Augmenting Assessment with Learning Analytics

Learning analytics as currently deployed has tended to consist of large-scale analyses of available learning process data to provide descriptive or predictive insight into behaviours. What is sometimes missing in this analysis is a connection to human-interpretable, actionable, diagnostic information. To gain traction, learning analytics researchers should work within existing good practice particularly in assessment, where high quality assessments are designed to provide both student and educator with diagnostic or formative feedback. Such a model keeps the human in the analytics design and implementation loop, by supporting student, peer, tutor, and instructor sense-making of assessment data, while adding value from computational analyses

Issues and challenges for implementing writing analytics at higher education

Effective written communication is an essential skill which promotes educational success for undergraduates. One of the key requirements of good academic writing in higher education is that students must develop a critical mind, and learn how to construct sound arguments in their discipline. Writing Analytics focuses on the measurement and analysis of written texts to improve the teaching and learning of writing, and is being developed at the intersection of fields such as automated assessment, and computational linguistics. Since writing is an activity that is deeply human, its association with computational formulations is double-edged. This chapter discusses issues and challenges for implementing writing analytics in higher education through theoretical considerations that emerge from the literature review and an example application. It includes findings from empirical research conducted with academic tutors of the Open University, UK on adopting writing analytics to support their feedback processes, which reveal the preconceptions that academic tutors have had about the use of writing analytics specifically concerns centered around the privacy and ethical aspects

Caracterización del desafío de educación superior para implementar un modelo de puntuación de ensayos automatizado para universidades con un sistema de evaluación de aprendizaje tradicional actual

Higher education is currently challenged to respond to a massive interest in learning with a current model that shows increasing evidence of too much cost, effort and decreasing efficacy of the operational learning process. Artificial intelligence has gained presence as a solution, but the integration process is already reporting problems and will not be implemented easily, in particular for universities with low degree of automation integrated to their systems. Universities need to quickly adapt and develop organizational and individual competencies, and clarity about the elements for new learning evaluation systems. This work contributes to propose a model to help universities to define these new systems making the most of artificial intelligence tools for academic essays scoring

Caracterización del desafío de educación superior para implementar un modelo automatizado de puntuación de ensayos para universidades con un sistema de evaluación de aprendizaje tradicional actual

La educación superior actualmente tiene el desafío de responder a un interés masivo en el aprendizaje con un modelo actual que muestra evidencia creciente de demasiado costo, esfuerzo y disminución de la eficacia del proceso de aprendizaje operacional. La inteligencia artificial ha ganado presencia como una solución, pero el proceso de integración ya está reportando problemas y no se implementará fácilmente, en particular para las universidades con bajo grado de automatización integrada a sus sistemas. Las universidades deben adaptarse y desarrollar rápidamente competencias organizativas e individuales, y claridad sobre los elementos para los nuevos sistemas de evaluación de aprendizaje. Este trabajo contribuye a proponer un modelo para ayudar a las universidades a definir estos nuevos sistemas que aprovechan al máximo las herramientas de inteligencia artificial para la calificación de ensayos académicos. © 2019, Springer Nature Switzerland A