This article provides clear and practical guidelines for researchers seeking to use national secondary datasets to conduct evidence-based research. Drawing from our own experiences, we discuss a six-step research process model (Bryan et al., 2010, 2017) to help researchers navigate the use of these datasets. We present examples from the school…

With technological advances, institutional stakeholders are considering evidence-based developments such as Curriculum Analytics (CA) to reflect on curriculum and its impact on student learning, dropouts, program quality, and overall educational effectiveness. However, little is known about the CA state of the art in Higher Education Institutions…

How are educational values and pedagogical approaches inscribed into automated education technologies and their data visualizations? In this article we analyze the design process and technical and pedagogical debates of a team of researchers and developers working on an automated scoring tool for primary school students' early writing as part of…

Students' sense of belonging in the mathematics classroom can be supported and increased by building mathematics tasks that connect to and incorporate aspects of students' identities and sense of selves (e.g., their names, images, and ages). This article shares one commonly used mathematics task and highlights how it can be used to create…

The Australian Universities Accord Final Report offers a historic yet insufficient opportunity to advance Indigenous self-determination in higher education. Its goals will remain hollow without dismantling the entrenched colonial foundations embedded in universities' governance and data practices. This paper demands that Indigenous data…

Data-driven marketing analytics courses are integral to modern business management degrees in universities, yet many graduates focus solely on single, separated data analysis techniques during their learning process, hindering effective integration and practical performance. This study proposes that employing the Fishbowl method, which divides…

In this article, we discuss the methodological implications of data and theory integration for Theory-Based Evaluation (TBE). TBE is a family of approaches to program evaluation that use program theories as instruments to answer questions about whether, how, and why a program works. Some of the groundwork about TBE has expressed the idea that a…

Data extraction is a time-consuming and resource-intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this…

This article presents a general-purpose method for detecting cheating in online courses, which combines anomaly detection and supervised machine learning. Using features that are rooted in psychometrics and learning analytics literature, and capture anomalies in learner behavior and response patterns, we demonstrate that a classifier that is…

Meta-analyses have become the gold standard for synthesizing evidence from multiple clinical trials, and they are especially useful when outcomes are rare or adverse since individual trials often lack sufficient power to detect a treatment effect. However, when zero events are observed in one or both treatment arms in a trial, commonly used…

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…

The purpose of this invited paper is to show the learning disabilities field what LDbase is, why it's important for the field, what it offers the field, and examples of how you can leverage LDbase in your own work.

In this paper, we propose a new method for selecting cases for in situ, immediate interview research: detector-driven classroom interviewing (DDCI). Published work in educational data mining and learning analytics has yielded highly scalable measures that can detect key aspects of student interaction with computer-based learning in close to…

Accurate data extraction is a key component of evidence synthesis and critical to valid results. The advent of publicly available large language models (LLMs) has generated interest in these tools for evidence synthesis and created uncertainty about the choice of LLM. We compare the performance of two widely available LLMs (Claude 2 and GPT-4) for…

This chapter introduces the learning analytics as a catalyst to transform data utilization and bolster support for the scholarship of teaching and learning.