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Quantitative and Statistical Data in Education
This book presents different data collection and representation techniques: elementary descriptive statistics, confirmatory statistics, multivariate approaches and statistical modeling. It exposes the possibility of giving more robustness to the classical methodologies of education sciences by adding a quantitative approach. The fundamentals of each approach and the reasons behind them are methodically analyzed, and both simple and advanced examples are given to demonstrate how to use them. Subsequently, this book can be used both as a course for the uninitiated and as an accompaniment for researchers who are already familiar with these concepts.
eBook
Data Rules
Data Rules provides much-needed clarity on how instructional leaders can effectively leverage data.
It's no secret that using data can be a key driver of teacher growth and student achievement. The only real question is how. Coaching expert Jim Knight and professor Michael Faggella-Luby distill decades of research into an accessible, proven approach that explains
- Why data is important for transforming teaching.
- A framework of 10 easy-to-apply rules for effective data use.
- Best practices to communicate and discuss data.
- How to analyze data for student engagement and achievement.
- How to analyze data for instructional practice.
To help schools achieve sustained improvement, this book also connects its data rules to the Impact Cycle, Knight's field-tested model for coaching teachers based on research from the Instructional Coaching Group (ICG). Equipped with the right tools, any instructional leader or coach will be able to realize the full potential of data, move the needle on classroom instruction, and improve student outcomes.
This book is a copublication of ASCD and One Fine Bird Press.
eBook
Quality Indicators for Learning Analytics
This article proposes a framework of quality indicators for learning analytics that aims to standardise the evaluation of learning analytics tools and to provide a mean to capture evidence for the impact of learning analytics on educational practices in a standardised manner. The criteria of the framework and its quality indicators are based on the results of a Group Concept Mapping study conducted with experts from the field of learning analytics. The outcomes of this study are further extended with findings from a focused literature review.
Journal Article
Enhancing educational practices:: strategies for assessing and improving learning outcomes
\"The effective assessment of learning outcomes serves as the cornerstone of educational guidance while improving learning outcomes stands as the central objective of effective teaching. As intelligent technology continues to advance, the field of education must endeavor to develop increasingly personalized, effective, and human-centric approaches to assessing and enhancing learning outcomes. To realize this vision, this book seeks to identify educational realities, dismantle educational barriers using advanced technology, and speculate on future trajectories. Throughout this book, readers will delve into cutting-edge research about the assessment and enhancement of learning outcomes, explore the latest educational technologies for this purpose, and gain a more comprehensive understanding of future research directions. Let us collectively contribute to shaping the future of AI for education\"--.
eBook
Prediction of Student’s performance by modelling small dataset size
Prediction of student’s performance became an urgent desire in most of educational entities and institutes. That is essential in order to help at-risk students and assure their retention, providing the excellent learning resources and experience, and improving the university’s ranking and reputation. However, that might be difficult to be achieved for startup to mid-sized universities, especially those which are specialized in graduate and post graduate programs, and have small students’ records for analysis. So, the main aim of this project is to prove the possibility of training and modeling a small dataset size and the feasibility of creating a prediction model with credible accuracy rate. This research explores as well the possibility of identifying the key indicators in the small dataset, which will be utilized in creating the prediction model, using visualization and clustering algorithms. Best indicators were fed into multiple machine learning algorithms to evaluate them for the most accurate model. Among the selected algorithms, the results proved the ability of clustering algorithm in identifying key indicators in small datasets. The main outcomes of this study have proved the efficiency of support vector machine and learning discriminant analysis algorithms in training small dataset size and in producing an acceptable classification’s accuracy and reliability test rates.
Journal Article
Characteristics of women obtaining induced abortions in selected low- and middle-income countries
In 2010-2014, approximately 86% of abortions took place in low- and middle-income countries (LMICs). Although abortion incidence varies minimally across geographical regions, it varies widely by subregion and within countries by subgroups of women. Differential abortion levels stem from variation in the level of unintended pregnancies and in the likelihood that women with unintended pregnancies obtain abortions.
To examine the characteristics of women obtaining induced abortions in LMICs.
We use data from official statistics, population-based surveys, and abortion patient surveys to examine variation in the percentage distribution of abortions and abortion rates by age at abortion, marital status, parity, wealth, education, and residence. We analyze data from five countries in Africa, 13 in Asia, eight in Europe, and two in Latin America and the Caribbean (LAC).
Women across all sociodemographic subgroups obtain abortions. In most countries, women aged 20-29 obtained the highest proportion of abortions, and while adolescents obtained a substantial fraction of abortions, they do not make up a disproportionate share. Region-specific patterns were observed in the distribution of abortions by parity. In many countries, a higher fraction of abortions occurred among women of high socioeconomic status, as measured by wealth status, educational attainment, and urban residence. Due to limited data on marital status, it is unknown whether married or unmarried women make up a larger share of abortions.
These findings help to identify subgroups of women with disproportionate levels of abortion, and can inform policies and programs to reduce the incidence of unintended pregnancies; and in LMICs that have restrictive abortion laws, these findings can also inform policies to minimize the consequences of unsafe abortion and motivate liberalization of abortion laws. Program planners, policymakers, and advocates can use this information to improve access to safe abortion services, postabortion care, and contraceptive services.
Journal Article
Combining cognitive theory and data driven approaches to examine students’ search behaviors in simulated digital environments
BackgroundDigital Information Literacy (DIL) refers to the ability to obtain, understand, evaluate, and use information in digital contexts. To accurately capture various dimensions of DIL, assessment designers have increasingly looked toward complex, interactive simulation-based environments that afford more authentic learner performances. These rich assessment environments can capture process data produced by students’ goal driven interactions with digital sources but linking this data to inferences about the target constructs introduces significant measurement challenges which cognitive theory can help us address.MethodsIn this paper, we analyzed data generated from a simulated web search tool embedded within a theoretically-grounded virtual world assessment of multiple-source inquiry skills. We describe a multi-step clustering approach to identify patterns in student’s search processes by bringing together theory-informed process data indicators and sequence clustering methods.ResultsWe identified four distinct search behaviors captured in students’ process data. We found that these search behaviors differed both in their contribution to the web search tool subscores as well as correlations with task level multiple-source inquiry subconstructs such as locating, evaluating, and synthesizing information. We argue that the search behaviors reflect differences in how students generate and update their task goals.ConclusionThe data-driven approach we describe affords a qualitative understanding of student strategy use in a complex, dynamic simulation- and scenario-based environment. We discuss some of the strengths and challenges of using a theoretical understanding of multiple-source inquiry to inform how we processed, analyzed, and interpreted the data produced from this assessment tool and the implications of this approach for future research and development.
Journal Article