Content Analysis to Detect the Role Behaviors of Student in Online Discussion

Online discussion is a powerful way to conduct online conversation and a significant component of online learning. Online discussion can provide a platform for online learners to communicate with one another easily, without the constraint of place and time. In an online discussion, the students communicate a common interest, exchange information, share ideas, and assist each other in text/transcript form. So far, content analysis is a popular method for analyzing transcripts. However, using content analysis in computer supported collaborative learning (CSCL) or computer mediated communication (CMC) research focused on the surface of the transcripts. Usually, content analysis is employed to categorize news article, product reviews and web pages. Therefore, this study proposed content analysis to a deeper level is to detect the role behavior of students in an online discussion based on a conversation in text form. The findings showed that this method provides more meaningful students\u27 interaction analysis in term of information on communication transcripts in online discussion. Educators can assess the contribution of students and can detect the role behavior of the student based on their conversation in transcript form; whether the role behavior as a mediator, motivator, informer, facilitator, or as a questioner

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Content Analysis to Detect the Role Behaviors of Student in Online Discussion

Online discussion is a powerful way to conduct online conversation and a significant component of online learning. Online discussion can provide a platform for online learners to communicate with one another easily, without the constraint of place and time. In an online discussion, the students communicate a common interest, exchange information, share ideas, and assist each other in text/transcript form. So far, content analysis is a popular method for analyzing transcripts. However, using content analysis in computer supported collaborative learning (CSCL) or computer mediated communication (CMC) research focused on the surface of the transcripts. Usually, content analysis is employed to categorize news article, product reviews and web pages. Therefore, this study proposed content analysis to a deeper level is to detect the role behavior of students in an online discussion based on a conversation in text form. The findings showed that this method provides more meaningful students’ interaction analysis in term of information on communication transcripts in online discussion. Educators can assess the contribution of students and can detect the role behavior of the student based on their conversation in transcript form; whether the role behavior as a mediator, motivator, informer, facilitator, or as a questioner

An ontology enhanced parallel SVM for scalable spam filter training

This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids

We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the object to be predictable from learned features. We implement this idea as an encoder-decoder convolutional neural network. The network maps an input image of an unknown category and unknown viewpoint to a latent space, from which a deconvolutional decoder can best "lift" the image to its complete viewgrid showing the object from all viewing angles. Our class-agnostic training procedure encourages the representation to capture fundamental shape primitives and semantic regularities in a data-driven manner---without manual semantic labels. Our results on two widely-used shape datasets show 1) our approach successfully learns to perform "mental rotation" even for objects unseen during training, and 2) the learned latent space is a powerful representation for object recognition, outperforming several existing unsupervised feature learning methods.Comment: To appear at ECCV 201