Cognitive Styles and Adaptive Web-based Learning

Adaptive hypermedia techniques have been widely used in web-based learning programs. Traditionally these programs have focused on adapting to the user’s prior knowledge, but recent research has begun to consider adapting to cognitive style. This study aims to determine whether offering adapted interfaces tailored to the user’s cognitive style would improve their learning performance and perceptions. The findings indicate that adapting interfaces based on cognitive styles cannot facilitate learning, but mismatching interfaces may cause problems for learners. The results also suggest that creating an interface that caters for different cognitive styles and gives a selection of navigational tools might be more beneficial for learners. The implications of these findings for the design of web-based learning programs are discussed

Children's preferences in types of assignments

Thesis (Ed.M.)--Boston Universit

Has the Internet improved medical student information literacy skills? A retrospective case study: 1995-2005

Our goal in this investigation was to see if the popularity of the Internet has had an effect on searching skills and an increased awareness of where to search for appropriate medical information

Adolescent Literacy and Textbooks: An Annotated Bibliography

A companion report to Carnegie's Time to Act, provides an annotated bibliography of research on textbook design and reading comprehension for fourth through twelfth grade, arranged by topic. Calls for a dialogue between publishers and researchers

Adaptive Neural Compilation

This paper proposes an adaptive neural-compilation framework to address the problem of efficient program learning. Traditional code optimisation strategies used in compilers are based on applying pre-specified set of transformations that make the code faster to execute without changing its semantics. In contrast, our work involves adapting programs to make them more efficient while considering correctness only on a target input distribution. Our approach is inspired by the recent works on differentiable representations of programs. We show that it is possible to compile programs written in a low-level language to a differentiable representation. We also show how programs in this representation can be optimised to make them efficient on a target distribution of inputs. Experimental results demonstrate that our approach enables learning specifically-tuned algorithms for given data distributions with a high success rate.Comment: Submitted to NIPS 2016, code and supplementary materials will be available on author's pag