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Using Multivariate Statistics

Data Mining Practical Machine Learning Tools and Techniques

It is amazing to me how in all the hoopla and debate these days about the decline of education in the US we ignore the most fundamental of its causes. Our students have changed radically. Today’s students are no longer the people our educational system was designed to teach. Today’s students have not just changed incrementally from those of the past, nor simply changed their slang, clothes, body adornments, or styles, as has happened between generations previously. A really big discontinuity has taken place. One might even call it a “singularity” – an event which changes things so fundamentally that there is absolutely no going back. This so-called “singularity” is the arrival and rapid dissemination of digital technology in the last decades of the 20 th century.

/pdf/digital-natives-digital-immigrants-34lcyau33p.pdf

Digital Natives, Digital Immigrants

How to Do Things With Words

Recent studies of eye movements in reading and other information processing tasks, such as music reading, typing, visual search, and scene perception, are reviewed. The major emphasis of the review is on reading as a specific example of cognitive processing. Basic topics discussed with respect to reading are (a) the characteristics of eye movements, (b) the perceptual span, (c) integration of information across saccades, (d) eye movement control, and (e) individual differences (including dyslexia). Similar topics are discussed with respect to the other tasks examined. The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined. Theoretical and practical considerations concerning the use of eye movement data are also discussed.

/pdf/eye-movements-in-reading-and-information-processing-20-years-3z5ay8hdwc.pdf

Eye movements in reading and information processing: 20 years of research.

This chapter reviews our past and ongoing investigations into conversational interaction during human tutoring and our attempts to build intelligent tutoring systems (ITS) to simulate this interaction. We have previously modeled the strategies, actions, and dialogue of novice tutors in an ITS, called AutoTutor, with learning gains comparable to novice tutors. There is evidence, however, that expert human tutors may foster exceptional learning gains beyond those reported for some categories of human tutors. We have undertaken a rigorous, large scale study of expert human tutors and are using these data to create Guru, an expert ITS for high school biology. Based on our analyses, expert human tutoring has several distinctive features which differ from novice human tutoring. These distinctive features have implications for the development of an expert ITS, and we briefly describe how these are being addressed in Guru.

Tutorial Dialog in Natural Language

This paper describes the design of AutoTutor, a fully automated computer tutor that simulates dialogue moves of normal human tutors and that will eventually incorporate sophisticated tutoring strategies. Autolhtot follows a curriculum script that presents subtopics in different forms, such as didactic content, graphic displays, questions, and problems. The tutor selects dialogue moves that assist the learner in the active coustruction of knowledge as the learner ans~ers questions or solves problems posed by the tutor. The goal is to have the tutor produce dialogue moves that fit the conversational context, that are sensitive to the learner’s abilities, and that have pedagogical value. The di~dogue rnoves include immediate feedback, pumping, prompting, splicing, hinting, requestioning, and summarizing. The tutor’s moves are delivered by a talking head with appropriate facial expressions and synthe.4zed speech. The first version of Auto~ltor should be completed in the spring of 1998 on the topic of comput.er literacy.

https://aaaipress.org/Papers/FLAIRS/1998/FLAIRS98-031.pdf

Simulating Smooth Tutorial Dialogue with Pedagogical Value

The role of technology in education has mystified the contributors to Theory Into Practice (TIP) during its 50-year history. In the first issue of TIP, Guba (1962) was confident that “teaching machines are here to stay” and would help education, but raised various practical concerns, such as costs, programming resources, and acceptance by the education communities. Howell (1968) was confident that new technologies would change education, but not directly without educators understanding their potential and having a commitment to use them wisely. Caldwell (1980) emphasized the need to reconceptualize the use of computers in education by shifting the emphasis from mere information delivery and testing systems to facilities that assist students in meaningful active learning, inquiry, and thought.

Evolution of Advanced Learning Technologies in the 21st Century

Question-asking in advanced distributed learning environments.

We report results from two subject‐paced reading experiments in which word‐reading times were collected using the moving‐window method. Word type, the amount of information at successive locations within sentences, and task were the independent variables, and word‐reading time was the dependent variable. Reading times increased with successive locations, as indexed by the cumulative number of new arguments per sentence. There was an interaction involving word type, such that reading times of content words increased more steeply than reading times of function words. Among content words, the increase was steeper for nouns than for verbs; and, among nouns, the increase was steeper for new nouns than for repeated nouns. The results are discussed in terms of buffer models of reading, the processing of different lexical classes, and hypotheses which predict serial position effects.

Arthur C. Graesser

Papers

Tutorial Dialog in Natural Language

Simulating Smooth Tutorial Dialogue with Pedagogical Value

Evolution of Advanced Learning Technologies in the 21st Century

Question-asking in advanced distributed learning environments.

Buffering new information during reading