Pier Giuseppe Rossi's List: ai, its

Abstract. Analysis of student-tutor coaching dialogs suggest that good human
tutors attend to and attempt to influence the motivational state of learners.
Moreover, they are sensitive to the social face of the learner, and seek to mitigate
the potential face threat of their comments. This paper describes a dialog
generator for pedagogical agents that takes motivation and face threat factors
into account. This enables the agent to interact with learners in a socially appropriate
fashion, and foster intrinsic motivation on the part of the learner,
which in turn may lead to more positive learner affective states.

This study investigates the effect of Emotional Intelligence (EQ) training on student
satisfaction with the collaborative writing process and product. Business communication students
at an AACSB-accredited state university worked collaboratively on writing assignments in preand
post- EQ-training sessions. Pre-and post-training surveys measured student satisfaction with
the collaborative writing process. An independent evaluator measured the quality of the writing
product. Our findings suggest that student awareness of EQ strategies enhances their
communication behavior in work groups. Incorporating EQ training into the business
communication curriculum can provide students a competitive advantage academically,
personally, and professionally.

pag 137 - embodiment

progetto americano su ITS

comprare

Scott W. McQuiggan1, Bradford W. Mott2, and James C. Lester1, 2008
Self-efficacy is an individual's belief about her ability to perform well in a given situation. Because selfefficacious
students are effective learners, endowing intelligent tutoring systems with the ability to diagnose selfefficacy
could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus,
physiological data might be used to predict a student's level of self-efficacy. This article investigates an inductive
approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical
decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy
models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a
dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a
biofeedback apparatus. In the second empirical study, a similar experimental design was applied to an interactive
narrative-centered learning environment. Self-efficacy models were induced from combinations of static and
dynamic information including pre-test data, physiological data, and observations of student behavior in the learning
environment. The highest performing induced naïve Bayes models correctly classified 85.2% of instances in the
first empirical study and 82.1% of instances in the second empirical study. The highest performing decision tree
models correctly classified 86.9% of instances in the first study and 87.3% of instances in the second study.

leggere e costruire

pag 547
VFTS interface, metodi epr motivare gli studenti, affettivi,

ARTIFICIAL INTELLIGENCE IN E-LEARNING

LABORATORIO DI INTELLIGENZA ARTIFICIALE

AIML: Artificial Intelligence Markup Language

Chatbot List

Hubert Dreyfus has been a critic of artificial intelligence research since the 1960s. In a series of papers and books, including Alchemy and AI (1965), What Computers Can't Do (1972, 1979, 1991) and Mind over Machine (1986), he presented an assessment of AI's progress and a critique of the philosophical foundations of the field. Dreyfus' objections are discussed in most introductions to the philosophy of artificial intelligence, including Russell & Norvig (2003), the standard AI textbook, and in Fearn (2007), a survey of contemporary philosophy.[1]

We investigated the impact of a Web tutor on college students' critical stance and learning while exploring Web pages on science. Critical stance is an aspect of self-regulated learning that emphasizes the need to evaluate the truth and relevance of information as the learner engages in systematic inquiry to answer challenging questions. The Web tutor is called SEEK, an acronym for Source, Evidence, Explanation, and Knowledge. The SEEK Tutor was designed to promote a critical stance through several facilities in a computer environment: spoken hints on a mock Google™ search page, on-line ratings on the reliability of particular Web sites, and a structured note-taking facility that prompted them to reflect on the quality of particular Web sites. We conducted two experiments that trained students how to take a critical stance and that tracked their behavior while exploring Web pages on plate tectonics to research the causes of the volcanic eruption of Mt. St. Helens. The SEEK Tutor did improve critical stance, as manifested in essays on the causes of the volcanic eruption, and did yield learning gains for some categories of information (compared with comparison conditions). However, many measures were unaffected by either the presence of the SEEK Tutor or by prior training on critical stance. We anticipate that robust improvements on critical stance and learning will require more training and/or some expert feedback and interactive scaffolding of critical stance in the context of specific examples.

Arthur C. Graesser and Danielle S. McNamara