Practitioner Track Proceedings of the 6th International Learning Analytics & Knowledge Conference (LAK16)

Practitioners spearhead a significant portion of learning analytics, relying on implementation and experimentation rather than on traditional academic research. Both approaches help to improve the state of the art. The LAK conference has created a practitioner track for submissions, which first ran in 2015 as an alternative to the researcher track. The primary goal of the practitioner track is to share thoughts and findings that stem from learning analytics project implementations. While both large and small implementations are considered, all practitioner track submissions are required to relate to initiatives that are designed for large-scale and/or long-term use (as opposed to research-focused initiatives). Other guidelines include: • Implementation track record The project should have been used by an institution or have been deployed on a learning site. There are no hard guidelines about user numbers or how long the project has been running. • Learning/education related Submissions have to describe work that addresses learning/academic analytics, either at an educational institution or in an area (such as corporate training, health care or informal learning) where the goal is to improve the learning environment or learning outcomes. • Institutional involvement Neither submissions nor presentations have to include a named person from an academic institution. However, all submissions have to include information collected from people who have used the tool or initiative in a learning environment (such as faculty, students, administrators and trainees). • No sales pitches While submissions from commercial suppliers are welcome; reviewers do not accept overt (or covert) sales pitches. Reviewers look for evidence that a presentation will take into account challenges faced, problems that have arisen, and/or user feedback that needs to be addressed. Submissions are limited to 1,200 words, including an abstract, a summary of deployment with end users, and a full description. Most papers in the proceedings are therefore short, and often informal, although some authors chose to extend their papers once they had been accepted. Papers accepted in 2016 fell into two categories. • Practitioner Presentations Presentation sessions are designed to focus on deployment of a single learning analytics tool or initiative. • Technology Showcase The Technology Showcase event enables practitioners to demonstrate new and emerging learning analytics technologies that they are piloting or deploying. Both types of paper are included in these proceedings

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

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

A framework for strategic planning of data analytics in the educational sector

The field of big data and data analysis is not a new one. Big data systems have been investigated with respect to the volume of the data and how it is stored, the data velocity and how it is subject to change, variety of data to be analysed and data veracity referring to integrity and quality. Higher Education Institutions (HEIs) have a significant range of data sources across their operations and increasingly invest in collecting, analysing and reporting on their data in order to improve their efficiency. Data analytics and Business Intelligence (BI) are two terms that are increasingly popular over the past few years in the relevant literature with emphasis on their impact in the education sector. There is a significant volume of literature discussing the benefits of data analytics in higher education and even more papers discussing specific case studies of institutions resorting on BI by deploying various data analytics practices. Nevertheless, there is a lack of an integrated framework that supports HEIs in using learning analytics both at strategic and operational level. This research study was driven by the need to offer a point of reference for universities wishing to make good use of the plethora of data they can access. Increasingly institutions need to become ‘smart universities’ by supporting their decisions with findings from the analysis of their operations. The Business Intelligence strategies of many universities seems to focus mostly on identifying how to collect data but fail to address the most important issue that is how to analyse the data, what to do with the findings and how to create the means for a scalable use of learning analytics at institutional level. The scope of this research is to investigate the different factors that affect the successful deployment of data analytics in educational contexts focusing both on strategic and operational aspects of academia. The research study attempts to identify those elements necessary for introducing data analytics practices across an institution. The main contribution of the research is a framework that models the data collection, analysis and visualisation in higher education. The specific contribution to the field comes in the form of generic guidelines for strategic planning of HEI data analytics projects, combined with specific guidelines for staff involved in the deployment of data analytics to support certain institutional operations. The research is based on a mixed method approach that combines grounded theory in the form of extensive literature review, state-of-the-art investigation and case study analysis, as well as a combination of qualitative and quantitative data collection. The study commences with an extensive literature review that identifies the key factors affecting the use of learning analytics. Then the research collected more information from an analysis of a wide range of case studies showing how learning analytics are used across HEIs. The primary data collection concluded with a series of focus groups and interviews assessing the role of learning analytics in universities. Next, the research focused on a synthesis of guidelines for using learning analytics both at strategic and operational levels, leading to the production of generic and specific guidelines intended for different university stakeholders. The proposed framework was revised twice to create an integrated point of reference for HEIs that offers support across institutions in scalable and applicable way that can accommodate the varying needs met at different HEIs. The proposed framework was evaluated by the same participants in the earlier focus groups and interviews, providing a qualitative approach in evaluating the contributions made during this research study. The research resulted in the creation of an integrated framework that offers HEIs a reference for setting up a learning analytics strategy, adapting institutional policies and revising operations across faculties and departments. The proposed C.A.V. framework consists of three phases including Collect, Analysis and Visualisation. The framework determines the key features of data sources and resulting dashboards but also a list of functions for the data collection, analysis and visualisation stages. At strategic level, the C.A.V. framework enables institutions to assess their learning analytics maturity, determine the learning analytics stages that they are involved in, identify the different learning analytics themes and use a checklist as a reference point for their learning analytics deployment. Finally, the framework ensures that institutional operations can become more effective by determining how learning analytics provide added value across different operations, while assessing the impact of learning analytics on stakeholders. The framework also supports the adoption of learning analytics processes, the planning of dashboard contents and identifying factors affecting the implementation of learning analytics

DATUS: Dashboard Assessment Usability Model: A case study with student dashboards

The software market sees the appearance of new companies and products every day. This growth translates into the competition, and the survival of companies is reduced to investment in their products. Universities are also interested in improving their product, education. This improvement can be achieved by investing in the learning experience of students. Usability and user experience play an important role and have been a competitive advantage worth investing. Consequently, new methods have emerged to improve the process of evaluating the usability of products. Despite this growth, there is no direct model for assessing the usability of a dashboard. This gap led to the investigation of this dissertation, a proposal for a new model, Dashboard Assessment Usability Model (DATUS), accompanied by an evaluation method, which can be applied to the evaluation of the usability of dashboards. Eight usability dimensions are included in DATUS, each corresponding to a specific usability facet that has been identified in an existing standard or model and decomposed into a total of 20 metrics. In this sense, to verify if the model created is feasible, and as a contribution to Iscte - Instituto Universitário de Lisboa, a prototype dashboard was designed for the Fénix platform, to which the DATUS model was applied. To test the usability of the dashboards, a behavioural study was conducted with 30 Iscte students. After analysing the results, not only was the feasibility of the proposed model and method confirmed, but positive conclusions were also reached regarding the usability of the prototype.O mercado de software observa o aparecimento de novas empresas e produtos todos os dias. Este crescimento traduz-se em competição e a sobrevivência das empresas resume-se ao investimento nos seus produtos. Também as universidades têm interesse em melhorar o seu produto, o ensino. Esta melhoria pode ser alcançada através de investimento na experiência de aprendizagem dos estudantes. A usabilidade e a experiência do utilizador desempenham um papel importante e demonstram ser uma vantagem competitiva em que vale a pena investir. Consequentemente, têm surgido novos métodos para melhorar o processo de avaliação de usabilidade. Apesar deste crescimento, não existe um modelo claro para avaliar a usabilidade de um dashboard. Esta lacuna levou à investigação desta dissertação, uma proposta de um novo modelo, Dashboard Assessment Usability Model (DATUS), acompanhado por um método de avaliação, que pode ser aplicado à avaliação da usabilidade de dashboards. Estão incluídas no DATUS oito dimensões de usabilidade, cada uma corresponde a uma faceta específica de usabilidade que foi identificada numa normalização ou modelo existente, e decompõem-se num total de 20 métricas. Para verificar se o modelo é viável, e como contribuição para o Iscte - Instituto Universitário de Lisboa, foi desenhado um protótipo de dashboard para a plataforma Fénix, à qual o modelo DATUS foi aplicado. Para testar a usabilidade dos dashboards, foi realizado um estudo comportamental com 30 alunos do Iscte. Após a análise dos resultados, foi confirmada a viabilidade do modelo e do método propostos e retiraram-se conclusões positivas em relação à usabilidade do protótipo

Learning Analytics and Online Language learning

This chapter addresses the challenges and future potential of learning analytics. It examines some of the key questions raised by the research literature that will influence language education over the next decade, and investigates what kind of data can be used to inform effective decision-making in online language-learning contexts and how it can be visualized. The chapter turns to consider preliminary data arising from the needs analysis phase of the VITAL Project (Visualization Tools and Analytics to Monitor Online Language Learning and Teaching), a two-year EU-funded project that specifically addresses the gap in the research literature on analytics in language learning and teaching. Turning to the first large-scale project on learning analytics and online language learning, Link & Li's theoretical framework provides a useful starting point to consider the role of dashboards for language learners and instructors