Adaptive learning algorithms and data cloning Citation Pratap, Amrit Adaptive learning algorithms and data cloning. It summarizes three of my research projects. We first investigate the role of margins in the phenomenal success of the Boosting Algorithms.
The environment was designed to accommodate individual learning styles while students progressed through a computer programming course. Despite the benefits of individualised instruction and a growing worldwide e-learning market, there is a paucity of guidance on how to effectively accommodate learning styles in an online environment.
Consequently, these environments rarely offer the learner choices between different versions of content. However, these choices could cater for flexible learning styles, promote cognitive flexibility, and increase learner control.
The first research question underlying the project asked how learning styles could be accommodated in an adaptive e-learning environment.
The second question asked whether a dynamically adaptive environment that provides the learner with a choice of media experiences is more beneficial than a statically adapted environment. To answer these questions, an adaptive e-learning environment named iWeaver was created and experimentally evaluated.
These experiences and tools were based on the perceptual and information processing dimension of an adapted version of the Dunn and Dunn learning styles model. An experimental evaluation of iWeaver was conducted with 63 multimedia students. The analysis investigated the effect of having a choice of multiple media experiences compared to having just one static media experience on learning gain, enjoyment, perceived progress, and motivation.
In addition to these quantitative measurements, learners provided qualitative feedback at the end of each lesson. Data from 27 participants were sufficiently complete to be analysed. For the data analysis, participants were divided into two groups of high and low interest in programming and Java, then into two groups of high and low experience with computers and the Internet.
Both group comparisons revealed statistically significant differences for the effect of choice. Having a choice of media experiences proved beneficial for learners with low experience but detrimental for learners with high experience or interest.
It is hypothesised that encouraging a more active learner role in educational systems would expand the positive influence of choice to a wider range of learners.
The study has contributed some weight to the argument that for certain groups of learners, it is more beneficial to view learning style as a flexible, rather than a stable construct. As a practical implication, it seems advisable to collect data on prior experience, interest, and the initial learning style distribution of the target audience before developing environments comparable to iWeaver.This thesis is brought to you for free and open access by Schol[email protected] Amherst.
For more information, please contact [email protected] Schnarr, Lindsay M., "Pendulum Performing Arts Center: Adaptive Reuse Design of the Historic Court Square Building in Springfield, this adaptive reuse project aims to bring the public.
our opinion, adaptive learning has not been yet adequately addressed in any e-learning specification or standard and requires a further research effort. This project represents a contribution to this task.
Thesis Capstone Projects, Fall MindUI. Alvaro Olsen. MindUI is a web based toolkit for the research and analysis of affective learning. These tools can be used to collect, visualize and correlate learner's affective information.
An adaptive e-Learning system using knowledge management Abstract: Knowledge Management is an emerging, interdisciplinary business model that has knowledge within the framework of an organization as its focus.
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E-learning Uncovered. APRIL 5, Authoring Tool eLearning tools training eLearning projects eLearning samples online samples training projects This post provides four quick. A personalized adaptive e-learning approach based on semantic web technology Maryam Yarandi School of Computing Information Technology and Engineering, University of East London, London, UK.
E-mail: (at) kaja-net.com Hossein Jahankhani.