Performance Management in Action

Outlines how collecting and sharing data enables educators to assess student performance in real time and predict and address issues early in order to improve achievement levels and graduation rates. Profiles successful programs in three urban districts

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

Evaluating Digital Math Tools in the Field

Many school districts have adopted digital tools to supplement or replace teacher-led instruction, usually based on the premise that these tools can provide more personalized or individualized experiences for students and at lower cost. Rigorously evaluating whether such initiatives promote better student outcomes in the field is difficult as most schools and teachers are unwilling to enforce rigorous study designs such as randomized control trials. We used study designs that were feasible in practice to assess whether two digital math tools, eSpark and IXL, were associated with improvements in 3rd – 6th grade student test scores in math. We also investigated the resource requirements and costs of implementing eSpark and IXL to assess whether these tools represent a valuable use of resources. We find that while IXL is substantially less costly to implement than eSpark, its use is not significantly associated with students’ math performance

Comprendiendo el potencial y los desafíos del Big Data en las escuelas y la educación

In recent years, the world has experienced a huge revolution centered around the gathering and application of big data in various fields. This has affected many aspects of our daily life, including government, manufacturing, commerce, health, communication, entertainment, and many more. So far, education has benefited only a little from the big data revolution. In this article, we review the potential of big data in the context of education systems. Such data may include log files drawn from online learning environments, messages on online discussion forums, answers to open-ended questions, grades on various tasks, demographic and administrative information, speech, handwritten notes, illustrations, gestures and movements, neurophysiologic signals, eye movements, and many more. Analyzing this data, it is possible to calculate a wide range of measurements of the learning process and to support various educational stakeholders with informed decision-making. We offer a framework for better understanding of how big data can be used in education. The framework comprises several elements that need to be addressed in this context: defining the data; formulating data-collecting and storage apparatuses; data analysis and the application of analysis products. We further review some key opportunities and some important challenges of using big data in educationEn los últimos años, el mundo ha experimentado una gran revolución centrada en la recopilación y aplicación de big data en varios campos. Esto ha afectado muchos aspectos de nuestra vida diaria, incluidos el gobierno, la manufactura, el comercio, la salud, la comunicación, el entretenimiento y muchos más. Hasta ahora, la educación se ha beneficiado muy poco de la revolución del big data. En este artículo revisamos el potencial de los macrodatos en el contexto de los sistemas educativos. Dichos datos pueden incluir archivos de registro extraídos de entornos de aprendizaje en línea, mensajes en foros de discusión en línea, respuestas a preguntas abiertas, calificaciones en diversas tareas, información demográfica y administrativa, discurso, notas escritas a mano, ilustraciones, gestos y movimientos, señales neurofisiológicas, movimientos oculares y muchos más. Analizando estos datos es posible calcular una amplia gama de mediciones del proceso de aprendizaje y apoyar a diversos interesados educativos con una toma de decisiones informada. Ofrecemos un marco para una mejor comprensión de cómo se puede utilizar el big data en la educación. El marco comprende varios elementos que deben abordarse en este contexto: definición de los datos; formulación de aparatos de recolección y almacenamiento de datos; análisis de datos y aplicación de productos de análisis. Además, revisamos algunas oportunidades clave y algunos desafíos importantes del uso de big data en la educació

"Low-Income Students of Color in the U.S. Neoliberal Public Education System: An Examination of Federal and State Intervention Policies”

In this thesis I will use a critical policy analysis approach to examine US education reform and its effects on social inequality. I situate my analysis within the rise of neoliberal ideology which prioritizes market-driven values, champions individualism, diminishes social responsibility, and promotes deregulation. I seek to answer research questions such as: how does the United States’ neoliberal agenda create, maintain, and reproduce the marginalization of low-income students of color? How is the neoliberal agenda embedded in US education policy and law at state and federal levels? And, how might students of color conceptualize themselves within the larger framework of neoliberalism? As evidence I draw on theory, policy analysis, and existing empirical data on one of the most underperforming public schools in San Diego, California, with most of the students being minorities and low-income: Abraham Lincoln High School

Teachers Know Best: Making Data Work For Teachers and Students

The Teachers Know Best research project seeks to encourage innovation in K - 12 education by helping product developers and those who procure resources for teachers better understand teachers' views. The intent of Making Data Work is to drill down to help educators, school leaders, and product developers better understand the challenges teachers face when working with this critical segment of digital instructional tools. More than 4,600 teachers from a nationally representative sample were surveyed about their use of data to drive instruction and the use of these tools.This study focuses on the potential of a specific subset of digital instructional tools: those that help teachers collect and make use of student data to tailor and improve instruction for individual students. The use of data is a crucial component in personalized learning, which ensures that student learning experiences -- what they learn and how, when, and where they learn it -- are tailored to their individual needs, skills, and interests and enable them to take ownership of their learning. Personalized learning is critical to meeting all students where they are, so they are neither bored with assignments that are too easy nor overwhelmed by work that is too hard

Project based learning on industrial informatics: applying IoT to urban garden

Copyright (c) 2018 IEEEThe fast evolution of technologies forces teachers to trade content off for self-learning. PBL is one of the best ways to promote self-learning and simultaneously boost motivation. In this paper, we present our experience introducing project-based learning in the last year subject. New Internet of Things (IoT) topic allows us to carry out complete projects, integrating different technologies and tools. Moreover, the selection of open-source and standard free technologies makes easy and cheap the access to hardware and software platforms used. We carefully have picked communication, data management, and programming tools that we think would be attractive to our students. They can start making fast prototyping with little initial skills and, at the same time, these are serious and popular tools widely used in the industry. In this paper, we report on the design of a project-based learning for our course and the impact this has on the student satisfaction and motivation. Surveys taught us that tuning the courses towards developing real projects on the field, has a large impact on acceptance, learning objectives achievements and motivation towards the course content.”I Plan Propio Integral de Docencia de la Universidad de Málaga” y Proyecto de Innovación Educativa PIE17/085, de la Universidad de Málaga. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

LAView: Learning Analytics Dashboard Towards Evidence-based Education

The 9th International Learning Analytics and Knowledge (LAK) Conference : March 4-8, 2019, Tempe, Arizona, USALearning analytics dashboards (LAD) have supported prior finds that visualizing learning behavior helps students to reflect on their learning. We developed LAViEW, a LAD that can be easily integrated with different learning environments through LTI. In this paper, we focus on the context of eBook-based learning and present an overview of the indicators of engagement that LAView visualizes. Its integrated email widget enables the teacher to directly send personalized feedbacks to selected cohorts of students, clustered by their engagement scores. These interventions and dashboard interactions are further tracked to extract evidence of learning

Adaptive Learning Technology in Primary Education: Implications for Professional Teacher Knowledge and Classroom Management

The aim of this study is to explore the introduction of Adaptive Learning Technology (ALT) and inherent Learning Analytics (LA) in the classroom management and professionalism of teachers in a primary education real-life context. ALT is characterized by an inherent opportunity to personalize curriculum and learning experiences for each individual learner and to support teacher-facilitated learning. In this mixed methods study, we explore upper-primary teachers understanding of ALT application in real-life context, and we take a closer look at their experiences with ALT in their own context and practice through three different methodological lenses. The study offers insight into how teachers think and reason as they integrate ALT in their practice and addresses advantages and disadvantages of using ALT technology in primary education learning ecologies. The study also aims to discuss some more general implications of applying ALT and LA in primary and secondary level learning ecologies and concludes that automated system affordances and constraints can create new challenges for teachers, which exceeds teachers’ digital competence and ability to make use of certain real and perceived affordances.publishedVersio