48 research outputs found
Author Age Prediction from Text using Linear Regression
While the study of the connection between discourse patterns and personal identification is decades old, the study of these patterns using language technologies is relatively recent. In that more recent tradition we frame author age prediction from text as a regression problem. We explore the same task using three very different genres of data simultaneously: blogs, telephone conversations, and online forum posts. We employ a technique from domain adaptation that allows us to train a joint model involving all three corpora together as well as separately and analyze differences in predictive features across joint and corpusspecific aspects of the model. Effective features include both stylistic ones (such as POS patterns) as well as content oriented ones. Using a linear regression model based on shallow text features, we obtain correlations up to 0.74 and mean absolute errors between 4.1 and 6.8 years.
Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network
Knowledge Graph (KG) completion research usually focuses on densely connected
benchmark datasets that are not representative of real KGs. We curate two KG
datasets that include biomedical and encyclopedic knowledge and use an existing
commonsense KG dataset to explore KG completion in the more realistic setting
where dense connectivity is not guaranteed. We develop a deep convolutional
network that utilizes textual entity representations and demonstrate that our
model outperforms recent KG completion methods in this challenging setting. We
find that our model's performance improvements stem primarily from its
robustness to sparsity. We then distill the knowledge from the convolutional
network into a student network that re-ranks promising candidate entities. This
re-ranking stage leads to further improvements in performance and demonstrates
the effectiveness of entity re-ranking for KG completion.Comment: The Joint Conference of the 59th Annual Meeting of the Association
for Computational Linguistics and the 11th International Joint Conference on
Natural Language Processing (ACL-IJCNLP 2021
A comparative evaluation of socratic versus didactic tutoring
While the effectiveness of one-on-one human tutoring has been well established, a great deal of controversy surrounds the issue of which features of tutorial dialogue separate effective uses of dialogue in tutoring from those that are less effective. In this paper we present a formal comparison of Socratic versus Didactic style tutoring that argues in favor of the Socratic tutoring style
Towards Value-Sensitive Learning Analytics Design
To support ethical considerations and system integrity in learning analytics,
this paper introduces two cases of applying the Value Sensitive Design
methodology to learning analytics design. The first study applied two methods
of Value Sensitive Design, namely stakeholder analysis and value analysis, to a
conceptual investigation of an existing learning analytics tool. This
investigation uncovered a number of values and value tensions, leading to
design trade-offs to be considered in future tool refinements. The second study
holistically applied Value Sensitive Design to the design of a recommendation
system for the Wikipedia WikiProjects. To proactively consider values among
stakeholders, we derived a multi-stage design process that included literature
analysis, empirical investigations, prototype development, community
engagement, iterative testing and refinement, and continuous evaluation. By
reporting on these two cases, this paper responds to a need of practical means
to support ethical considerations and human values in learning analytics
systems. These two cases demonstrate that Value Sensitive Design could be a
viable approach for balancing a wide range of human values, which tend to
encompass and surpass ethical issues, in learning analytics design.Comment: The 9th International Learning Analytics & Knowledge Conference
(LAK19
Towards an Intelligent Tutor for Mathematical Proofs
Computer-supported learning is an increasingly important form of study since
it allows for independent learning and individualized instruction. In this
paper, we discuss a novel approach to developing an intelligent tutoring system
for teaching textbook-style mathematical proofs. We characterize the
particularities of the domain and discuss common ITS design models. Our
approach is motivated by phenomena found in a corpus of tutorial dialogs that
were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor
for textbook-style mathematical proofs can be built on top of an adapted
assertion-level proof assistant by reusing representations and proof search
strategies originally developed for automated and interactive theorem proving.
The resulting prototype was successfully evaluated on a corpus of tutorial
dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453