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

La relación docente-estudiante en el aula de computación uno a uno

The student-teacher relationship is an important component of both students' and teachers' development. Today, technology-rich learning environments offer opportunities that might change these relationships. This paper presents findings from six studies of teacher-student relationships in the one-to-one computing classroom (and another study that refers to distance teaching). Taken together, those studies—that were carried out in Israel between 2014–2016 using both qualitative and quantitative methodologies, with a combined N=238 teachers—highlight various improvements in student-teacher relationships. Overall, it is argued that one-to-one computing programs drive some important changes in teaching/learning strategies, and that these changes affect student-teacher relationships positively.La relación entre docente y estudiante es un componente importante tanto para el desarrollo de uno como del otro. Los ambientes de aprendizaje actuales, ricos en tecnología, ofrecen oportunidades que podrían cambiar dichas relaciones. El presente trabajo presenta conclusiones de seis estudios de relaciones docente-estudiante en aulas de computación uno a uno (y otro estudio que refiere a la docencia a distancia). Juntos, ambos estudios —llevados a cabo en Israel, entre 2014 y 2016, con metodologías tanto cualitativas como cuantitativas, con una muestra combinada de 238 docentes— resaltan mejoras varias en las relaciones docente-estudiante. En general, se mantiene que los programas de computación uno a uno generan cambios importantes en las estrategias de enseñanza y de aprendizaje, y que dichos cambios afectan las relaciones docente-estudiante de manera positiva

The role of pedagogy in one-to-one computing lessons: a quantitative observational study of teacher-student interactions

In this study, we compared teacher-student interactions in traditional lessons and in lessons implementing a one-to-one computing program, where all students and the teacher have an Internet-connected tablet to be used in the classroom. Taking a within-subject approach, we used quantitative field observations to investigate deviance from traditional lessons (with no use of computers). The study population included three 5th- and 6th grade English teachers. Findings show that the teachers change their teaching when tablets are used in the classroom, but each teacher changes differently. Nevertheless, there are similarities in the overall time spent on whole-class discussions and in the time of overall computer use. We also find that interactions are typical of learning configuration types, independent of computer use. We conclude the paper by discussing the findings and noting their implications for teacher trainingEn este estudio comparamos las interacciones entre profesor y alumno en clases tradicionales y en clases que implementan un programa de computación uno a uno, donde todos los alumnos y el profesor tienen una tableta conectada a Internet para usar en el aula. Adoptando un enfoque intrasujeto, hemos usado observaciones de campo cuantitativas para investigar la desviación respecto de las clases tradicionales (sin uso de computadoras). La población del estudio incluyó tres profesoras de inglés de 5.º y 6.º curso. Los hallazgos mostraron que las profesoras cambiaron su manera de enseñar cuando usaron tabletas en su clase, pero cada profesora cambió de manera diferente. Sin embargo, hubo similitudes en el tiempo total empleado en debates de toda la clase y en el tiempo de uso de la computadora. También hallamos que las interacciones son típicas de los tipos de configuración de aprendizaje, con independencia del uso que se le dé a la computadora. Concluimos el artículo con la exposición de los hallazgos, destacando sus implicaciones para la capacitación del profesorado

About "Learning" and "Analytics"

This issue of the Journal of Learning Analytics features three special sections that look into topics of learning analytics for 21st century skills, multimodal learning analytics, and sharing of datasets for learning analytics. The issue also features a paper that looks at models for early detection of students at risk in tertiary education. The editorial concludes with a summary of the changes in the editorial team of the journal

Learning analytics:Richer perspectives across stakeholders

This issue of the Journal of Learning Analytics features seven research papers, complemented by a practitioner research paper (Dvorak & Jia). Papers by McCoy and Shih, and Knight, Brozina, and Novoselich discuss the important topic of educators working with educational data, alongside (in the latter paper) student perspectives on learning analytics. Douglas, Bermel, Alam, and Madhavan; and Waddington, Nam, Lonn, and Teasley offer empirical insight on developing a richer perspective on learning material interaction and engagement in online learning contexts (MOOCs, and LMS’ respectively). Dvorak and Jia bring a practitioner perspective to the issue in their discussion of approaches to analyzing online work habits via timeliness, regularity, and intensity. Sutherland and White, and Vieira, Goldstein, Purzer, and Magana offer focus on specific subject-based learning activities (algebra learning, and student experimentation strategies in engineering design, respectively). Finally, Howley and Rosé discuss the complex interactions of theory and method in computational modeling of group learning processes. The issue also features a special section on learning analytics tutorials, edited by Gašević and Pechenizkiy. The editorial concludes with a report of the recent ‘hot spots section’ consultation from the editorial team of the journal

Research with simulated data

This first issue of the Journal of Learning Analytics in 2017 features a special section of invited papers from the recent Learning Analytics and Knowledge conference (LAK'16). The theme of the conference, and this special section, relates to the need for Learning Analytics research to challenge our methodological and theoretical assumptions and build new interdisciplinary connections to further our thinking.</jats:p

Discovery with Models: A Case Study on Carelessness in Computer-based Science Inquiry

In recent years, an increasing number of analyses in Learning Analytics and Educational Data Mining (EDM) have adopted a &quot;Discovery with Models&quot; approach, where an existing model is used as a key component in a new EDM/analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its potential to enhance research on learning and learners, and key lessons learned in how discovery with models can be conducted validly and effectively. We illustrate these issues through discussion of a case study where discovery with models was used to investigate a form of disengaged behavior, i.e., carelessness, in the context of middle school computer-based science inquiry. This behavior has been acknowledged as a problem in education as early as the 1920s. With the increasing use of high-stakes testing, the cost of student carelessness can be higher. For instance, within computer-based learning environments careless errors can result in reduced educational effectiveness, with students continuing to receive material they have already mastered. Despite the importance of this problem, it has received minimal research attention, in part due to difficulties in operationalizing carelessness as a construct. Building from theory on carelessness and a Bayesian framework for knowledge modeling, we use machine-learned detectors to predict carelessness within authentic use of a computer-based learning environment. We then use a discovery with models approach to link these validated carelessness measures to survey data, to study the correlations between the prevalence of carelessness and student goal orientation. The second construct, carelessness, refers to incorrect answers given by a student on material that the student should be able to answer correctly Rodriguez-Fornells &amp; Maydeu-Olivares, 2000). The application of discovery with models involves two main phases. First, a model of a construct is developed using machine learning or knowledge engineering techniques, and is then validated, as discussed below. Second, this validated model is applied to data and used as a component in another analysis: For example, for identifying outliers through model predictions; examining which variables best predict the modeled construct; finding relationships between the construct and other variables using correlations, predictions, associations rules, causal relationships or other methods; or studying the contexts where the construct occurs, including its prevalence across domains, systems, or populations. For example, in One essential question to pose prior to a discovery with model analysis is whether the model adopted is valid, both overall, and for the specific situation in which it is being used. Ideally, a model should be validated using an approach such as cross-validation, where the model is repeatedly trained on one portion of the data and tested on a different portion, with model predictions compared to appropriate external measures, for example assessments made by humans with acceptably high inter-rater reliability, such as field observations of student behavior for gaming the system (cf. Even after validating in this fashion, validity should be re-considered if the model is used for a substantially different population or context than was used when developing the model.. An alternative approach is to use a simpler knowledge-engineered definition, rationally deriving a function/rule that is then applied to the data. In this case, the model can be inferred to have face validity. However, knowledge-engineered models often DISCOVERY WITH MODELS: A CASE STUDY ON CARELESSNESS 6 produce different results than machine learning-based models, for example in the case of gaming the system. Research studying whether student or content is a better predictor of gaming the system identified different results, depending on which model was applied (cf. Baker, 2007a

Expression Analysis of the Ligands for the Natural Killer Cell Receptors NKp30 and NKp44

BACKGROUND: The natural cytotoxicity receptors (NCR) are important to stimulate the activity of Natural Killer (NK) cells against transformed cells. Identification of NCR ligands and their level of expression on normal and neoplastic cells has important implications for the rational design of immunotherapy strategies for cancer. METHODOLOGY/PRINCIPAL FINDINGS: Here we analyze the expression of NKp30 ligand and NKp44 ligand on 30 transformed or non-transformed cell lines of different origin. We find intracellular and surface expression of these two ligands on almost all cell lines tested. Expression of NKp30 and NKp44 ligands was variable and did not correlate with the origin of the cell line. Expression of NKp30 and NKp44 ligand correlated with NKp30 and NKp44-mediated NK cell lysis of tumor cells, respectively. The surface expression of NKp30 ligand and NKp44 ligand was sensitive to trypsin treatment and was reduced in cells arrested in G(2)/M phase. CONCLUSION/SIGNIFICANCE: These data demonstrate the ubiquitous expression of the ligands for NKp30 and NKp44 and give an important insight into the regulation of these ligands

Modulation of NKp30- and NKp46-Mediated Natural Killer Cell Responses by Poxviral Hemagglutinin

Natural killer (NK) cells are an important element in the immune defense against the orthopox family members vaccinia virus (VV) and ectromelia virus (ECTV). NK cells are regulated through inhibitory and activating signaling receptors, the latter involving NKG2D and the natural cytotoxicity receptors (NCR), NKp46, NKp44 and NKp30. Here we report that VV infection results in an upregulation of ligand structures for NKp30 and NKp46 on infected cells, whereas the binding of NKp44 and NKG2D was not significantly affected. Likewise, infection with ectromelia virus (ECTV), the mousepox agent, enhanced binding of NKp30 and, to a lesser extent, NKp46. The hemagglutinin (HA) molecules from VV and ECTV, which are known virulence factors, were identified as novel ligands for NKp30 and NKp46. Using NK cells with selectively silenced NCR expression and NCR-CD3ζ reporter cells, we observed that HA present on the surface of VV-infected cells, or in the form of recombinant soluble protein, was able to block NKp30-triggered activation, whereas it stimulated the activation through NKp46. The net effect of this complex influence on NK cell activity resulted in a decreased NK lysis susceptibility of infected cells at late time points of VV infection when HA was expression was pronounced. We conclude that poxviral HA represents a conserved ligand of NCR, exerting a novel immune escape mechanism through its blocking effect on NKp30-mediated activation at a late stage of infection

NKp44-NKp44 Ligand Interactions in the Regulation of Natural Killer Cells and Other Innate Lymphoid Cells in Humans

Natural Killer (NK) cells are potent cytotoxic cells belonging to the family of Innate Lymphoid Cells (ILCs). Their most characterized effector functions are directed to the control of aberrant cells in the body, including both transformed and virus-infected cells. NK cell-mediated recognition of abnormal cells primarily occurs through receptor-ligand interactions, involving an array of inhibitory and activating NK receptors and different types of ligands expressed on target cells. While most of the receptors have become known over many years, their respective ligands were only defined later and their impressive complexity has only recently become evident. NKp44, a member of Natural Cytotoxicity Receptors (NCRs), is an activating receptor playing a crucial role in most functions exerted by activated NK cells and also by other NKp44+ immune cells. The large and heterogeneous panel of NKp44 ligands (NKp44L) now includes surface expressed glycoproteins and proteoglycans, nuclear proteins that can be exposed outside the cell, and molecules that can be either released in the extracellular space or carried in extracellular vesicles. Recent findings have extended our knowledge on the nature of NKp44L to soluble plasma glycoproteins, such as secreted growth factors or extracellular matrix (ECM)-derived glycoproteins. NKp44L are induced upon tumor transformation or viral infection but may also be expressed in normal cells and tissues. In addition, NKp44-NKp44L interactions are involved in the crosstalk between NK cells and different innate and adaptive immune cell types. NKp44 expression in different ILCs located in tissues further extends the potential role of NKp44-NKp44L interactions