higher ed
Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning
The results show that learners’ engagement with the content, exercises and mentoring of a blended learning environment, final academic performance could be predicted when only one-third of the course had elapsed. Furthermore, the blended data set combining online and traditional critical factors (including face-to-face mentoring after class) resulted in the best learner performance.
Journal of Educational Technology & Society, Vol. 21, No. 2 (April 2018), pp. 220-232
Higher academic performance in an Asian University : replacing traditional lecturing with blended learning
The mean of academic performance achieved in blended learning is higher than that in traditional lecturing; furthermore, traditional lecturing can be eliminated from higher education without diminishing the learning. Attendance is increased significantly, and appears to be a very effective deep learning approach.
Nanyang Technological University Library, Singapore.
Integrating educational knowledge: reactivation of prior knowledge during educational learning enhances memory integration
Reactivation of prior knowledge during new learning and congruency of prior knowledge with new learning are beneficial to memory formation.
npj Science of Learningvolume 3, Article number: 11 (2018)
The learning benefits of teaching: A retrieval practice hypothesis
Teaching educational materials to others enhances the teacher's own learning of those to‐be‐taught materials, although the underlying mechanisms remain largely unknown. The learning‐by‐teaching benefit is possibly a retrieval benefit.
Applied Cognitive Psychology Volume 32, Issue 3, May/June 2018, Pages 401-410
The Effects of Overlearning and Distributed Practise on the Retention of Mathematics Knowledge
Long-term retention of mathematics knowledge was boosted by distributed practise and unaffected by overlearning.
Applied Cognitive Psychology Vol 20: 1209–1224 (2006)
Experiences in Introducing Blended Learning in an Introductory Programming Course
Though programming exercises can be efficiently tested against expected output, the assessment systems often only deliver feedback regarding the correctness and sometimes additionally which test cases fail. But so far, these systems are not able to identify the reason for the error. Therefore, there should also be human tutors available with which students can discuss their solution and which can help them in identifying their misconceptions.
ECSEE'18 Proceedings of the 3rd European Conference of Software Engineering Education