Automatic website comprehensibility evaluation

Abstract

The Web provides easy access to a vast amount of informational content to the average person, who may often be interested in selecting websites that best match their learning objectives and comprehensibility level. Web content is generally not tagged for easy determination of its instructional appropriateness and comprehensibility level. Our research develops an analytical model, using a group of website features, to automatically determine the comprehensibility level of a website. These features, selected from a large pool of website features quantitatively measured, are statistically shown to be significantly correlated to website comprehensibility based on empirical studies. The automatically inferred comprehensibility index may be used to assist the average person, interested in using web content for self-directed learning, to find content suited to their comprehension level and filter out content which may have low potential instructional value. © 2007 IEEE.