Sunday, June 8, 2008

HCI's role in e-learning

Today the eLearning environment is dramatically changing the way of students, employees and indeed all members of the general public learning new knowledge and performing learning activities. Effective learning occurs where students actively participate in the learning process and when they have ownership of what and how they learn, and are supported in appropriate ways. That is where e-learning has always been successful but still, making elearning sustainable in a traditional education environment involves many challenges.

A major challenge currently faced by e-learning systems’ designers is the development of improved tools better able to engage new learners and sustain their online learning activities any time and anywhere. E-learning systems should be designed in such a way so that its more usable and innovative, supporting creative learning, based on strategies which guides the learners to make the most effective use of the learning content. The approach to e-learning should be

  • learner centered
  • digitally minded
  • research based
  • focused on quality
  • innovative and
  • providing leadership.
Human- Computer Interaction (HCI) theories and methodologies can support the design of appropriate e-learning settings responding to the requirements of today’s e-learning environment which were shown above. It will make e-learning applications smart enough to adapt themselves to the students’ learning styles and to assure high standards of accessibility and usability, in order to make learners’ interaction with the systems as natural and intuitive as possible. In the context of Human-Computer Interaction it is important to consider a perspective that recognizes, respects, values and attempts to accommodate a wide range of human abilities, skills, requirements and preferences in the design of learning material. This automatically reduces the need for a lot of special features. It also encourages individualization, high quality of interaction and, ultimately, end-user acceptability. In short the focus here is always on the human user.

Analysis of learners’ preferred interactions with e-learning environment, and a learner-centered design perspective which takes into account also the typical learning styles shared within the different cultural contexts, are the key factors that would contribute to the successful integration of HCI in e-learning.

Datamining using RapidMiner 4.1

Data mining involves searching through databases for correlations and patterns that differ from results that would be anticipated to occur by chance or in random conditions. The practice of data mining in and of itself is neither good nor bad and the use of data mining has become common in many industries. I participated in a datamining assignment which analyzed the export patterns of Gems and Jewelry in Sri Lanka. the tool used was RapidMiner 4.1. Some most important facts were found out in the process.
The process of data mining consists of three stages:
(1) the initial exploration,
(2) model building or pattern identification with, and
(3) deployment

Stage 1: Exploration
This stage usually starts with data preparation which may involve cleaning data, data transformations, selecting subsets of records and - in case of data sets with large numbers of variables ("fields") - performing some preliminary feature selection operations to bring the number of variables to a manageable range

The figure shows a screenshot of a graph which was made on the gem exports using Rapid Miner.

Stage 2: Model building and validation

The dimensional model must suit the requirements of the users and support ease of use for direct access. The model must also be designed so that it is easy to maintain and can adapt to future changes.

The figure shows a 2D Model View of the data.

The model design must result in a relational database that supports OLAP cubes to provide instantaneous query results for analysts. a typical dimensional model uses a star or snowflake design that is easy to understand and relate to business needs, supports simplified business queries, and provides superior query performance by minimizing table joins.

Stage 3: Deployment
That final stage involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome.

I have attached my datamining asignment report here,