Most existing solutions to the current replication crisis in science address only the factors stemming from specific poor research practices. We introduce a novel mechanism that leverages the experts' predictive abilities to analyze the root causes of replication failures. It is backed by the principle that the most accurate predictor is the most…

Data-informed decision-making is no longer an optional or occasional practice, as higher education professionals now routinely respond to calls for accountability by providing data to show how their work impacts students. Institutions are operating with a culture that, at a minimum, includes the use of descriptive and diagnostic analyses to assess…

As institutions seek to shift into more advanced analytics and data-based decision-support, many institutional research offices face the challenge of meeting the office's current demands while taking on more intricate and specialized work to support decision-making. Given the great need organizations have for information that supports real-time…

Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, but their analyses are challenging. The present paper mathematically derived, empirically compared, and step-by-step demonstrated three types (i.e.,…

Multimodal learning analytics (MMLA) has increasingly been a topic of discussion within the learning analytics community. The Society of Learning Analytics Research is home to the CrossMMLA Special Interest Group and regularly hosts workshops on MMLA during the Learning Analytics Summer Institute (LASI). In this paper, we articulate a set of 12…

Colleges are increasingly turning to predictive analytics to identify "at-risk" students in order to target additional supports. While recent research demonstrates that the types of prediction models in use are reasonably accurate at identifying students who will eventually succeed or not, there are several other considerations for the…

The system architecture of big data in massive open online courses (BD-MOOCs System Architecture) is composed of six components. The first component was comprised of big data tools and technologies such as Hadoop, YARN, HDFS, Spark, Hive, Sqoop, and Flume. The second component was educational data science, which is composed of the following four…

Strategy and research are essential parts of strategic enrollment management (SEM), yet little information exists regarding how to use research and predictive analytics for effective strategy. It is often easier to react to what is happening in the moment, rather than be proactive in predicting the future or developing long-term plans. This…

The NAEP EDM Competition required participants to predict efficient test-taking behavior based on log data. This paper describes our top-down approach for engineering features by means of psychometric modeling, aiming at machine learning for the predictive classification task. For feature engineering, we employed, among others, the Log-Normal…

One significant impact of the COVID-19 pandemic for educators in forensic science was adapting what is traditionally a very applied field to a virtual learning environment. Because of this, science classes with a practical laboratory component had to implement significant adjustments to ensure that student learning objectives were still met,…

Organizations are keenly interested in data gathering from websites where discussions of products and brands occur. This increasingly means that programmers need an understanding of how to work with website application programming interfaces (APIs) for data acquisition. In this hands-on lab activity, students will learn how to gather data from…

Data mining in education predictions are made about other groups based on the big data of education. However, the extent to which training data can be accessed is relative. A group that can be described as missing data is children living or working on the street. For this reason this study aimed to deal with children working or living on the…

The authors present a pedagogical primer on the highly controversial business strategies of data mining and automated prediction. They provide a summary that allows business professors and students the opportunity to better understand the privacy and ethical issues that arise from high-tech, Internet-based organizations implementing programs to…

In recent years we have developed a data analytics pipeline using artificial neural networks to predict prospective student matriculation for university admissions using very limited demographic data. Predictions are generated at the earliest stages of the admissions process and successfully inform recruiting and admissions staff about the…

Due to the conditions imposed worldwide by the pandemic, students' access to school laboratories is limited, if not impossible. To provide students with raw experimental data to assess, analyse and reason out, we have filmed experiments that can be used in a flipped classroom. This paper presents an experiment which makes use of an array of six…