consequences. A lot of approaches proposed in the literature analyzed and modeled…

This article analyzes the Common Core State Standards initiative as an innovation network. Using narrative data and quantitative analysis of hypertext linkages on the World Wide Web, we describe a network of about 3200 organizations that arose to scale up the Common Core State Standards and link them to aligned academic resources such as…

A research question that is of interest across many disciplines is whether and how relationships in a network are related to the attributes of the nodes of the network. In this dissertation, we propose two joint frameworks for modeling the relationship between the network and attributes. In the joint latent space model in Chapter 2, shared latent…

This paper is part 4 in a series of workshops that examine the properties of some simple models of vocabulary networks. This Workshop explores how the overall activity level of a vocabulary network can be altered by changing the connections in the network (i.e., by implementing relinking events). The Workshop is linked to an online practice room…

Process data from educational assessments enhance the understanding of how students answer cognitive items. However, effectively making use of these data is challenging. We propose an approach to identify solution patterns from operation sequences and response times by generating networks from process data and defining network features that…

Background/Context: Social network methodology is particularly relevant to the types of social structures found in education research. The current study develops a finite mixture approach for clustering ensembles of networks (NetMix). Following a structural equation modeling framework, NetMix simultaneously estimates a measurement model comprised…

Since 2001, the National Institutes of Health (NIH) have funded the Institutional Development Award (IDeA) Network of Biomedical Research Excellence (INBRE) to expand biomedical research capacity among states in which NIH funding was historically low. The Western IDeA Region comprises seven states: Alaska, Hawaii, Idaho, Montana, New Mexico,…

This dissertation is about a particular style of research. The philosophy of this style is that in order to scientifically understand deep learning, it is fruitful to investigate what happens when neural networks are trained on simple, mathematically well-defined tasks. Even though the training data is simple, the training algorithm can end up…