学習モチベーションの社会構成主義アプローチ～ソーシャル・ネットワーク・サービスにおける多様ピアメッセージの推薦システム

Contemporary learning theories and their implementations associated with information and communication technologies increasingly integrate social constructivist approaches in order to assist and facilitate the construction of knowledge. Social constructivism also highlights the important role of culture, learning attitude and behavior in the cognitive process. Modern e-learning systems need to include these psychological aspects in addition to knowledge construction in order to connect with long-standing pedagogical issues such as the decrease and lack of motivation for education. Motivation is a central part of educational psychology and plays an important role in Computer-Supported Collaborative Learning (CSCL) environment. A prominent factor of motivation consists in the strong connection between pedagogical goals and purposes for learning because learners want to know the reasons why learning is important for them, to make it more meaningful. However, although pedagogical institutions provide structured curricula with specific outcomes, students are often unable to relate to these goals as they have various conceptual perceptions and learning purposes. This issue has even more consequence in informal and self-regulated learning environments where learners must monitor their own actions, motivation, and goals. Contemporary CSCL applications need therefore to integrate a larger social presence in order to provide more diverse purposes for achieving a shared goal. Current social networking services (SNS) provide a platform where peers can for instance express their passion, emotion and motivation towards learning. This research utilizes therefore this platform to recommend motivational contents from peers for learning motivation enhancement (i.e. learners’ perception of their goal and purpose for learning). The proposed system consists of an SNS platform for learners to 1) express and evaluate their own goals for learning, 2) observe diverse motivational messages expressed by peers who share a same goal and recommended by an LDA-based (Latent Dirichlet Allocation) model, and 3) evaluate their perceptions on motivational attributes after each observation. This platform initially requires a database of messages from peers publicly expressing on SNS their own purposes for learning various subjects. This part of the research focuses on collecting and analyzing messages from Twitter to determine linguistic features used to construct the meaning of expressing diverse learning purposes. The recommender system was implemented as a Web-based application using SNS environment to conduct an experiment over a semester, with students who could observe purposes expressed by other peers. Results compared evaluations from 77 students on motivational attributes before and after observing diverse or similar purposes from peers. Participants who observed diverse purposes significantly and positive enhanced their motivational perceptions, such as on goal specificity, attainability and on the confidence to achieve the desired outcome.電気通信大学201

Innovative Heuristics to Improve the Latent Dirichlet Allocation Methodology for Textual Analysis and a New Modernized Topic Modeling Approach

Natural Language Processing is a complex method of data mining the vast trove of documents created and made available every day. Topic modeling seeks to identify the topics within textual corpora with limited human input into the process to speed analysis. Current topic modeling techniques used in Natural Language Processing have limitations in the pre-processing steps. This dissertation studies topic modeling techniques, those limitations in the pre-processing, and introduces new algorithms to gain improvements from existing topic modeling techniques while being competitive with computational complexity. This research introduces four contributions to the field of Natural Language Processing and topic modeling. First, this research identifies a requirement for a more robust “stopwords” list and proposes a heuristic for creating a more robust list. Second, a new dimensionality-reduction technique is introduced that exploits the number of words within a document to infer importance to word choice. Third, an algorithm is developed to determine the number of topics within a corpus and demonstrated using a standard topic modeling data set. These techniques produce a higher quality result from the Latent Dirichlet Allocation topic modeling technique. Fourth, a novel heuristic utilizing Principal Component Analysis is introduced that is capable of determining the number of topics within a corpus that produces stable sets of topic words

Extraction Of Users Life Styles For Buddy Recommendation In Social Network

Friend-book is a novel semantic-based friend recommendation system for social networks which recommends friends to users based on their life styles as a substitute of social graphs. Friend-book can help mobile phone users find friends whichever among strangers or within a certain group as long as they share similar life styles. Friend-book assumes a client-server mode where each client is a smart phone carried by a user and the servers are data centres or clouds. With this module the accuracy of friend recommendation can be improved

A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews

Online reviews provide important demand-side knowledge for product manufacturers to improve product quality. However, discovering and quantifying potential products’ defects from large amounts of online reviews is a nontrivial task. In this paper, we propose a Latent Product Defect Mining model that identifies critical product defects. We define domain-oriented key attributes, such as components and keywords used to describe a defect, and build a novel LDA model to identify and acquire integral information about product defects. We conduct comprehensive evaluations including quantitative and qualitative evaluations to ensure the quality of discovered information. Experimental results show that the proposed model outperforms the standard LDA model, and could find more valuable information. Our research contributes to the extant product quality analytics literature and has significant managerial implications for researchers, policy makers, customers, and practitioners

Leverage Business Analytics and OWA to Recommend Appropriate Projects in Crowdfunding Platform

Nowadays, crowdfunding is becoming more and more popular. Many studies have been published on the crowdfunding platform from different perspectives. However, among all these studies, few are concerned about the recommendation methods, which, in effect, are highly beneficial to crowdfunding websites and the participants. Having considered the situation talked above, this paper works out the several features from the relative projects of user’s current browsing project. Then we give different weights to each feature based on selective attention phenomenon, and adopt the method of OWA operator to calculate the final score of each relative project and accomplish our model by picking out the four projects with different outstanding characteristics. Finally, according to the statistics on China’s famous crowdfunding website, we conducted a group of contrast experiments and eventually testified that our proposed model could, to some extent, help classify and give recommendation effectively. Furthermore, the results of this research can give guidance to the management of crowdfunding websites and they are also very significant advices for the future crowdfunding website development

Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC

The edge partition model (EPM) is a generative model for extracting an overlapping community structure from static graph-structured data. In the EPM, the gamma process (GaP) prior is adopted to infer the appropriate number of latent communities, and each vertex is endowed with a gamma distributed positive memberships vector. Despite having many attractive properties, inference in the EPM is typically performed using Markov chain Monte Carlo (MCMC) methods that prevent it from being applied to massive network data. In this paper, we generalize the EPM to account for dynamic enviroment by representing each vertex with a positive memberships vector constructed using Dirichlet prior specification, and capturing the time-evolving behaviour of vertices via a Dirichlet Markov chain construction. A simple-to-implement Gibbs sampler is proposed to perform posterior computation using Negative- Binomial augmentation technique. For large network data, we propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in the proposed model. The experimental results show that the novel methods achieve competitive performance in terms of link prediction, while being much faster

Modeling Human Group Behavior In Virtual Worlds

Virtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and analyzing public conversational patterns of users grouped in close physical proximity. To do this, we created a set of tools for monitoring, partitioning, and analyzing unstructured conversations between changing groups of participants in Second Life, a massively multi-player online user-constructed environment that allows users to construct and inhabit their own 3D world. Although there are some cues in the dialog, determining social interactions from unstructured chat data alone is a difficult problem, since these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether iii people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? These are the questions that we aim to answer in this thesis. The contributions of this thesis include: 1) a link prediction algorithm for identifying friendship relationships from unstructured chat data 2) a method for identifying social groups based on the results of community detection and topic analysis. The output of these two algorithms (links and group membership) are useful for studying a variety of research questions about human behavior in virtual worlds. To demonstrate this we have performed a longitudinal analysis of human groups in different regions of the Second Life virtual world. We believe that studies performed with our tools in virtual worlds will be a useful stepping stone toward creating a rich computational model of human group dynamics