The Impact of Learning Analytics on the Dutch Education System

The article reports the findings of a Group Concept Mapping study that was conducted within the framework of the Learning Analytics Summer Institute (LASI) in the Netherlands. Learning Analytics are expected to be beneficial for students and teacher empowerment, personalization, research on learning design, and feedback for performance. The study depicted some management and economics issues and identified some possible treats. No differences were found between novices and experts on how important and feasible are changes in education triggered by Learning Analytics

Ethical and privacy issues in the application of learning analytics

The large-scale production, collection, aggregation, and processing of information from various learning platforms and online environments have led to ethical and privacy concerns regarding potential harm to individuals and society. In the past, these types of concern have impacted on areas as diverse as computer science, legal studies and surveillance studies. Within a European consortium that brings together the EU project LACE, the SURF SIG Learning Analytics, the Apereo Foundation and the EATEL SIG dataTEL, we aim to understand the issues with greater clarity, and to find ways of overcoming the issues and research challenges related to ethical and privacy aspects of learning analytics practice. This interactive workshop aims to raise awareness of major ethics and privacy issues. It will also be used to develop practical solutions to advance the application of learning analytics technologies

Analíticas en acción: Evaluando la efectividad e impacto de intervenciones basadas en evidencia en cursos en línea

Investigating effectiveness of learning analytics is a major topic of research, with a recent systematic review finding 689 papers in this field (Larrabee Sonderlund et al., 2019). Few of these (11 out of 689) highlight the potential of interventions based on learning analytics. The Open University UK (OU) is one of few institutions to systematically develop and implement a learning analytics framework at scale. This paper reviews the impact of one part of this framework - the Analytics for Action (A4A) process, focusing on the 2017-18 academic year and reviewing both feedback from module teams and interventions coming out of the process. The A4A process includes hands-on training for staff, followed by data support meetings with educators when the course is live to students. The aim being to help educators with making informed, evidence-based interventions to aid student retention and engagement.  Findings from this study indicate that participants are satisfied with the training and that the data support meetings are helping in providing new perspectives on the data. The scope and nature of actions taken by module teams varies widely, ranging from no intervention at all to interventions spanning over multiple presentations. In some cases, measuring the impact of the actions taken will require data analysis from further presentations. The paper also presents findings indicating room for improvement in the follow up of the actions agreed, support given to module teams to implement such actions and final evaluation of impact on student outcomes.La efectividad del uso de las analíticas de aprendizaje es un tópico de gran relevancia en la literatura sobre el tema. Una revisión sistemática reciente encontró 689 artículos en este campo (Larrabee Sonderlund et al., 2019). Sin embargo, solamente 11 de los 689 artículos destacan el potencial de las intervenciones basadas directamente en el análisis de los datos disponibles. La Open University UK (OU) es una de las pocas instituciones que desarrolla e implementa sistemáticamente un marco de uso de las análiticas a gran escala. Este documento revisa el impacto de una parte de este marco: el proceso de Analytics for Action (A4A). Utilizando datos del curso académico 2017-18, revisamos los comentarios de los participantes y las intervenciones acordadas como parte  del proceso.El proceso A4A implica la capacitación práctica del personal, seguida de reuniones sucesivas en las que se discuten los datos cuando el curso ya está disponible en línea. El objetivo del proceso es ayudar a los educadores a planificar y realizar intervenciones  basadas en la evidencia, con el fin de mejorar  la retención y satisfacción de los estudiantes. Los resultados de este estudio indican que los participantes están satisfechos con la capacitación y que las reuniones de apoyo están ayudando a proporcionar nuevas perspectivas sobre los datos. El alcance y la naturaleza de las intervenciones varían ampliamente, desde la no intervención hasta intervenciones que abarcan múltiples presentaciones (cohortes) del curso. En algunos casos, medir el impacto real de las acciones tomadas requerirá el análisis de los datos de otras presentaciones. El trabajo también presenta hallazgos que indican que todavía hay margen para mejorar el seguimiento de las acciones acordadas, el apoyo brindado a los equipos académicos para implementar tales acciones y la evaluación final del impacto en los resultados y satisfacción de los estudiantes

Towards highly informative learning analytics

Among various trending topics that can be investigated in the field of educational technology, there is a clear and high demand for using artificial intelligence (AI) and educational data to improve the whole learning and teaching cycle. This spans from collecting and estimating the prior knowledge of learners for a certain subject to the actual learning process and its assessment. AI in education cuts across almost all educational technology disciplines and is key to many other technological innovations for educational institutions. The use of data to inform decision-making in education and training is not new, but the scope and scale of its potential impact on teaching and learning have silently increased by orders of magnitude over the last few years. The release of ChatGPT was another driver to finally make everyone aware of the potential effects of AI technology in the digital education system of today. We are now at a stage where data can be automatically harvested at previously unimagined levels of granularity and variety. Analysis of these data with AI has the potential to provide evidence-based insights into learners’ abilities and patterns of behaviour that, in turn, can provide crucial action points to guide curriculum and course design, personalised assistance, generate assessments, and the development of new educational offerings. AI in education has many connected research communities like Artificial Intelligence in Education (AIED), Educational Data Mining (EDM), or Learning Analytics (LA). LA is the term that is used for research, studies, and applications that try to understand and support the behaviour of learners based on large sets of collected data

Tracing the creation and evaluation of accessible Open Educational Resources through learning analytics

The adoption of Open Educational Resources (OER) has been continuously growing and with it the need to addressing the diversity of students’ learning needs. Because of that, OER should meet with characteristics such as the web accessibility and quality. Thus, teachers as the creators of OER need supporting tools and specialized competences. The main contribution of this thesis is a Learning Analytics Model to Trace the Creation and Evaluation of OER (LAMTCE) considering web accessibility and quality. LAMTCE also includes a user model of the teacher’s competences in the creation and evaluation of OER. Besides that, we developed ATCE, a learning analytics tool based on the LAMTCE model. Finally, it was carried out an evaluation conducted with teachers involving the use of the tool and we found that the tool really benefited teachers in the acquisition of their competences in creation and evaluation of accessible and quality OER.La adopción de Recursos Educativos Abiertos (REA) ha ido en aumento y con ello la necesidad de abordar la diversidad de necesidades de aprendizaje de los estudiantes. Por ello, los REA deben cumplir con características tales como la accesibilidad web y la calidad. Así, los profesores como los creadores de REA necesitan de herramientas de soporte y competencias especializadas. La principal contribución de la tesis es el modelo LAMTCE, un modelo de analíticas de aprendizaje para hacer seguimiento a la creación y evaluación de REA considerando la accesibilidad web y la calidad. LAMTCE también incluye un modelo de usuario de las competencias del profesor en creación y evaluación de REA. Además, se desarrolló ATCE, una herramienta de analíticas de aprendizaje que está basada en el modelo LAMTCE. Finalmente, se llevó a cabo un estudio con profesores involucrando el uso de la herramienta encontrando que ésta realmente benefició a los profesores en la adquisición de sus competencias en creación y evaluación de REA accesibles y de calidad

Big data analytics and organisational change. The case of learning analytics

Much of the Information Systems (IS) literature on Big Data Analytics (BDA) assumes a straightforward relationship between human activity and data, and between data and analytical insights that can be used to steer operations (e.g. Chen, Preston and Swink, 2015; Brynjolfsson, Geva and Reichman, 2016; Yahav, Shmueli and Mani, 2016). On the other hand, researchers also try to understand the role of big data within organisations, the contributions of analytics to strategy and decision-making, and the value of big data and its organisational consequences (Constantiou and Kallinikos, 2015; Abbasi, Sarker and Chiang, 2016; Günther et al., 2017). At the same time, more critical scholars have suggested that the implications of BDA can go beyond decision-making, sometimes twisting or even undermining managerial efforts (Newell and Marabelli, 2015; Galliers et al., 2017; Markus, 2017). This research investigates how BDA systems change organisations that implement them and aims to uncover the resulting organisational transformations. In line with the Transformational Model of Social Activity (Archer and Bhaskar, 1998; Faulkner and Runde, 2013), it is argued that BDA systems as technological objects change how work is done, and these changes lead to the reproduction or transformation of organisations as social structures. In order to uncover this reproduction or transformation, the concepts of encoding, aggregation and correlation (Alaimo and Kallinikos, 2017) are deployed to analyse how data is produced, and the theory of reactivity (Espeland and Sauder, 2007), originally developed to study university rankings, is adapted to trace the mechanisms and effects of organisational transformation in a case study. The study provides an answer to the question of how organisations are transformed, in unintended ways, through the implementation of BDA systems. The concept of the analytical cage is proposed as a new form of organising emerging from BDA within organisations

The Impact of Learning Analytics on the Dutch Education System

The article reports the findings of a Group Concept Mapping study that was conducted within the framework of the Learning Analytics Summer Institute (LASI) in the Netherlands. Learning Analytics are expected to be beneficial for students and teacher empowerment, personalization, research on learning design, and feedback for performance. The study depicted some management and economics issues and identified some possible treats. No differences were found between novices and experts on how important and feasible are changes in education triggered by Learning Analytics