Respondent-driven sampling (RDS) is a methodology for sampling and analysing survey data from rare and elusive populations that has gained increasing attention in migration research in recent years. Since its introduction in 1997, it has been applied in numerous surveys in the field of epidemiology (mainly for the estimation of HIV/AIDS prevalence…

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…

In many developed countries, disagreement on important policy issues between groups with different social identities ('ideological polarisation') is increasing. In professional settings, these disagreements undermine cooperation and trust between employees, which negatively affects work relationships and managerial decision-making. We investigate…

In this paper, we investigate an interesting question that came up when reading a problem in a school textbook: What happens to the variance of a dataset in the case of changing one single data point, and why? Some of the answers are not surprising but here we find the full answer and demonstrate the understanding of it suitable for school…

Multilevel modeling is commonly used with clustered data, and much emphasis has been placed specifically on the multilevel linear model (MLM). When modeling clustered ordinal data, a multilevel ordinal model with cumulative logit link assuming proportional odds (i.e., multilevel cumulative logit model) is typically used. Depending on the research…

We propose a new method for estimating school-level characteristics from publicly available census data. We use a school's location to impute its catchment area by aggregating the nearest "n" census block groups such that the number of school-aged children in those "n" block groups is just over the number of students enrolled…

Participatory modeling (PM) is an engaged research methodology for creating analog or computer-based models of complex systems, such as socio-environmental systems. Used across a range of fields, PM centers stakeholder knowledge and participation to create more internally valid models that can inform policy and increase engagement and trust…

Artificial intelligence (AI) and operations research (OR) have long been intertwined because of their synergistic relationship. Given the increasing popularity of AI and machine learning in particular, we face growing demand for educational offerings in this area from our students. This paper describes two courses that introduce machine learning…

Analysing the interaction between classroom dialogue and digital technology is challenging. Studies in this area typically draw on methods developed for the analysis of spoken interactions. This article reports on a new approach for analysing the enacted affordances of digital technology in classroom dialogue. Using examples from cross-country…

Large data sets invite students to engage in scientific practices such as question asking, identifying correlations, using visualizations, and practicing data literacy in an authentic context. However, authentic data sets are rarely introduced in the biology classroom. We prepared an online inquiry activity based on authentic gross characteristics…

Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the…

Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication…

Although various academic disciplines use data papers to support effective research practices, data papers are still uncommon in the educational sciences. Main goals of data papers are enhancing transparency regarding research processes and supporting data sharing among researchers and thus, open science. As many educational research projects…

In this study, we propose a MOOC Analytic Statistical Visual model (MOOC-ASV) to explore students' engagement in MOOC courses and predict their performance on the basis of their behaviors logged as big data in MOOC platforms. The model has multifunctions, which performs on visually analyzing learners' data by state-of-the-art techniques. The model…

The current study used a randomized control trial to evaluate a decision-making model on special education preservice candidates' instructional decision-making and self-reported confidence ratings when analyzing graphed student data. Thirty-two special education preservice candidates viewed authentic curriculum-based measurement (CBM) graphs and…