Titlebook: Digital Education: At the MOOC Crossroads Where the Interests of Academia and Business Converge; 6th European MOOCs S Mauro Calise,Carlos D

直言不諱

https://doi.org/10.1007/978-3-319-65729-5e the time to deliberately plan (before the start) and evaluate (after finishing) their learning process. This insight can serve as starting point for developing supporting tools for learners and personalised dashboards, which can offer the tools at appropriate times in a learner’s course of action.

emulsify

TAP

Socializing on MOOCs: Comparing University and Self-enrolled Studentsforums and seem to interact less than others when the workload increases, which could lead to misleading conclusions when analyzing data. We therefore encourage MOOC researchers to be particularly mindful of this new trend when performing social network analyses.

興奮過度

Goal Setting and Striving in MOOCs: A Peek Inside the Black Box of Learner Behavioure the time to deliberately plan (before the start) and evaluate (after finishing) their learning process. This insight can serve as starting point for developing supporting tools for learners and personalised dashboards, which can offer the tools at appropriate times in a learner’s course of action.

debble

Online Course Production and University Internationalization: Correlation Analysisbsample. Further research regarding annual statistics is required to continue the discussion and to approach the interrelation between MOOC production and its impact on university key performance indicators.

Control-Group

Conference proceedings 2019 restricted to global outreach: the same technology can be used to improve teaching on campus and training inside companies and institutions...The chapter ‘Goal Setting and Striving in MOOCs. A Peek inside the Black Box of Learner Behaviour‘ is open access under a CC BY 4.0 license at link.springer.com..

evanescent

Inscrutable

澄清

Markov Decision Process for MOOC Users Behavioral Inferencedel MOOC users behaviour given their log data. We mold their behavior into a Markov Decision Process framework. We associate the user’s intentions with the MDP reward and argue that this allows us to classify them.