In today's era of large-scale data, academic institutions, businesses, and government agencies are increasingly faced with heterogeneous datasets. Consequently, there is a growing need to develop effective methods for extracting meaningful insights from this type of data. Quantile, expectile, and expected shortfall regression methods offer useful…

Data-based decision-making has been a frequently used policy and practice intervention used in schools to help inform the decision-making processes of educational practitioners, with the aim of improving student outcomes. Interim benchmark assessments are designed by commercial test developers to support educators in this framework. In fact, the…

This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models - Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree - within a not-for-profit,…

Machine learning (ML) techniques have been successfully applied to a wide array of applications. This dissertation aims to take application data handling into account when developing ML-based solutions for real-world problems through a holistic framework. To demonstrate the generality of our framework, we consider two real-world applications: spam…

Data-based decision making (DBDM) is a process of using student data to inform instructional decisions and intensify intervention for students whose data indicate inadequate academic and behavioral progress. Data teams, an important structure for DBDM, are a collaborative group of school faculty who meet to systematically analyze student data,…

This study investigates how 11- to 12-year-old students construct data-based decision trees using data cards for classification purposes. We examine the students' heuristics and reasoning during this process. The research is based on an eight-week teaching unit during which students labeled data, built decision trees, and assessed them using test…

This case study examines the critical role school leaders play in teacher data use. Aligned with previous literature, we find that educators perceive high levels of support for data use and prefer formative data; however, observations showed a data use method unlike previously described inquiry cycles. From these findings we propose a new data use…

Integrity, security, and sustained investment in data systems are critical for state agencies and stakeholders to effectively harness information to inform policy and decision-making in postsecondary education. Recognizing this crucial need, the State Higher Education Executive Officers Association (SHEEO) has led the charge with its State…

Evidence-based policymaking can transform the delivery of education services, restore public trust in schools, and improve outcomes for students. It can cut through the noise of political and cultural divisions and give decision makers clarity on how to prioritize the use of limited resources. And it can help build a shared understanding of where…

Based on our experience developing and delivering a highly successful data visualization course within a Master of Science in Business Analytics program, we present a taxonomy for data visualization courses and recommend content and pedagogical features for each type of data visualization course therein. We also discuss the interdependence between…

Higher education has faced increased pressure to prove its quality through 'economic efficiency' and 'value for money', thrusting institutions into what researchers call an 'audit culture'. This study explores whether and how the audit culture has impacted educational developers in Canadian post-secondary institutions. Results include a summary of…

State postsecondary education data systems are vital assets for policymakers, researchers, and the public. The State Higher Education Executive Officers Association's (SHEEO) Communities of Practice project builds upon SHEEO's efforts to measure the capacity and effective use of state postsecondary data systems and provides states with…

Implementing supported and customized employment in all their components is essential for supporting job seekers with disabilities to achieve their career goals. We asked 42 employment consultants in nine employment programs to respond daily to three quick questions about their employment support activities, for 6 months. Through monthly coaching,…

Learning analytics is an emerging trend in American community colleges brought about by technological advancements, institutional accountability, and external pressure on institutions to substantiate and improve learning. Learning analytics can potentially improve student engagement, retention, and success but also possess inherent ethical and…

This nonexperimental, survey-based online quantitative study on nonprofit managers' technical training measures the extent of the influence on big data technology use. The unified theory of acceptance and use of technology is a theoretical framework to determine whether business managers are trained to have know-how in using big data technology.…