This paper proposes a conceptual framework enabling the development and adoption of descriptive, diagnostic, predictive and recommendatory data analytics in teacher professional learning by harnessing some of the affordances of digital technologies to convert data into actionable insights. The paper argues for a technology-enhanced approach that…

Learning is a multimodal process, and learning analytics (LA) researchers can readily access rich learning process data from multiple modalities, including audio-video recordings or transcripts of in-person interactions; logfiles and messages from online activities; and biometric measurements such as eye-tracking, movement, and galvanic skin…

Missing data are common with large scale assessments (LSAs). A typical approach to handling missing data with LSAs is the use of listwise deletion, despite decades of research showing that approach can be a suboptimal strategy resulting in biased estimates. In order to help researchers account for missing data, we provide a tutorial using R and…

In this paper, we investigate the conditions under which data imbalances, a common data characteristic that occurs when factor values are unevenly distributed, are problematic for the performance of Coincidence Analysis (CNA). We further examine how such imbalances relate to fragmentation and noise in data. We show that even extreme data…

This case study examines employee attrition, its detrimental effects on businesses, and the potential of data analytics to address this challenge. By employing Latent Dirichlet Allocation (LDA), a sophisticated NLP technique, we delve into the underlying reasons for employee departures. Additionally, we explore using RapidMiner to develop…

Supervision models are fundamental to our supervision practices and criticized for lacking empirical support. As a data-driven approach based on research with expert supervisors, Cohesive Model of Supervision unifies existing models' central premises in a meaningful manner and emphasizes the understated areas of supervision practice.

This study presents several multivariate Generalizability theory designs for analyzing automatic item-generated (AIG) based test forms. The study used real data to illustrate the analysis procedure and discuss practical considerations. We collected the data from two groups of students, each group receiving a different form generated by AIG. A…

Sport analytics remains a growing area in the sport industry. As such, the demand for skills and knowledge in this area has grown. This demand includes off-field data, such as marketing trends, as well as financial data related to sport organizations. There has been a trickle-down effect in sport management (and other) education programs to teach…

This paper introduces the LEGO® Database, a large natural dataset that can be used to teach Structured Query Language (SQL) and relational database concepts. This dataset is well-suited for introductory and advanced database assignments and end-of-semester group projects. The data is freely available from Kaggle.com and contains eight tables with…

This supplement contains additional information for the report, "Support for the Journey Home: An Impact Study of the Returning Citizens Stimulus Program." The Returning Citizens Stimulus (RCS) program was a pilot program for reentry cash support and aimed to help people quickly reach stability in their lives outside of prison and reduce…

This study introduces an optimized ensemble deep neural network (Optimized Ensemble Deep-NN) to enhance the accuracy of predicting student grades. This model solves the problem of different and complicated student performance data by using deep neural networks, ensemble learning, and a number of optimization algorithms, such as Adam, SGD, and RMS…

Children's fuzziness leads to increased variance in the data, data loss, and high dropout rates in developmental studies. This study investigated the importance of 20 factors on the person (child, caregiver, experimenter) and situation (task, method, time, and date) level for the data quality as indicated via the number of valid trials in 11…

Often, eye-tracking researchers define areas of interest (AOIs) to analyze eye-tracking data. Although AOIs can be defined with systematic methods, researchers in organic chemistry education eye-tracking research often define them manually, as the semantic composition of the stimulus must be considered. Still, defining appropriate AOIs during data…

This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the available literature on predicting online learners' performance through ontological machine-learning techniques and, using a systematic approach, identifies the…

Generative artificial intelligence (GenAI) has opened new possibilities for designing learning analytics (LA) tools, gaining new insights about student learning processes and their environment, and supporting teachers in assessing and monitoring students. This systematic literature review maps the empirical research of 41 papers utilizing GenAI…