1,335 research outputs found
Teaching Multiple Concepts to Forgetful Learners
How can we help a forgetful learner learn multiple concepts within a limited time frame? While there have been extensive studies in designing optimal schedules for teaching a single concept given a learner's memory model, existing approaches for teaching multiple concepts are typically based on heuristic scheduling techniques without theoretical guarantees. In this paper, we look at the problem from the perspective of discrete optimization and introduce a novel algorithmic framework for teaching multiple concepts with strong performance guarantees. Our framework is both generic, allowing the design of teaching schedules for different memory models, and also interactive, allowing the teacher to adapt the schedule to the underlying forgetting mechanisms of the learner. Furthermore, for a well-known memory model, we are able to identify a regime of model parameters where our framework is guaranteed to achieve high performance. We perform extensive evaluations using simulations along with real user studies in two concrete applications: (i) an educational app for online vocabulary teaching; and (ii) an app for teaching novices how to recognize animal species from images. Our results demonstrate the effectiveness of our algorithm compared to popular heuristic approaches
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Learning near-optimal behaviour from an expert's demonstrations typically
relies on the assumption that the learner knows the features that the true
reward function depends on. In this paper, we study the problem of learning
from demonstrations in the setting where this is not the case, i.e., where
there is a mismatch between the worldviews of the learner and the expert. We
introduce a natural quantity, the teaching risk, which measures the potential
suboptimality of policies that look optimal to the learner in this setting. We
show that bounds on the teaching risk guarantee that the learner is able to
find a near-optimal policy using standard algorithms based on inverse
reinforcement learning. Based on these findings, we suggest a teaching scheme
in which the expert can decrease the teaching risk by updating the learner's
worldview, and thus ultimately enable her to find a near-optimal policy.Comment: NeurIPS'2018 (extended version
Interactive Teaching Algorithms for Inverse Reinforcement Learning
We study the problem of inverse reinforcement learning (IRL) with the added
twist that the learner is assisted by a helpful teacher. More formally, we
tackle the following algorithmic question: How could a teacher provide an
informative sequence of demonstrations to an IRL learner to speed up the
learning process? We present an interactive teaching framework where a teacher
adaptively chooses the next demonstration based on learner's current policy. In
particular, we design teaching algorithms for two concrete settings: an
omniscient setting where a teacher has full knowledge about the learner's
dynamics and a blackbox setting where the teacher has minimal knowledge. Then,
we study a sequential variant of the popular MCE-IRL learner and prove
convergence guarantees of our teaching algorithm in the omniscient setting.
Extensive experiments with a car driving simulator environment show that the
learning progress can be speeded up drastically as compared to an uninformative
teacher.Comment: IJCAI'19 paper (extended version
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
The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning
Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy
A CONTENT ANALYSIS ON ENGLISH TEXTBOOK OF “BAHASA DAN SASTRA INGGRIS X” USED BY STUDENTS OF MAN 1 SURAKARTA
ABSTRACT
Febrian Giska Bangkit. 2017.A Content Analysis On English Textbook ”Bahasa dan
Sastra Inggris X” Used by Students Of MAN 1 Surakarta. Thesis. English Education
Department, Islamic Education and Teacher Training Faculty.
Advisor : Dr. Imroatus Solikhah, M.Pd
Key words : Content Analysis, Bahasa dan Sastra X English Textbook.
The research is a content analysis on English textbook of “Bahasa dan Sastra
Inggris X” used by students of MAN 1 Surakarta. The objectives of this research
were to find out the quality of material English textbook for the tenth grade students
of MAN 1 Surakarta. The researcher focuses on the quality of textbook suggested by
checklist from Permendikbud (2016:8).
The method used in this research was descriptive qualitative research. The
subject of the research was the English textbook used by students of MAN 1
Surakarta. The instrument to collect the data is English textbook entitled “Bahasa
dan Sastra Inggris X” for Senior High School published by Mediatama. The
researcher analyzed the data by using descriptive qualitative research. The
thrustwortiness of the reseach was methodological triangulation.
From the result of the research, the researcher found the answer of the
research problem that the textbook of "Bahasa dan Sastra Inggris X" that only two
things which are not on the checklist from Permendikbud no 8. Tahun 2016. They
are the higher retail price of the textbook and the image page on “Bahasa dan
Sastra Inggris” textbook. So, “Bahasa dan Sastra Inggris” textbook are
categorized in good textbook and suitable for X grade students according to
checklist from Permendikbud No. 8 Tahun 2016
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Curriculum development for disadvantaged students enrolled in nursing courses in career and technical education programs
The purpose of this study was to identify the characteristics of special needs students in technical educational programs
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
Successful teaching requires an assumption of how the learner learns - how
the learner uses experiences from the world to update their internal states. We
investigate what expectations people have about a learner when they teach them
in an online manner using rewards and punishment. We focus on a common
reinforcement learning method, Q-learning, and examine what assumptions people
have using a behavioral experiment. To do so, we first establish a normative
standard, by formulating the problem as a machine teaching optimization
problem. To solve the machine teaching optimization problem, we use a deep
learning approximation method which simulates learners in the environment and
learns to predict how feedback affects the learner's internal states. What do
people assume about a learner's learning and discount rates when they teach
them an idealized exploration-exploitation task? In a behavioral experiment, we
find that people can teach the task to Q-learners in a relatively efficient and
effective manner when the learner uses a small value for its discounting rate
and a large value for its learning rate. However, they still are suboptimal. We
also find that providing people with real-time updates of how possible feedback
would affect the Q-learner's internal states weakly helps them teach. Our
results reveal how people teach using evaluative feedback and provide guidance
for how engineers should design machine agents in a manner that is intuitive
for people.Comment: 21 pages, 4 figure
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