Data is sometimes seen as something cold and removed from the human element, but in reality, it is a window into that very humanity, and can form an essential foundation for keeping students on track.
The Experience API (xAPI for short) is far more than just an update to SCORM, the popular standard for tracking data from a learning management system. xAPI opens up a whole new world of possibilities for learning analytics. Examples of what real organizations are doing with it in real-life situations make it easier to grasp the scale of this advance and apply the learnings to your own situation.
As part of a Title III federal grant awarded to Bay Path University, the institution has planned to create a predictive model for traditional undergraduate persistence and another for financial aid warning flags. This is final installment in a series of posts that follow the steps in the CRISP-DM framework on our journey to produce the former.
Durch digitale Lernplattformen können vermehrt Daten über Lernende, Lerninhalte und die Lernsituation ausgewertet werden. Die algorithmische Analyse nennt sich Learning Analytics. Diese Analyse ermöglicht einen individuellen Lernprozess sowie eine Früherkennung von Lernschwächen. Learning Analytics bergen allerdings auch einige Nachteile.
Some students need more support than others to get their education. In today's Academic Minute, the University of South Florida's Paul Dosal describes how to identify these students early on. Dosal is a professor of Latin American history at USF
Smarte Ausstattung: Um das Lernen der Zukunft digital zu gestalten, benötigen Schulen passende technische Ausstattung. Die Auswahl ist jedoch jedoch rießig.
LMS Reporting is a crucial feature of any modern online course and it shows full advantage of Big Data nowadays. But admit it, just looking at figures and metrics is of little use.
Jeff Greene and Matt Bernacki are learning scientists in the UNC-Chapel Hill School of Education. They leverage the data that students create when they use digital resources to help them learn.
Begona Nunez-Herran and Kevin Mayles (Data and Student Analytics), Rebecca Ward (Data Strategy and Governance), Prof Bart Rienties & PhD students (Institute of Educational Technology), Prof John Domingue (Knowledge Media Institute) & Dr Thea Herodotou (IET)
This Moodle plugin adds a new predictive model to identify students that are likely to miss assignment due dates. The model automatically generates insights for teachers about these students.
With predictive analytics, colleges and universities are able to “nudge” individuals toward making better decisions and exercising rational behavior to enhance their probabilities of success.
The Inspire plugin implements open source, transparent next-generation learning analytics using machine learning backends that go beyond simple descriptive analytics to provide predictions of learner success, and ultimately diagnosis and prescriptions (advisements) to learners and teachers. From Moodle HQ.
Data and analytics are at the center of today’s student success movement. Increased interest and access to institutional data are helping colleges and universities identify systematic and structural barriers to retention and graduation. Predictive analytics is making proactive advising possible at scale.
This study aims at providing explanations of students’ behaviors on LMS by incorporating dispositional dimensions (e.g., self-regulation and emotions) into conventional learning analytics models. Using a combination of demographic, trace, and self-reported data.
Should students be told what the data predict about their chances of success? Corporate leaders in predictive analytics business consider the issue posed by an Inside Higher Ed blogger.
Not all learning analytics are the same. Discover how proactive learning analytics help you influence and improve ongoing learning processes by predicting the future and creating recommendations for action. Identify the 4 key elements that will determine the success of your analytics journey.