It never bodes well to dive into the unknown without preparation. To define, design and enable learning analytics, it’s essential to have a clear strategy in place. Prep yourself with these evaluation questions before you dive into learning analytics.
Game Learning Analytics (GLA) is the process of applying Learning Analytics techniques to Serious Games in order to get insight about how the game is being used and improve the educational experience.
For about 10 years, from 2005 – 2015, much of the discussion about tracking eLearning revolved around the Shareable Content Object Reference Model (SCORM) and learning management systems (LMS).
As Massive Open Online Courses (MOOCs) generate a huge amount of learning activity data through its thousands of users, great potential is provided to use this data to understand and optimize the learning experience and outcome.
I'll start this article by making one simple statement: Feedback loops work. Why? That’s the way we human beings learn, as feedback provides us with a sense of where we stand and an evaluation of our progress.
An interesting question arose at a recent xAPI Camp hosted by The eLearning Guild: “What happened to objectives in xAPI?” We should be able to use xAPI to document successful completion of eLearning, but without statements of learning objectives in the content, this is not possible.
M. Ochsner, S. Hug, and H. Daniel (Eds.).
. SpringerLinkSpringer eBook Collection Springer, Cham, (2016)Paketwechsel, der Titel wurde zuerst für ZDB-2-EDA, Jahr 2016 ausgeliefert (SWB); This book analyses and discusses the recent developments for assessing research quality in the humanities and related fields in the social sciences. Research assessments in the humanities are highly controversial and the evaluation of humanities research is delicate. While citation-based research performance indicators are widely used in the natural and life sciences, quantitative measures for research performance meet strong opposition in the humanities. This volume combines the presentation of state-of-the-art projects on research assessments in the humanities by humanities scholars themselves with a description of the evaluation of humanities research in practice presented by research funders. Bibliometric issues concerning humanities research complete the exhaustive analysis of humanities research assessment. The selection of authors is well-balanced between humanities scholars, research funders, and researchers on higher education. Hence, the edited volume succeeds in painting a comprehensive picture of research evaluation in the humanities. This book is valuable to university and science policy makers, university administrators, research evaluators, bibliometricians as well as humanities scholars who seek expert knowledge in research evaluation in the humanities.
L. Arras, A. Osman, K. Müller, and W. Samek. Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, page 113--126. Florence, Italy, Association for Computational Linguistics, (August 2019)
B. Letham, C. Rudin, T. McCormick, and D. Madigan. (2015)cite arxiv:1511.01644Comment: Published at http://dx.doi.org/10.1214/15-AOAS848 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org).