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2nd Workshop on Personalization Approaches for Learning Environments (PALE 2012)
"... Abstract. PALE workshop aims to offer an opportunity where interrelated issues regarding personalization approaches in learning environments can be contrasted, such as pedagogic conversational agents, responsive open learning environments, and user modeling for all. Nine submissions have been accept ..."
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Abstract. PALE workshop aims to offer an opportunity where interrelated issues regarding personalization approaches in learning environments can be contrasted, such as pedagogic conversational agents, responsive open learning environments, and user modeling for all. Nine submissions have been
3rd Workshop on Personalization Approaches for Learning Environments (PALE 2013) Preface
"... Abstract. Personalization approaches in learning environments can be addressed from different perspectives and also in various educational settings, including formal, informal, workplace, lifelong, mobile, contextualized, and selfregulated learning. PALE workshop offers an opportunity to present and ..."
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Abstract. Personalization approaches in learning environments can be addressed from different perspectives and also in various educational settings, including formal, informal, workplace, lifelong, mobile, contextualized, and selfregulated learning. PALE workshop offers an opportunity to present
4th International Workshop on Personalization Approaches in Learning Environments (PALE 2014) Preface
"... Abstract. Personalization approaches in learning environments can be ad-dressed from different perspectives and also in various educational settings, in-cluding formal, informal, workplace, lifelong, mobile, contextualized, and self-regulated learning. PALE workshop offers an opportunity to present ..."
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Abstract. Personalization approaches in learning environments can be ad-dressed from different perspectives and also in various educational settings, in-cluding formal, informal, workplace, lifelong, mobile, contextualized, and self-regulated learning. PALE workshop offers an opportunity to present
Predicting Student Outcomes from Unstructured Data. In proceedings of the 2nd Workshop on Personalization Approaches for Learning Environments
- 20th conference on User Modeling, Adaptation, and Personalization (UMAP 2012
, 2012
"... Abstract. We investigated the validity of applying topic modeling to unstructured student text data from online class discussion forums to predict students’ final grades. Using only student discussion data from introductory courses in biology and economics, both probabilistic latent semantic analysi ..."
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Cited by 1 (0 self)
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conceptual hierarchies relevant to actual student data. Results indicate that topic modeling of studentgenerated text may provide a useful source of formative assessment to support learning and instruction.
Designing Learning
- In
, 2004
"... …Truth [is] being involved in an eternal conversation about things that matter, conducted with passion and discipline…truth is not in the conclusions so much as in the process of conversation itself…if you want to be in truth you must be in conversation. Parker Palmer ..."
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Cited by 555 (9 self)
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…Truth [is] being involved in an eternal conversation about things that matter, conducted with passion and discipline…truth is not in the conclusions so much as in the process of conversation itself…if you want to be in truth you must be in conversation. Parker Palmer
NewsWeeder: Learning to Filter Netnews
- in Proceedings of the 12th International Machine Learning Conference (ML95
, 1995
"... A significant problem in many information filtering systems is the dependence on the user for the creation and maintenance of a user profile, which describes the user's interests. NewsWeeder is a netnews-filtering system that addresses this problem by letting the user rate his or her interest l ..."
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Cited by 555 (0 self)
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level for each article being read (1-5), and then learning a user profile based on these ratings. This paper describes how NewsWeeder accomplishes this task, and examines the alternative learning methods used. The results show that a learning algorithm based on the Minimum Description Length (MDL
Semi-Supervised Learning Literature Survey
, 2006
"... We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a chapter ..."
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Cited by 757 (8 self)
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We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a
A learning algorithm for Boltzmann machines
- Cognitive Science
, 1985
"... The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a probl ..."
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Cited by 586 (13 self)
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to a gen-eral learning rule for modifying the connection strengths so as to incorporate knowledge obout o task domain in on efficient way. We describe some simple examples in which the learning algorithm creates internal representations thot ore demonstrobly the most efficient way of using
Machine Learning in Automated Text Categorization
- ACM COMPUTING SURVEYS
, 2002
"... The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this p ..."
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Cited by 1658 (22 self)
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to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual
Reinforcement Learning I: Introduction
, 1998
"... In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e.g., supervised learning and neural networks, genetic algorithms and artificial life, control theory. Intuitively, RL is trial and error (variation and selection, search ..."
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Cited by 5500 (120 self)
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In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e.g., supervised learning and neural networks, genetic algorithms and artificial life, control theory. Intuitively, RL is trial and error (variation and selection
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