60,876 research outputs found
Using a self-reflective journal to enhance science communication
In new times the ability to self-evaluate and reflect on one's own actions in communicating with others will be a crucial workplace skill. An innovative peer tutoring course for academic credit, by university science students in high schools, will be presented, with a review on its ability to develop a link between school tutoring and workplace communication. Course content relates to broad issues of science literacy, science communication and group situations and peer interactions. Students complete on-campus lecture and workshop component, and do 20-30 hours of in-school tutoring; assessment includes an examination, assignments in the form of journals, and a personal learning log of experiences.
Findings from the first two years of the course, based on data sources of students' journal entries and responses to the end of unit evaluations (1996, n = 21; 1997, n = 21) are presented. Analysis focuses on the development of reflective skills and students' awareness of their personal power in detecting and solving problems and developing strategies to promote two way communication. The use of self-evaluation through reflective journals was found to enhance the effectiveness of tutoring. Implications for developing the 'human side' of science will be discussed, and the appropriateness of the course to develop these often under-represented aspects of science
Training an adaptive dialogue policy for interactive learning of visually grounded word meanings
We present a multi-modal dialogue system for interactive learning of
perceptually grounded word meanings from a human tutor. The system integrates
an incremental, semantic parsing/generation framework - Dynamic Syntax and Type
Theory with Records (DS-TTR) - with a set of visual classifiers that are
learned throughout the interaction and which ground the meaning representations
that it produces. We use this system in interaction with a simulated human
tutor to study the effects of different dialogue policies and capabilities on
the accuracy of learned meanings, learning rates, and efforts/costs to the
tutor. We show that the overall performance of the learning agent is affected
by (1) who takes initiative in the dialogues; (2) the ability to express/use
their confidence level about visual attributes; and (3) the ability to process
elliptical and incrementally constructed dialogue turns. Ultimately, we train
an adaptive dialogue policy which optimises the trade-off between classifier
accuracy and tutoring costs.Comment: 11 pages, SIGDIAL 2016 Conferenc
Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System
This thesis presents research that combines the benefits of intelligent tutoring
systems (ITS), conversational agents (CA) and learning styles theory by constructing
a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS
aims to imitate a human tutor by implicitly predicting individuals’ learning style
preferences and adapting its tutoring style to suit them during a tutoring
conversation.
ITS are computerised learning systems that intelligently personalise tutoring
based on learner characteristics such as existing knowledge and learning style. ITS
are traditionally student-led, hyperlink-based learning systems that adapt the
presentation of learning resources by reordering or hiding links. Research suggests
that students learn more effectively when instruction matches their learning style,
which is typically modelled explicitly using questionnaires or implicitly based on
behaviour. Learning is a social process and natural language interfaces to ITS, such
as CAs, allow students to construct knowledge through discussion. Existing CITS
adapt tutoring according to student knowledge, emotions and mood, however no
CITS adapts to learning styles.
Oscar CITS models a human tutor by directing a tutoring conversation and
automatically detecting and adapting to an individual’s learning styles. Original
methodologies and architectures were developed for constructing an Oscar Predictive
CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured
from a learning styles model to dynamically predict learning styles from an
individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation
algorithm to select the best tutoring style for each tutorial question. The Oscar CITS
methodologies and architectures are independent of the learning styles model and
subject domain. Empirical studies involving real students have validated the
prediction and adaptation of learning styles in a real-world teaching/learning
environment. The results show that learning styles can be successfully predicted
from a natural language tutoring dialogue, and that adapting the tutoring style
significantly improves learning performance
Understanding and Supporting Vocabulary Learners via Machine Learning on Behavioral and Linguistic Data
This dissertation presents various machine learning applications for predicting different cognitive states of students while they are using a vocabulary tutoring system, DSCoVAR. We conduct four studies, each of which includes a comprehensive analysis of behavioral and linguistic data and provides data-driven evidence for designing personalized features for the system.
The first study presents how behavioral and linguistic interactions from the vocabulary tutoring system can be used to predict students' off-task states. The study identifies which predictive features from interaction signals are more important and examines different types of off-task behaviors. The second study investigates how to automatically evaluate students' partial word knowledge from open-ended responses to definition questions. We present a technique that augments modern word-embedding techniques with a classic semantic differential scaling method from cognitive psychology. We then use this interpretable semantic scale method for predicting students' short- and long-term learning.
The third and fourth studies show how to develop a model that can generate more efficient training curricula for both human and machine vocabulary learners. The third study illustrates a deep-learning model to score sentences for a contextual vocabulary learning curriculum. We use pre-trained language models, such as ELMo or BERT, and an additional attention layer to capture how the context words are less or more important with respect to the meaning of the target word. The fourth study examines how the contextual informativeness model, originally designed to develop curricula for human vocabulary learning, can also be used for developing curricula for various word embedding models. We identify sentences predicted as low informative for human learners are also less helpful for machine learning algorithms.
Having a rich understanding of user behaviors, responses, and learning stimuli is imperative to develop an intelligent online system. Our studies demonstrate interpretable methods with cross-disciplinary approaches to understand various cognitive states of students during learning. The analysis results provide data-driven evidence for designing personalized features that can maximize learning outcomes. Datasets we collected from the studies will be shared publicly to promote future studies related to online tutoring systems. And these findings can also be applied to represent different user states observed in other online systems. In the future, we believe our findings can help to implement a more personalized vocabulary learning system, to develop a system that uses non-English texts or different types of inputs, and to investigate how the machine learning outputs interact with students.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162999/1/sjnam_1.pd
Intelligent and adaptive tutoring for active learning and training environments
Active learning facilitated through interactive and adaptive learning environments differs substantially from traditional instructor-oriented, classroom-based teaching. We present a Web-based e-learning environment that integrates knowledge learning and skills training. How these tools are used most effectively is still an open question. We propose knowledge-level interaction and adaptive feedback and guidance as central features. We discuss these features and evaluate the effectiveness of this Web-based environment, focusing on different aspects of learning behaviour and tool usage. Motivation, acceptance of the approach, learning organisation and actual tool usage are aspects of behaviour that require different evaluation techniques to be used
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