Learning Analytics Community Exchange: Evidence Hub

This poster sets out the background and development of the LACE Evidence Hub, a site that gathers evidence about learning analytics in an accessible form. The poster also describes the functionality of the site, summarises its quantitative and thematic content to date and the state of evidence. In addition, it encourages people to add to and make use of the Hub

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers

Guest Editorial: Ethics and Privacy in Learning Analytics

The European Learning Analytics Community Exchange (LACE) project is responsible for an ongoing series of workshops on ethics and privacy in learning analytics (EP4LA), which have been responsible for driving and transforming activity in these areas. Some of this activity has been brought together with other work in the papers that make up this special issue. These papers cover the creation and development of ethical frameworks, as well as tools and approaches that can be used to address issues of ethics and privacy. This editorial suggests that it is worth taking time to consider the often intertangled issues of ethics, data protection and privacy separately. The challenges mentioned within the special issue are summarised in a table of 22 challenges that are used to identify the values that underpin work in this area. Nine ethical goals are suggested as the editors’ interpretation of the unstated values that lie behind the challenges raised in this paper

The Complexities of Developing a Personal Code of Ethics for Learning Analytics Practitioners: Implications for Institutions and the Field

In this paper we explore the potential role, value and utility of a personal code of ethics (COE) for learning analytics practitioners, and in particular we consider whether such a COE might usefully mediate individual actions and choices in relation to a more abstract institutional COE. While several institutional COEs now exist, little attention has been paid to detailing the ethical responsibilities of individual practitioners. To investigate the problems associated with developing and implementing a personal COE, we drafted an LA Practitioner COE based on other professional codes, and invited feedback from a range of learning analytics stakeholders and practitioners: ethicists, students, researchers and technology executives. Three main themes emerged from their reflections: 1. A need to balance real world demands with abstract principles, 2. The limits to individual accountability within the learning analytics space, and 3. The continuing value of debate around an aspirational code of ethics within the field of learning analytics

Linking students' timing of engagement to learning design and academic performance

In recent years, the connection between Learning Design (LD) and Learning Analytics (LA) has been emphasized by many scholars as it could enhance our interpretation of LA findings and translate them to meaningful interventions. Together with numerous conceptual studies, a gradual accumulation of empirical evidence has indicated a strong connection between how instructors design for learning and student behaviour. Nonetheless, students' timing of engagement and its relation to LD and academic performance have received limited attention. Therefore, this study investigates to what extent students' timing of engagement aligned with instructor learning design, and how engagement varied across different levels of performance. The analysis was conducted over 28 weeks using trace data, on 387 students, and replicated over two semesters in 2015 and 2016. Our findings revealed a mismatch between how instructors designed for learning and how students studied in reality. In most weeks, students spent less time studying the assigned materials on the VLE compared to the number of hours recommended by instructors. The timing of engagement also varied, from in advance to catching up patterns. High-performing students spent more time studying in advance, while low-performing students spent a higher proportion of their time on catching-up activities. This study reinforced the importance of pedagogical context to transform analytics into actionable insights

Understanding Evidence-Based Interventions for Cross-Cultural Group Work: A Learning Analytics Perspective

As the numbers of international students worldwide continue to rise, one common challenge is how best to socially integrate diverse groups of students. Indeed, research demonstrates that many students form social and learning relationships with those from the same cultural background, despite benefits of cross-cultural communication. This lack of social cohesion negatively affects students, particularly when it comes to their perceptions of collaborative group work. However, few studies have analysed measurable student behaviours in group work, such as with learning analytics, to determine how culture and existing social networks influence measurable differences in contributions. Similarly, little is known about what evidence-based interventions lead to more equal participation between diverse students. In this research, learning analytics is combined with social network analysis to determine the role of social connections on group work participation, and highlight replicable interventions that can help promote social cohesion in diverse classrooms

Learning at Scale: Using an Evidence Hub To Make Sense of What We Know

The large datasets produced by learning at scale, and the need for ways of dealing with high learner/educator ratios, mean that MOOCs and related environments are frequently used for the deployment and development of learning analytics. Despite the current proliferation of analytics, there is as yet relatively little hard evidence of their effectiveness. The Evidence Hub developed by the Learning Analytics Community Exchange (LACE) provides a way of collating and filtering the available evidence in order to support the use of analytics and to target future studies to fill the gaps in our knowledge

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