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• W2181830416 abstract "In this paper, we promote a model for peer-based intelligent tutoring that leverages the past learning experiences of students with a repository of learning objects. Consistent with McCalla's ecological approach, we determine appropriate peers and appropriate learning objects to direct a new student's learning. In particular, we focus on allowing peers to provide annotations of learning objects. We revisit a procedure developed to select which annotations to present to students in order to improve their learning: one that combines a modeling of the reputation of the annotation (based on its approval or disapproval by previous students), the reputability of the annotator (based on the reputation of all annotations left by the student) and the similarity of the raters with the new student. Our focus is on developing effective validation of the procedure's benefit, using an approach of simulated student learning. This is achieved by developing algorithms in greater detail and then making particular design decisions for the simulation in order to manage the reputability of the annotators and annotations in a way that enables the best learning objects to be employed for the tutoring. We are able to demonstrate the value of our proposed approach using distinct measures of rater similarity. We conclude with a comparison to related work and a view to future directions for the research. As a result, we present an approach for interpreting data from interactions with previous students in order to influence how to interact with current and future students, to enable effective learning. In the work of (2), a peer-based intelligent tutoring model was introduced that determined which learning objects from a given repository to present to a new student, based on which objects had provided the most advantageous benefits to learning to similar students. This Collaborative Learning Algorithm (CLA) leveraged pre- and post-tests of student learning in order to model the benefits derived from a particular learning object, and used a modeling of student knowledge in terms of letter grades, where students were similar if their grade level of achievement was sufficiently close on that letter grade scale. That work also introduced a method of validation based on simulating student learning, attaching target levels of knowledge to each learning object in the repository. In this paper, we explore the use of a richer repository of learning objects, namely ones where previous students have elected to attach annotations. An annotation could be for example a text message including an observation, a question, references to related work, etc. Peer-based learning is then facilitated by exposing each student to some of the available annotations and allowing the student to provide a rating (thumbs up or thumbs down) to record whether the annotation provided learning benefit. The annotations and the ratings would then inform each subsequent student, as part of the learning experience." @default.
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• W2181830416 date "2011-01-01" @default.
• W2181830416 modified "2023-09-27" @default.
• W2181830416 title "The Validation of an Annotations Approach to Peer Tutoring Through Simulation Incorporating the Modeling of Reputation" @default.
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