Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners

In real-world applications of education, an effective teacher adaptively chooses the next example to teach based on the learner's current state. However, most existing work in algorithmic machine teaching focuses on the batch setting, where adaptivity plays no role. In this paper, we study the case of teaching consistent, version space learners in an interactive setting. At any time step, the teacher provides an example, the learner performs an update, and the teacher observes the learner's new state. We highlight that adaptivity does not speed up the teaching process when considering existing models of version space learners, such as "worst-case" (the learner picks the next hypothesis randomly from the version space) and "preference-based" (the learner picks hypothesis according to some global preference). Inspired by human teaching, we propose a new model where the learner picks hypotheses according to some local preference defined by the current hypothesis. We show that our model exhibits several desirable properties, e.g., adaptivity plays a key role, and the learner's transitions over hypotheses are smooth/interpretable. We develop efficient teaching algorithms and demonstrate our results via simulation and user studies.Comment: NeurIPS 2018 (extended version

A Probabilistic Framework for Non-Cheating Machine Teaching

Over the past decades in the field of machine teaching, several restrictions have been introduced to avoid ‘cheating’, such as collusion-free or non-clashing teaching. However, these restrictions forbid several teaching situations that we intuitively consider natural and fair, especially those ‘changes of mind’ of the learner as more evidence is given, affecting the likelihood of concepts and ultimately their posteriors. Under a new generalised probabilistic teaching, not only do these non-cheating constraints look too narrow but we also show that the most relevant machine teaching models are particular cases of this framework: the consistency graph between concepts and elements simply becomes a joint probability distribution. We show a simple procedure that builds the witness joint distribution from the ground joint distribution. We prove a chain of relations, also with a theoretical lower bound, on the teaching dimension of the old and new models. Overall, this new setting is more general than the traditional machine teaching models, yet at the same time more intuitively capturing a less abrupt notion of non-cheating teaching.Ferri Ramírez, C.; Hernández Orallo, J.; Telle, JA. (2022). A Probabilistic Framework for Non-Cheating Machine Teaching. http://hdl.handle.net/10251/18236

Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners

In real-world applications of education, an effective teacher adaptively chooses the next example to teach based on the learner's current state. However, most existing work in algorithmic machine teaching focuses on the batch setting, where adaptivity plays no role. In this paper, we study the case of teaching consistent, version space learners in an interactive setting. At any time step, the teacher provides an example, the learner performs an update, and the teacher observes the learner's new state. We highlight that adaptivity does not speed up the teaching process when considering existing models of version space learners, such as "worst-case" (the learner picks the next hypothesis randomly from the version space) and "preference-based" (the learner picks hypothesis according to some global preference). Inspired by human teaching, we propose a new model where the learner picks hypotheses according to some local preference defined by the current hypothesis. We show that our model exhibits several desirable properties, e.g., adaptivity plays a key role, and the learner's transitions over hypotheses are smooth/interpretable. We develop efficient teaching algorithms and demonstrate our results via simulation and user studies

Teacher/Artist/Teacher

Art educators have expressed the need for art teachers with more qualifications and for more evidence of creativity and successful art experience. The purpose of this project is to illustrate the importance of the interrelationship between the action-oriented sensory capacities of the artist and the verbal-analytical capacities of the teacher, and in so doing to develop a vehicle through which an art teacher might demonstrate the necessary proficiency in at least one medium within the fine arts. More specifically, the objective of the project is the development of an art exhibit to validate the competency of the artist-teacher in the medium of oil painting. The philosophy of every art teacher should be based on art as experience, since valid creative work evolves from sensitive experience. An art teacher must be able to communicate with a student verbally; worlds are one relationship to the creative experience, the art forms another. Much precise thought can go on in words, but ultimately it can only be meaningful for the teacher and student, in turn, if both have experienced art. The teacher should be a creative artist in his own right. He should know from “doing” the experience he is to teach. For the purpose of the study, the literature was divided into five categories: 1) philosophical and psychological, 2) definitive information, 3) historical, 4) present attitudes, and 5) implications for the future. The evidence in the literature indicated that art educators strongly favor the idea that the teacher should be a creative artist in his own right, that he should be skilled in at least one major productive area of art. The vehicle developed in this project was an art exhibit containing fifteen oil paintings. The University of North Florida Library Exhibit Area was chosen as the site of the display which was scheduled from July 18-29, 1977. The show was accompanied by a reception; a printed brochure and invitation described the project, the background of the artist, and contained a list of the paintings. The project contains a complete photographic record of all works in the exhibit. An evaluation of the work was undertaken by a group of five qualified judges in the field of fine arts and art education. At the close of the exhibit the responses to the evaluation were tabulated and a correlation was made on the ratings of five paintings chosen at random from the show

HOW THE CLIMATE OF OPINION IN STATES AND COUNTRIES INFLUENCES GAY RIGHTS

This dissertation examines attitudes on same-sex marriage and how personal predispositions toward support and the climate of opinion interact to help create attitudes. Over the past few decades, support for gay rights has increased dramatically in the United States and many other countries around the world. I argue that, while the set of basic personal determinants of attitudes toward homosexuality and gay rights stays roughly the same, the impact of such determinants changes over time and space. The framework used in this dissertation draws on attitudinal and political psychology, political sociology and theories of contextual effects. I argue that over the course of their lives, people develop an overall propensity to tolerate or approve of homosexuality and support gay rights based on their political and social characteristics, such as religiosity, partisan or ideological identities, personality characteristics and various demographic characteristics (e.g., education, race, gender and age). The influence of these predispositions on gay rights attitudes is moderated by the political and social environments in which people live. Even among people whose predispositions push them consistently toward support for gay rights, if they live in a homophobic environment, support for gay rights will be attenuated compared to a similar individual living in a more socially tolerant environment