Post-secondary education (PSE) experienced explosive growth and diversification over the past century, affording students a range of increasingly complex pathways that they can travel to acquire a credential. In various jurisdictions, governments have made significant investments to facilitate student uptake of unconventional transfer pathways…

The educational recommendation system to provide support for academic guidance and adaptive learning has always been an important issue of research for smart education. A bad guidance can give rise to difficulties in further studies and can be extended to school dropout. This paper explores the potential of Educational Data Mining for academic…

Community schools have a long history of helping students succeed in school by addressing the problems they face outside of school. But without specific data on students and the full range of their needs, community schools cannot be as effective as they would like to be. Driven by the desire to make more data-informed decisions, the Children's Aid…

This study aims to develop a list of technological enhancements to a financial aid analytics application (F3A) designed to assist higher education leaders in gaining actionable insights for decision-making with grants optimization. This research utilizes the Design Science Research framework which highlights the design and evaluation of an IT…

California's education data, at least as they currently are collected, managed, and made available, are not sufficient for understanding and addressing the needs of California's students. That's the conclusion of the first three reports in the series "California Education Policy, Student Data, and the Quest to Improve Student Progress,"…

Predicting the future is now in the hands of K12 administrators. While for years districts have collected thousands of pieces of student data, educators have been using them only for data-driven decision-making or formative assessments, which give a "rear-view" perspective only. Now, using predictive analysis--the pulling together of data over…

Schools gather, store and use an increasingly large amount of data. Keeping track of everything from bus routes to building access codes to test scores to sports equipment is done with the help of electronic database programs. Large databases designed for budgeting and student record keeping have long been an integral part of the educational…

Now that all 50 states and the District of Columbia are building statewide longitudinal data systems, the next step is to ensure that the information in these systems is used to improve student learning. The Data Quality Campaign (DQC) has identified 10 actions that states can take to ensure that the right data are available and accessible and…

This document introduces the Student Teacher Accountability Reporting System (STARS), the educational data collection and reporting model selected by the New Mexico Public Education Department (NMPED) to improve the quality and timeliness of data, analysis, and information in meeting increased reporting and analysis requirements. The STARS model…

The No Child Left Behind legislation has drawn increased attention to student data. Data are most useful in educational decision-making when the purpose extends beyond vertical accountability and toward school- and classroom-level decision-making that enhances the experience and achievement of students. This necessarily involves getting practical…

With the passage of the No Child Left Behind Act (NCLB), state education agencies (SEAs) and local educational agencies (LEAs) have stepped up activities to broadly expand their data collection and reporting systems. The Data Quality Campaign (DQC) has advocated strongly for states to build robust student-level longitudinal data systems based on…

This document contains a design for creating an educational data bank of pupil personnel information. The specific information that should be collected and maintained in such an updatable data bank is described in detail. The document provides instructions on interpretation of this data for (1) predicting approximate percentages of students who…

Data collection and analyses of college records and interviewing provided a cross-sectional view of data flow and information transmission in a small college. The micro-analysis of interview data, forms, and reports yielded a picture of functional relationships, clarified loci of decision making, and stipulated functions served by data items.…

A detailed description is provided of the variable information acquisition (IA), which is part of the analysis in phase I of the Evaluation of Practices in Individualized Schooling. The variable is defined; the questionnaires on which the variable is based are listed; the procedures used to scale the variable are explained; the distribution of the…