Redundancy and Readability

Redundancy is, in itself something of an unknown quantity, and a number of researchers have been experimenting on it (Horning, 1979). Several years ago, H. J. Hsia, a Texas Tech communications theorist, clarified several different types of redundancy, providing important insights into the ways in which these different types of redundancy contribute to readability (Hsia, 1977). Hsia\u27s research helps to isolate some of the unknown elements in readability, and these elements look as if they have the potential to bond neatly to the textual analysis system proposed in great detail by Walter Kintsch of the University of Colorado (Kintsch, 1974). Propositional analysis provides a system for objectively analyzing meaning in a text, and may yield a measure of the properties of redundancy described by Hsia. If so, the result will be a much purer analysis of the nature of readability

The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes

The measurement of student performance during their progress through university study provides academic leadership with critical information on each student’s likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those “at risk” of failure/withdrawal. However, modern university environments, offering easy on-line availability of course material, may see reduced lecture/tutorial attendance, making such identification more challenging. Modern data mining and machine learning techniques provide increasingly accurate predictions of student examination assessment marks, although these approaches have focussed upon large student populations and wide ranges of data attributes per student. However, many university modules comprise relatively small student cohorts, with institutional protocols limiting the student attributes available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. We describe an experiment conducted on a final-year university module student cohort of 23, where individual student data are limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. We found potential for predicting individual student interim and final assessment marks in small student cohorts with very limited attributes and that these predictions could be useful to support module leaders in identifying students potentially “at risk.”.Peer reviewe