8,107 research outputs found
A multi-granularity pattern-based sequence classification framework for educational data
In many application domains, such as education, sequences of events occurring over time need to be studied in order to understand the generative process behind these sequences, and hence classify new examples. In this paper, we propose a novel multi-granularity sequence lassification framework that generates features based on frequent patterns at multiple levels of time granularity. Feature selection techniques are applied to identify the most informative features that are then used to construct the classification model. We show the applicability and suitability of the proposed framework to the area of educational data mining by experimenting on an educational dataset collected from an asynchronous communication
tool in which students interact to accomplish an underlying
group project. The experimental results showed that our model can achieve competitive performance in detecting the students' roles in their corresponding projects, compared to a baseline similarity-based approach
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Models for Learning (Mod4L) Final Report: Representing Learning Designs
The Mod4L Models of Practice project is part of the JISC-funded Design for Learning Programme. It ran from 1 May – 31 December 2006. The philosophy underlying the project was that a general split is evident in the e-learning community between development of e-learning tools, services and standards, and research into how teachers can use these most effectively, and is impeding uptake of new tools and methods by teachers. To help overcome this barrier and bridge the gap, a need is felt for practitioner-focused resources which describe a range of learning designs and offer guidance on how these may be chosen and applied, how they can support effective practice in design for learning, and how they can support the development of effective tools, standards and systems with a learning design capability (see, for example, Griffiths and Blat 2005, JISC 2006). Practice models, it was suggested, were such a resource.
The aim of the project was to: develop a range of practice models that could be used by practitioners in real life contexts and have a high impact on improving teaching and learning practice.
We worked with two definitions of practice models. Practice models are:
1. generic approaches to the structuring and orchestration of learning activities. They express elements of pedagogic principle and allow practitioners to make informed choices (JISC 2006)
However, however effective a learning design may be, it can only be shared with others through a representation. The issue of representation of learning designs is, then, central to the concept of sharing and reuse at the heart of JISC’s Design for Learning programme. Thus practice models should be both representations of effective practice, and effective representations of practice. Hence we arrived at the project working definition of practice models as:
2. Common, but decontextualised, learning designs that are represented in a way that is usable by practitioners (teachers, managers, etc).(Mod4L working definition, Falconer & Littlejohn 2006).
A learning design is defined as the outcome of the process of designing, planning and orchestrating learning activities as part of a learning session or programme (JISC 2006).
Practice models have many potential uses: they describe a range of learning designs that are found to be effective, and offer guidance on their use; they support sharing, reuse and adaptation of learning designs by teachers, and also the development of tools, standards and systems for planning, editing and running the designs.
The project took a practitioner-centred approach, working in close collaboration with a focus group of 12 teachers recruited across a range of disciplines and from both FE and HE. Focus group members are listed in Appendix 1. Information was gathered from the focus group through two face to face workshops, and through their contributions to discussions on the project wiki. This was supplemented by an activity at a JISC pedagogy experts meeting in October 2006, and a part workshop at ALT-C in September 2006. The project interim report of August 2006 contained the outcomes of the first workshop (Falconer and Littlejohn, 2006).
The current report refines the discussion of issues of representing learning designs for sharing and reuse evidenced in the interim report and highlights problems with the concept of practice models (section 2), characterises the requirements teachers have of effective representations (section 3), evaluates a number of types of representation against these requirements (section 4), explores the more technically focused role of sequencing representations and controlled vocabularies (sections 5 & 6), documents some generic learning designs (section 8.2) and suggests ways forward for bridging the gap between teachers and developers (section 2.6).
All quotations are taken from the Mod4L wiki unless otherwise stated
Temporal Data Modeling and Reasoning for Information Systems
Temporal knowledge representation and reasoning is a major research field in Artificial
Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to
model and process time and calendar data is essential for many applications like appointment
scheduling, planning, Web services, temporal and active database systems, adaptive
Web applications, and mobile computing applications. This article aims at three complementary
goals. First, to provide with a general background in temporal data modeling
and reasoning approaches. Second, to serve as an orientation guide for further specific
reading. Third, to point to new application fields and research perspectives on temporal
knowledge representation and reasoning in the Web and Semantic Web
Robust Modeling of Epistemic Mental States
This work identifies and advances some research challenges in the analysis of
facial features and their temporal dynamics with epistemic mental states in
dyadic conversations. Epistemic states are: Agreement, Concentration,
Thoughtful, Certain, and Interest. In this paper, we perform a number of
statistical analyses and simulations to identify the relationship between
facial features and epistemic states. Non-linear relations are found to be more
prevalent, while temporal features derived from original facial features have
demonstrated a strong correlation with intensity changes. Then, we propose a
novel prediction framework that takes facial features and their nonlinear
relation scores as input and predict different epistemic states in videos. The
prediction of epistemic states is boosted when the classification of emotion
changing regions such as rising, falling, or steady-state are incorporated with
the temporal features. The proposed predictive models can predict the epistemic
states with significantly improved accuracy: correlation coefficient (CoERR)
for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for
Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special
Issue: Socio-Affective Technologie
Detecting hierarchical relationships and roles from online interaction networks
In social networks, analysing the explicit interactions among users can help in
inferring hierarchical relationships and roles that may be implicit. In this thesis,
we focus on two objectives: detecting hierarchical relationships between users and
inferring the hierarchical roles of users interacting via the same online communication
medium. In both cases, we show that considering the temporal dimension of
interaction substantially improves the detection of relationships and roles.
The first focus of this thesis is on the problem of inferring implicit relationships
from interactions between users. Based on promising results obtained by standard
link-analysis methods such as PageRank and Rooted-PageRank (RPR), we introduce
three novel time-based approaches, \Time-F" based on a defined time function,
Filter and Refine (FiRe) which is a hybrid approach based on RPR and Time-F,
and Time-sensitive Rooted-PageRank (T-RPR) which applies RPR in a way that
takes into account the time-dimension of interactions in the process of detecting
hierarchical ties.
We experiment on two datasets, the Enron email dataset to infer managersubordinate
relationships from email exchanges, and a scientific publication coauthorship
dataset to detect PhD advisor-advisee relationships from paper co-authorships.
Our experiments demonstrate that time-based methods perform better in terms of
recall. In particular T-RPR turns out to be superior over most recent competitor
methods as well as all other approaches we propose.
The second focus of this thesis is examining the online communication behaviour
of users working on the same activity in order to identify the different hierarchical
roles played by the users. We propose two approaches. In the first approach, supervised
learning is used to train different classification algorithms. In the second
approach, we address the problem as a sequence classification problem. A novel
sequence classification framework is defined that generates time-dependent features based on frequent patterns at multiple levels of time granularity. Our framework is
a
exible technique for sequence classification to be applied in different domains.
We experiment on an educational dataset collected from an asynchronous communication
tool used by students to accomplish an underlying group project. Our
experimental findings show that the first supervised approach achieves the best mapping
of students to their roles when the individual attributes of the students, information
about the reply relationships among them as well as quantitative time-based
features are considered. Similarly, our multi-granularity pattern-based framework
shows competitive performance in detecting the students' roles. Both approaches
are significantly better than the baselines considered
Interpretation of partial discharge activity in the presence of harmonics
Recent work has identified that circumstances of equipment operation can radically change condition monitoring data. This contribution investigates the significance of considering circumstance monitoring on the diagnostic interpretation of such condition monitoring data. Electrical treeing partial discharge data have been subjected to a data mining investigation, providing a platform for classification of harmonic influenced partial discharge patterns. The Total Harmonic Distortion (THD) index was varied to a maximum of 40%. The results show progressive development for interpretation of condition monitoring data, improving the asset manager's holistic view of an asset's health
A methodology for the capture and analysis of hybrid data: a case study of program debugging
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Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering
Multi-modal keyphrase generation aims to produce a set of keyphrases that
represent the core points of the input text-image pair. In this regard,
dominant methods mainly focus on multi-modal fusion for keyphrase generation.
Nevertheless, there are still two main drawbacks: 1) only a limited number of
sources, such as image captions, can be utilized to provide auxiliary
information. However, they may not be sufficient for the subsequent keyphrase
generation. 2) the input text and image are often not perfectly matched, and
thus the image may introduce noise into the model. To address these
limitations, in this paper, we propose a novel multi-modal keyphrase generation
model, which not only enriches the model input with external knowledge, but
also effectively filters image noise. First, we introduce external visual
entities of the image as the supplementary input to the model, which benefits
the cross-modal semantic alignment for keyphrase generation. Second, we
simultaneously calculate an image-text matching score and image region-text
correlation scores to perform multi-granularity image noise filtering.
Particularly, we introduce the correlation scores between image regions and
ground-truth keyphrases to refine the calculation of the previously-mentioned
correlation scores. To demonstrate the effectiveness of our model, we conduct
several groups of experiments on the benchmark dataset.
Experimental results and in-depth analyses show that our model achieves the
state-of-the-art performance. Our code is available on
https://github.com/DeepLearnXMU/MM-MKP.Comment: Accepted In Proceedings of the 31st ACM International Conference on
Multimedia (MM' 23
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