Computer-aided learning: experiences, perspectives and promises.

A M Abdulla; J S Henke; L O Watkins

doi:10.1016/s0735-1097(87)80064-5

Home/Archive/PubMed

PubMed · 3546462

Computer-aided learning: experiences, perspectives and promises.

A M Abdulla·J S Henke·L O Watkins

View original source ↗Export metadata ↓

Abstract

Despite the recent strong interest in computer-aided learning, very few high quality programs are available. This article describes an authoring system that was designed to help faculty at medical schools develop a library of effective computer-based lessons. Features of the system include ease of authoring and the ability to incorporate natural language input by the learner, model complex situations, keep track of 100 performance variables and employ interactive laser video disk technology. The authors describe the experience that has been gained in the development and implementation of computer-aided learning at a medical school, and reflect on its future applications and value.

Explore related subjects

Computer Simulation Computer-Assisted Instruction Education, Medical Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

A M Abdulla, J S Henke, L O Watkins. 1987. Computer-aided learning: experiences, perspectives and promises.. https://doi.org/10.1016/s0735-1097(87)80064-5

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗

PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗

PubMed1999

Monte Carlo estimation of extrapolation of quality-adjusted survival for follow-up studies.

The expected quality-adjusted survival (QAS) for an index population with a specific disease can be estimated by summing the product of the survival function and the mean quality of life function of the population. In many follow-up studies with heavy censoring, the expected QAS may not be well estimated due to the lack of data beyond the close of follow-up. In this paper, we first created a reference population from the life tables of the general population according to the Monte Carlo method. Secondly, we fitted a simple linear regression line to the logit of the ratio of quality-adjusted survival functions for the index and reference populations up to the end of follow-up. Finally, combining information on the reference population with the fitted line, we predicted the expected quality-adjusted survival curve beyond the follow-up period for the index population. Simulation studies have shown that the simple Monte Carlo estimation procedure is a potential approach for estimating expected QAS and the survival function beyond the follow-up with a certain degree of accuracy.

Computer Simulation↗