Learning Analytics in het onderwijs:Een onderwijskundig perspectief

https://www.surf.nl/kennisbank/2016/rapport-learning-analytics-in-het-onderwijs-een-onderwijskundig-perspectief.htmlLearning analytics in de onderwijspraktijk Meer inzicht in het onderwijsproces, gerichte feedback aan studenten en uiteindelijk verbetering van het onderwijs: dat is de gedachte achter learning analytics. De mogelijkheden van learning analytics zijn groot, maar hoe past een opleiding of instelling ze succesvol toe? Dat valt of staat met de manier waarop learning analytics wordt toegepast in de onderwijspraktijk. Ontwerpen van online onderwijs Learning analytics werkt pas echt als we erin slagen de juiste vragen aan de data te stellen. Dat begint al bij het ontwerpen van online onderwijs. Voor het rapport 'Learning analytics in het onderwijs: een onderwijskundig perspectief' hebben we samen met vertegenwoordigers uit het hoger onderwijs onderzocht hoe je in een onderwijsontwerp effectief gebruik kunt maken van learning analytics. In een aantal cases laten we bovendien zien hoe dat in de onderwijspraktijk kan werken. Ondersteuning en inspiratie voor docenten en onderwijsontwikkelaars Het rapport ondersteunt en inspireert docenten en onderwijsontwikkelaars bij het toepassen van learning analytics in online onderwijs. Zo kunnen ze data verzamelen over hoe studenten door een online omgeving klikken, welke video’s ze bekijken, en welke andere digitale voetsporen ze achterlaten, en wat dat zegt over hun leergedrag.SUR

User-centric evaluation of recommender systems in social learning platforms: Accuracy is just the tip of the iceberg

Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But the ultimate goal of a recommender system is to increase user satisfaction. Therefore, evaluations that measure user satisfaction should also be performed before deploying a recommender system in a real target environment. Such evaluations are laborious and complicated compared to the traditional, data-centric evaluations, though. In this study, we carried out a user-centric evaluation of state-of-the-art recommender systems as well as a graph-based approach in the ecologically valid setting of an authentic social learning platform. We also conducted a data-centric evaluation on the same data to investigate the added value of user-centric evaluations and how user satisfaction of a recommender system is related to its performance in terms of accuracy metrics. Our findings suggest that user-centric evaluation results are not necessarily in line with data-centric evaluation results. We conclude that the traditional evaluation of recommender systems in terms of prediction accuracy only does not suffice to judge performance of recommender systems on the user side. Moreover, the user-centric evaluation provides valuable insights in how candidate algorithms perform on each of the five quality metrics for recommendations: usefulness, accuracy, novelty, diversity, and serendipity

Learning Analytics and eAssessment: Towards Computational Psychometrics by Combining Psychometrics with Learning Analytics

From a psychometric point of view, assessment means to infer what a learner knows and can do in the real world from limited evidence observed in a standardized testing situation. From a learning analytics perspective assessment means to observe real behavior in digital learning environments to conclude the learner status with the intent to positively influence the learning process. Although psychometrics and learning analytics share similar goals, for instance, formative assessment, while applying different methods and theories, the two disciplines are so far highly separated. This chapter aims at paving the way for an advanced understanding of assessment by comparing and integrating the learning analytics and the psychometric approach of assessment. We will discuss means to show this new way of assessment of educational concepts such as (meta-) cognition, motivation, and reading comprehension skills that can be addressed either from data-driven approach (learning analytics) or from a theory-driven approach (psychometrics). Finally, we show that radically new ways of assessment are located in the middle space where both disciplines are combined into a new research discipline called ‘Computational Psychometrics’

Adopting Trust in Learning Analytics Infrastructure:A Structured Literature Review

One key factor for the successful outcome of a Learning Analytics (LA) infrastructure is the ability to decide which software architecture concept is necessary. Big Data can be used to face the challenges LA holds. Additional challenges on privacy rights are introduced to the Europeans by the General Data Protection Regulation (GDPR). Beyond that, the challenge of how to gain the trust of the users remains. We found diverse architectural concepts in the domain of LA. Selecting an appropriate solution is not straightforward. Therefore, we conducted a structured literature review to assess the state-of-the-art and provide an overview of Big Data architectures used in LA. Based on the examination of the results, we identify common architectural components and technologies and present them in the form of a mind map. Linking the findings, we are proposing an initial approach towards a Trusted and Interoperable Learning Analytics Infrastructure (TIILA). (DIPF/Orig.