Abstract: Systems and methods are disclosed enabling recommendations of content items based on a difficulty of the content item as well as a skill level of the user. Skill-based content recommendations may be utilized, for example, in recommending content to language learners. Skill-based recommendations may be based on a variety of difficulty metrics of the content item, such as vocabulary and complexity of the language (e.g., words per paragraph, syllables per word, etc.), as well as a variety of skill metrics of the user (e.g., as explicitly provided by the user or implicitly determined based on a user's interaction with content items). Advantageously, such metrics can enable generation of recommendations based on a multi-dimensional difficulty assessment. Further, difficulty metrics, skill metrics, or the relationship between such metrics may be dynamically updated over time based on continued feedback from users, such that recommendations are dynamically improved.

Abstract: Techniques for real-time observation assessment are provided. The techniques, which are designed for educators, take advantage of handheld computers, desktop/laptop computers and Internet access in order to reduce the paperwork associated with conventional educational assessments. An array of instructional assessment applications are designed to run on handheld computers. The instructional assessment applications may be based on existing and widely used paper methodologies. An instructional assessment application includes a self-correct feature that marks an incorrect response as a self-corrected response based upon selection of the self-correct feature.