Question: A hierarchical complementary attention network approach for forecasting stock price changes using news data.

This paper explores the utilization of structured events extracted from news to forecast stock price movements, marking a significant advancement in the realm of news-driven stock market prediction. Traditionally, such predictions have relied on shallow features, such as bags-of-words, named entities, and noun phrases, which lack the depth to fully capture the intricacies of entity-relation information within events. To address this limitation, we harness the power of Open Information Extraction (Open IE) techniques, which have recently gained prominence for their ability to extract structured events from vast pools of web data. By automating this process, we eliminate the need for manual intervention, making it a scalable solution for event-based stock price movement prediction.

We conduct a comprehensive investigation into the underlying relationships between structured events and stock market dynamics, employing both linear and nonlinear models. Our empirical study reveals that the accuracy of predicting the S&P 500 index reaches 60%, while individual stock predictions can surpass 70% accuracy. These findings highlight the superiority of our event-based approach over traditional bags-of-words models and outperform previously reported systems trained on historical S&P 500 stock data.

Our work underscores the potential of structured event extraction in enhancing the predictive capabilities of models designed to forecast stock price movements. By leveraging the nuanced information contained within these events, we can gain deeper insights into market trends, enabling more informed and accurate predictions. This research not only contributes to the theoretical understanding of event-driven market dynamics but also offers practical implications for financial analysts and investors seeking to make data-driven decisions.

This thesis addresses the problem of predicting message-level sentiments of English micro-blog messages from Twitter. Convolutional neural networks (CNN) have shown great promise in the task of sentiment classification. Here we expand the CNN proposed by [31, 32] and perform an in-depth analysis to deepen the understanding of these systems. In a first step we compare the performance of different architectures with focus on deeper architectures. We show how different choices of hyper-parameters impact the results and present the benefits and challenges of using deeper architectures. Second, we introduce the procedure for achieving a high quality initialization of the CNN and show the massive impact it has on its predictive capabilities. This procedure consists of creating custom word embeddings followed by pre-training the network using 90M tweets where the polarity of a tweet is inferred by noisy labels. Third, we present an in-depth analysis of the supervised phase. We show the impact of the pre-training on the supervised phase. We show how the choice of the optimization algorithm and the step size impact the variance of the score during the supervised phase. The analysis is concluded by an inspection of the errors made by the system. This gives us insights into the inner workings of the CNN and hints as to what is needed for further improvement. The CNN presented in this thesis is part of an ensemble which ranked 1st in the SemEval-2016 competition, Task 4a [22]. The task consists of classifying a given tweet as either positive, negative or neutral. This further demonstrates the potential these systems have in the field of sentiment analysis.

We discuss technology to help a person monitor changes in news coverage over time. We define temporal summaries of news stories as extracting a single sentence from each event within a news topic, where the stories are presented one at a time and sentences from a story must be ranked before the next story can be considered. We explain a method for evaluation, and describe an evaluation corpus that we have built. We also propose several methods for constructing temporal summaries and evaluate their effectiveness in comparison to degenerate cases. We show that simple approaches are effective, but that the problem is far from solved.

The study of sentiment analysis aims to explore the occurrences of opinion words in a document, and their orientation and strength. Most existing researches focus on writer's emotions, which are the feelings that authors were expressing. In contrast, news articles are usually described in objective terms, in which opinion words are rare. In this paper, we propose to discover the reader's feelings when they read news articles. First, different feature units are extracted such as unigrams, bigrams, and segmented words. We compare several feature selection methods including document frequency variation, information gain, mutual information, and chi-square test, to select the candidate features for sentiment classification. Then, the performance for multi-class classification and multiple binary classification are evaluated. The experimental results on real news articles show the effectiveness of the proposed method for discovering reader's emotions triggered from news articles. Further investigation is needed to validate the performance in larger scales.

We use an autoencoder composed of stacked restricted Boltzmann machines to extract features from the history of individual stock prices. Our model is able to discover an enhanced version of the momentum effect in stocks without extensive hand-engineering of input features and deliver an annualized return of 45.93% over the 1990-2009 test period versus 10.53% for basic momentum.

This paper presents the development of an ontology to represent financial headline news. This ontology is developed using the On-To-Knowledge methodology where the focus is on the design steps of the Knowledge Meta Process. This development is part of an ongoing project which aims to design a virtual stock market simulator based on multi-agent systems. The proposed ontology has 31 concepts and includes 201 attributes. The testing results conducted on reliable headline news show that 99% of these headline news can be properly represented by the attributes of the right category in the ontology. Unreliable headline news characterized by news having uncertainties, incompleteness, ill-definition, or imprecision cannot be represented by the proposed ontology. Approaches for representing these unreliable headline

With the development of online advertisements, clickbait spread wider and wider. Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy feature engineering and fail to distinguish clickbait from normal headlines precisely because of the limited information in headlines. A convolutional neural network is useful for clickbait detection, since it utilizes pretrained Word2Vec to understand the headlines semantically, and employs different kernels to find various characteristics of the headlines. However, different types of articles tend to use different ways to draw users’ attention, and a pretrained Word2Vec model cannot distinguish these different ways. To address this issue, we propose a clickbait convolutional neural network (CBCNN) to consider not only the overall characteristics but also specific characteristics from different article types. Our experimental results show that our method outperforms traditional clickbait-detection algorithms and the TextCNN model in terms of precision, recall and accuracy.

Cloud computing is the one of the admired paradigms of current era, which facilitates the users with on demand services and pay as you use services. It has tremendous applications in almost every sphere such as education, gaming, social networking, transportation, medical, business, stock market, pattern matching, etc. Stock market is such an industry where lots of data is generated and benefits are reaped on the basis of accurate prediction. So prediction is a vital part of stock market. Therefore, an attempt is being made to predict the stock market based on the given data set of stock market along with some features; using the techniques available for predictive data mining. Machine learning is one of the upcoming trends of data mining; hence few machine learning algorithms have been used such as Decision tree, Linear model, Random forest and further their results have been compared using the classification evaluation parameters such as H, AUC, ROC, TPR, FPR, etc. Random forest have been consider as the most effective model as it yield the highest accuracy of 54.12% whereas decision tree and linear model gives the accuracy of 51.87% and 52.83% respectively.

Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities. We propose two novel approaches to combine the outputs of attention mechanisms over each source sequence, flat and hierarchical. We compare the proposed methods with existing techniques and present results of systematic evaluation of those methods on the WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the proposed methods achieve competitive results on both tasks.

We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs). The input in this task is a sequence of utterances (i.e., conversational contributions) comprising a sequence of tokens, and the output is a sequence of DA labels (one label per utterance). Our model leverages the hierarchical nature of dialogue data by using two nested RNNs that capture long-range dependencies at the dialogue level and the utterance level. This model is combined with an attention mechanism that focuses on salient tokens in utterances. Our experimental results show that our model outperforms strong baselines on two popular datasets, Switchboard and MapTask; and our detailed empirical analysis highlights the impact of each aspect of our model.

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.


Question: A hierarchical complementary attention network approach for forecasting stock price movements using news data.

This paper introduces a sophisticated deep learning framework, Listening to Chaotic Whispers, aimed at predicting stock market trends with a focus on news-related data. Accurate stock trend forecasting is notoriously challenging due to the stock market's inherent volatility and non-stationary characteristics. The proliferation of internet-based information, coupled with advancements in natural language processing and text mining, offers a pathway for investors to discern market trends and volatility from online sources. However, the variable quality, reliability, and breadth of online content related to the stock market pose significant hurdles, with a substantial portion consisting of low-quality news, commentary, or even misinformation. To tackle these issues, our framework emulates human learning processes when confronted with chaotic online news, guided by three key principles: sequential content interdependence, diverse impact, and efficient learning. To embody the first two principles, we have engineered a Hybrid Attention Networks (HAN) model to forecast stock trends based on recent news sequences. Additionally, we incorporate a self-paced learning mechanism to reflect the third principle, mirroring the progressive learning observed in human cognition. Extensive testing on real-world stock market data underscores the efficacy of our framework. Furthermore, a simple trading strategy based on our proposed model showcases a notable enhancement in annualized returns, substantiating its practical value in financial decision-making.

In this paper we present a detailed scheme for annotating expressions of opinions, beliefs, emotions, sentiment and speculation (private states) in the news and other discourse. We explore inter-annotator agreement for individual private state expressions, and show that these low-level annotations are useful for producing higher-level subjective sentence annotations.

Abstract--This pal~er presents a neural network approach to multivariate time-series anal.lwis. Real world observations ~?/flottr /.'ices in three cities have been used as a benchmark in our ev~eriments, l"eed/orward connectionist networks have bt,t,tl ~k,.signed to model flottr l , ices over the period/iom ..lltgttsl 1972 to Novt,mher 1980./or the cities ~?[Blt[]ith~. :lliltneapoli.s, and Kansas Cit): Remarkable sttccess has heen achieved ill training the lletworks to leatvt the price CllrlV [i," each t?/these cities and in m a k i l l g acc l t ra le price predictions. Our r¢,~lt/l.s show that the it¢ltral network al~proat'h is a leading contender with the statistical modeling apl,oaches.

This study uses text and data mining to investigate the relationship between the text patterns of annual reports published by US listed companies and sales performance. Taking previous research a step further, although annual reports show only past and present financial information, analyzing text content can identify sentences or patterns that indicate the future business performance of a company. First, we examine the relation pattern between business risk factors and current business performance. For this purpose, we select companies belonging to two categories of US SIC (Standard Industry Classification) in the IT sector, 7370 and 7373, which include Twitter, Facebook, Google, Yahoo, etc. We manually collect sales and business risk information for a total of 54 companies that submitted an annual report (Form 10-K) for the last three years in these two categories. To establish a correlation between patterns of text and sales performance, four hypotheses were set and tested. To verify the hypotheses, statistical analysis of sales, statistical analysis of text sentences, sentiment analysis of sentences, clustering, dendrogram visualization, keyword extraction, and word-cloud visualization techniques are used. The results show that text length has some correlation with sales performance, and that patterns of frequently appearing words are correlated with the sales performance. However, a sentiment analysis indicates that the positive or negative tone of a report is not related to sales performance.

Stock market prediction is a challenging task to predict the upcoming stock values. It is very difficult to predict because of unstable nature of stock. This stock market prices are continuously changing day by day. Many Business Analysts spends more money on this stock market. Some of the persons may lose the money on this stock market because they don't have clear idea about the stock market. Estimate and analyse the stock market accurately then only get more profit. Artificial neural network is a very popular technique for forecast the stock market price, but using this technique to forecast the stock market price up to some extent. So there is a need to improve the accuracy of the system. In this paper, propose a novel system called Binarized Genetic Algorithm with Artificial Neural Network (BGANN) technique to predict and forecast future behavior of individual stocks or the upcoming stock. Binarized Genetic Algorithm is used for optimizing Neural Network weights while training. Comparative analysis shows that BGANN method performance is better compared to the Support Vector Machine (SVM), Neural Network (NN), and Auto Regressive Integrated Moving Average (ARIMA) models.

We model a market populated by two groups of boundedly rational agents: “newswatchers” and “momentum traders.” Each newswatcher observes some private information, but fails to extract other newswatchers’ information from prices. If information diffuses gradually across the population, prices underreact in the short run. The underreaction means that the momentum traders can profit by trendchasing. However, if they can only implement simple ~i.e., univariate! strategies, their attempts at arbitrage must inevitably lead to overreaction at long horizons. In addition to providing a unified account of underand overreactions, the model generates several other distinctive implications. OVER THE LAST SEVERAL YEARS, a large volume of empirical work has documented a variety of ways in which asset returns can be predicted based on publicly available information. Although different studies have used a host of different predictive variables, many of the results can be thought of as belonging to one of two broad categories of phenomena. On the one hand, returns appear to exhibit continuation, or momentum, in the short to medium run. On the other hand, there is also a tendency toward reversals, or fundamental reversion, in the long run.1 It is becoming increasingly clear that traditional asset-pricing models— such as the capital asset pricing model ~CAPM! of Sharpe ~1964! and Lintner ~1965!, Ross’s ~1976! arbitrage pricing theory ~APT!, or Merton’s ~1973! intertemporal capital asset pricing model ~ICAPM!—have a hard time explaining the growing set of stylized facts. In the context of these models, all of the predictable patterns in asset returns, at both short and long horizons, must ultimately be traced to differences in loadings on economically meaningful risk factors. And there is little affirmative evidence at this point to suggest that this can be done. * Stanford Business School and MIT Sloan School of Management and NBER. This research is supported by the National Science Foundation and the Finance Research Center at MIT. We are grateful to Denis Gromb, René Stulz, an anonymous referee, and seminar participants at MIT, Michigan, Wharton, Duke, UCLA, Berkeley, Stanford, and Illinois for helpful comments and suggestions. Thanks also to Melissa Cunniffe for help in preparing the manuscript. 1 We discuss this empirical work in detail and provide references in Section I below. THE JOURNAL OF FINANCE • VOL. LIV, NO. 6 • DECEMBER 1999

Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.

Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of 0.54, which are both better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignment.

The rapid development of online social media sites is accompanied by the generation of tremendous web contents. Web users are shifting from data consumers to data producers. As a result, topic detection and tracking without taking users' interests into account is not enough. This paper presents a statistical model that can detect interpretable trends and topics from document streams, where each trend (short for trending story) corresponds to a series of continuing events or a storyline. A topic is represented by a cluster of words frequently co-occurred. A trend can contain multiple topics and a topic can be shared by different trends. In addition, by leveraging a Recurrent Chinese Restaurant Process (RCRP), the number of trends in our model can be determined automatically without human intervention, so that our model can better generalize to unseen data. Furthermore, our proposed model incorporates user interest to fully simulate the generation process of web contents, which offers the opportunity for personalized recommendation in online social media. Experiments on three different datasets indicated that our proposed model can capture meaningful topics and trends, monitor rise and fall of detected trends, outperform baseline approach in terms of perplexity on held-out dataset, and improve the result of user participation prediction by leveraging users' interests to different trends.

This thesis addresses the problem of predicting message-level sentiments of English micro-blog messages from Twitter. Convolutional neural networks (CNN) have shown great promise in the task of sentiment classification. Here we expand the CNN proposed by [31, 32] and perform an in-depth analysis to deepen the understanding of these systems. In a first step we compare the performance of different architectures with focus on deeper architectures. We show how different choices of hyper-parameters impact the results and present the benefits and challenges of using deeper architectures. Second, we introduce the procedure for achieving a high quality initialization of the CNN and show the massive impact it has on its predictive capabilities. This procedure consists of creating custom word embeddings followed by pre-training the network using 90M tweets where the polarity of a tweet is inferred by noisy labels. Third, we present an in-depth analysis of the supervised phase. We show the impact of the pre-training on the supervised phase. We show how the choice of the optimization algorithm and the step size impact the variance of the score during the supervised phase. The analysis is concluded by an inspection of the errors made by the system. This gives us insights into the inner workings of the CNN and hints as to what is needed for further improvement. The CNN presented in this thesis is part of an ensemble which ranked 1st in the SemEval-2016 competition, Task 4a [22]. The task consists of classifying a given tweet as either positive, negative or neutral. This further demonstrates the potential these systems have in the field of sentiment analysis.

A prominent challenge in our information age is the classification over high frequency data streams. In this research, we propose an innovative and high-accurate text stream classificationmodel that is designed in an elastic distributed way and is capable to service text load with fluctuated frequency. In this classification model, text is represented as N-Gram Graphs and the classification process takes place using text pre-processing, graph similarity and feature classification techniques following the supervised machine learning approach. The work involves the analysis of many variations of the proposed model and its parameters, such as various representations of text as N-Gram Graphs, graph comparisons metrics and classification methods in order to conclude to the most accurate setup. To deal with the scalability, the availability and the timely response in case of high frequency text we employ the Beam programming model. Using the Beam programmingmodel the classification process occurs as a sequence of distinct tasks and facilitates the distributed implementation of the most computational demanding tasks of the inference stage. The proposed model and the various parameters that constitute it are evaluated experimentally and the high frequency stream emulated using two public datasets (20NewsGroup and Reuters-21578) that are commonly used in the literature for text classification.


Question: A hierarchical complementary attention network technique for forecasting stock price changes using news data.

This paper introduces the Hierarchical Complementary Attention Network (HCAN), a novel approach to predicting stock price movements by integrating news information. Unlike traditional models, HCAN employs a hierarchical attention mechanism to effectively extract and leverage the relevant features from news articles. The network is designed to capture both the local and global context of the news content, thereby enhancing its ability to understand the nuanced implications of the news on stock prices. HCAN processes news data at multiple levels, starting with word-level analysis to understand the semantics of individual words, followed by sentence-level analysis to capture the meaning of sentences, and finally, document-level analysis to comprehend the overall context of the news article. The complementary attention mechanism allows the network to focus on the most pertinent information at each level, ensuring that the model can selectively attend to the parts of the news that are most predictive of stock price movements. This hierarchical and complementary approach enables HCAN to outperform existing models in predicting stock price movements based on news data, demonstrating its effectiveness in capturing the complex relationships between news and financial markets.

The growing number of Twitter users makes it a valuable source of information to study what is happening right now. Users often use Twitter to report real-life events. Here we are only interested in following the financial community. This paper focuses on detecting events popularity through sentiment analysis of tweets published by the financial community on the Twitter universe. The detection of events popularity on Twitter makes this a non-trivial task due to noisy content that often are the tweets. This work aims to filter out all the noisy tweets in order to analyze only the tweets that influence the financial market, more specifically the thirty companies that compose the Dow Jones Average. To perform these tasks, in this paper it is proposed a methodology that starts from the financial community of Twitter and then filters the collected tweets, makes the sentiment analysis of the tweets and finally detects the important events in the life of companies. © 2016 Elsevier Ltd. All rights reserved.

Humans process visual scenes selectively and sequentially using attention. Central to models of human visual attention is the saliency map. We propose a hierarchical visual architecture that operates on a saliency map and uses a novel attention mechanism to sequentially focus on salient regions and take additional glimpses within those regions. The architecture is motivated by human visual attention, and is used for multi-label image classification on a novel multiset task, demonstrating that it achieves high precision and recall while localizing objects with its attention. Unlike conventional multi-label image classification models, the model supports multiset prediction due to a reinforcement-learning based training process that allows for arbitrary label permutation and multiple instances per label.

Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.

We introduce an autoregressive attention mechanism for parallelizable characterlevel sequence modeling. We use this method to augment a neural model consisting of blocks of causal convolutional layers connected by highway network skip connections. We denote the models with and without the proposed attention mechanism respectively as Highway Causal Convolution (Causal Conv) and Autoregressive-attention Causal Convolution (ARA-Conv). The autoregressive attention mechanism crucially maintains causality in the decoder, allowing for parallel implementation. We demonstrate that these models, compared to their recurrent counterparts, enable fast and accurate learning in character-level NLP tasks. In particular, these models outperform recurrent neural network models in natural language correction and language modeling tasks, and run in a fraction of the time.

We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a realistic video. Our hierarchical framework contains a sentence generator and a paragraph generator. The sentence generator produces one simple short sentence that describes a specific short video interval. It exploits both temporal-and spatial-attention mechanisms to selectively focus on visual elements during generation. The paragraph generator captures the inter-sentence dependency by taking as input the sentential embedding produced by the sentence generator, combining it with the paragraph history, and outputting the new initial state for the sentence generator. We evaluate our approach on two large-scale benchmark datasets: YouTubeClips and TACoS-MultiLevel. The experiments demonstrate that our approach significantly outperforms the current state-of-the-art methods with BLEU@4 scores 0.499 and 0.305 respectively.

This paper tests whether accurate sales forecasts for Nike are possible from Facebook data and how events related to Nike affect the activity on Nike's Facebook pages. The paper draws from the AIDA sales framework (Awareness, Interest, Desire, and Action) from the domain of marketing and employs the method of social set analysis from the domain of computational social science to model sales from Big Social Data. The dataset consists of (a) selection of Nike's Facebook pages with the number of likes, comments, posts etc. that have been registered for each page per day and (b) business data in terms of quarterly global sales figures published in Nike's financial reports. An event study is also conducted using the Social Set Visualizer (SoSeVi). The findings suggest that Facebook data does have informational value. Some of the simple regression models have a high forecasting accuracy. The multiple regressions have a lower forecasting accuracy and cause analysis barriers due to data set characteristics such as perfect multicollinearity. The event study found abnormal activity around several Nike specific events but inferences about those activity spikes, whether they are purely event-related or coincidences, can only be determined after detailed case-by-case text analysis. Our findings help assess the informational value of Big Social Data for a company's marketing strategy, sales operations and supply chain.

The purpose of this work is to empirically study desirable properties such as semantic coherence, attention mechanism and kernel reusability in Convolution Neural Networks (CNNs) for learning sentence classification tasks. We propose to learn semantically coherent kernels using clustering scheme combined with Word2Vec representation and domain knowledge such as SentiWordNet. We also suggest a technique to visualize attention mechanism of CNNs. These ideas are useful for decision explanation purpose. Reusable property enables kernels learned on one problem to be used in another problem. This helps in efficient learning as only a few additional domain specific kernels may have to be learned. Experimental results demonstrate the usefulness of our approach. The performance of the proposed approach, which uses semantic and re-usability properties, is close to that of the state-of-the-art approaches on many real-world datasets.

In this project, we exploit several deep learning architectures in Question Answering field, based on the newly released Stanford Question Answering dataset (SQuAD)[7]. We introduce a multi-stage process that encodes context paragraphs at different levels of granularity, uses co-attention mechanism to fuse representations of questions and context paragraphs, and finally decodes the co-attention vectors to get the answers. Our best model gets 62.23% F1 score and 48.72% EM score on the test set.

Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of 0.54, which are both better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignment.

Recent years have brought a symbolic growth in the volume of research in Sentiment Analysis, mostly on highly subjective text types like movie or product reviews. The main difference these texts have with news articles is that their target is apparently defined and unique across the text. Thence while dealing with news articles, we performed three subtasks namely identifying the target; separation of good and bad news content from the good and bad sentiment expressed on the target and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. On concluding these tasks, we present our work on mining opinions about three different Indian political parties during elections in the year 2009. We built a Corpus of 689 opinion-rich instances from three different English dailies namely The Hindu, Times of India and Economic Times extracted from 02/ 01/ 2009 to 05/ 01/ 2009 (MM/ DD/ YY). In which (a) we tested the relative suitability of various sentiment analysis methods (both machine learning and lexical based) and (b) we attempted to separate positive or negative opinion from good or bad news. Evaluation includes comparison of three sentiment analysis methods (two machine learning based and one lexical based) and analyzing the choice of certain words used in political text which influence the Sentiments of public in polls. This preliminary experiment will benefit in predicting and forecasting the winning party in forthcoming Indian elections 2014.


Question: Innovative thinking in tertiary education: Applying creative cognition to the analysis of textbook chapters.

In this book critique, we delve into the captivating world of "Flow: The Psychology of Optimal Experience," authored by Mihaly Csikszentmihalyi and published by HarperCollins in 2008. This seminal work, spanning 336 pages and bearing the ISBN 978-0-06-133920-2, explores the concept of flow—a state of heightened focus and immersion in activities that are both challenging and intrinsically rewarding. Csikszentmihalyi's exploration of flow offers profound insights into human psychology, particularly how individuals achieve a state of optimal experience. The book's relevance extends to various fields, including education, where understanding flow can enhance learning and creativity. By examining the conditions that facilitate flow, educators can design learning environments that promote deep engagement and creative cognition. This is particularly pertinent in the context of studying book sections, where the application of flow principles can transform passive reading into an active and enriching cognitive process. Through Csikszentmihalyi's lens, we gain a deeper understanding of how to cultivate creativity and engagement in higher education, making "Flow" an essential read for educators, students, and anyone interested in optimizing human potential.

Human cognitive architecture includes a working memory of limited capacity and duration with partially separate visual and auditory channels, and an effectively infinite long-term memory holding many schemas that can vary in their degree of automation. These cognitive structures have evolved to handle information that varies in the extent to which elements can be processed successively in working memory or, because they interact, must be processed simultaneously imposing a heavy load on working memory. Cognitive load theory uses this combination of information and cognitive structures to guide instructional design. Several designs that rely heavily on visual working memory and its characteristics are discussed in this paper. Knowledge of human cognitive architecture is essential for instructional design, and visual cognition is a central aspect of human cognition. Not surprisingly, there are several instructional design effects that rely heavily on the manner in which humans visually process information. This paper discusses some relevant information structures, cognitive structures and instructional designs that rely on our knowledge of visual information processing. A. Information structures While considerable work by many researchers over several decades has been devoted to the organization of human cognitive architecture, far less effort has gone into investigating the information structures that must have driven the evolution of that architecture. Some work has been carried out by Sweller (1994) and Halford, Wilson and Phillips (1998). Sweller (1994) suggested that all information can be placed on a continuum according to the extent to which the elements that constitute the information interact. At one extreme, there is no interaction between the elements that need to be learned. They are independent. Element interactivity is low, or indeed, non-existent, and that means each element can be considered and learned serially without reference to any other element. Because elements at the low element interactivity end of the continuum do not interact with each other, there is no loss of understanding despite each element being learned individually and in isolation. Understanding is defined as the ability to process all elements that necessarily interact, simultaneously in working memory. Learning such material

Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowledge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g .42), non-ITS computer-based instruction (g .57), and textbooks or workbooks (g .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g –.11) or small-group instruction (g .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.

This paper presents the methodology, design principles and detailed evaluation of a new freely available multilayer corpus, collected and edited via classroom annotation using collaborative software. After briefly discussing corpus design for open, extensible corpora, five classroom annotation projects are presented, covering structural markup in TEI XML, multiple part of speech tagging, constituent and dependency parsing, information structural and coreference annotation, and Rhetorical Structure Theory analysis. Layers are inspected for annotation quality and together they coalesce to form a richly annotated corpus that can be used to study the interactions between different levels of linguistic description. The evaluation gives an indication of the expected quality of a corpus created by students with relatively little training. A multifactorial example study on lexical NP coreference likelihood is also presented, which illustrates some applications of the corpus. The results of this project show that high quality, richly annotated resources can be created effectively as part of a linguistics curriculum, opening new possibilities not just for research, but also for corpora in linguistics pedagogy.

In this research paper we consider formative assessment (FA) and discuss ways in which it has been implemented in four different university courses. We illustrate the different aspects of FA by deconstructing it and then demonstrating effectiveness in improving both teaching and student achievement. It appears that specifically “what is done” was less important since there were positive achievement gains in each study. While positive gains were realized with use of technology, gains were also realized with implementation of nontechnology dependent techniques. Further, gains were independent of class size or subject matter.

How can designers of programming interfaces, interactive tools, and rich social environments enable more people to be more creative more often?

A review and meta-analysis was performed of seventy-five articles concerned with innovation characteristics and their relationship to innovation adoption and implementation. One part of the analysis consisted of constructing a methodological profile of the existing studies, and contrasting this with a hypothetical optimal approach. A second part of the study employed meta-analytic statistical techniques to assess the generality and consistency of existing empirical findings. Three innovation characteristics (compatibility, relative advantage, and complexity) had the most consistent significant relationship to innovation adoption. Suggestions for future research in the area were made.

Educational Data Mining (EDM) is an evolving field exploring pedagogical data by applying different machine learning techniques/tools. It can be considered as interdisciplinary research field which provides intrinsic knowledge of teaching and learning process for effective education. The main objective of any educational institution is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge that predicts teachers’ performance. This study presents an efficient system model for evaluation and prediction of teachers’ performance in higher institutions of learning using data mining technologies. To achieve the objectives of this work, a two-layered classifier system was designed; it consists of an Artificial Neural Network (ANN) and Decision Tree. The classifier system was tested successfully using case study data from a Nigerian University in the South West of Nigeria. The data consists of academic qualifications for teachers as well as their experiences and grades of students in courses they taught among others. The attribute selected were evaluated using two feature selection methods in order to get a subset of the attributes that would make for a compact and accurate predictive model. The WEKA machine learning tool was used for the mining. The results show that, among the six attributes used, Working Experience, and Rank are rated the best two attributes that contributed mostly to the performance of teachers in this study. Also, considering the time taken to build the models and performance accuracy level, C4.5 decision tree outperformed the other two algorithms (ID3 and MLP) with good performance of 83.5% accuracy level and acceptable kappa statistics of 0.743. It does mean that C4.5 decision tree is best algorithm suitable for predicting teachers’ performance in relation to the other two algorithms in this work. General Terms Data Mining

Recent policy initiatives in England have focused on promoting ‘interactive’ teaching in schools, with a clear expectation that this will lead to improvements in learning. This expectation is based on the perceived success of such approaches in other parts of the world.At the same time, there has been a large investment in Information and Communication Technology (ICT) resources, and particularly in interactive whiteboard technology. This paper explores the idea of interactive teaching in relation to the interactive technology which might be used to support it. It explains the development of a framework for the detailed analysis of teaching and learning in activity settings which is designed to represent the features and relationships involved in interactivity. When applied to a case study of interactive teaching during a lesson involving a variety of technology-based activities, the framework reveals a confusion of purpose in students’ use of an ICT resource that limits the potential for learning when students are working independently. Discussion of relationships between technical and pedagogical interactivity points a way forward concerning greater focus on learning goals during activity in order to enable learners to be more autonomous in exploiting ICT’s affordances, and the conclusion identifies the variables and issues which need to be considered in future research which will illuminate this path.

Although there is evidence that academically successful students are engaged with their studies, it has proved difficult to define student engagement clearly. Student engagement is commonly construed as having two dimensions, social and academic. The rapid adoption of social media and digital technologies has ensured increasing interest in using them for improving student engagement. This paper examines Facebook usage among a first year psychology student cohort and reports that although the majority of students (94%) had Facebook accounts and spent an average of one hour per day on Facebook, usage was found to be predominantly social. Personality factors influenced usage patterns, with more conscientious students tending to use Facebook less than less conscientious students. This paper argues that, rather than promoting social engagement in a way that might increase academic engagement, it appears that Facebook is more likely to operate as a distracting influence.

This paper extends the Function-Behaviour-Structure (FBS) framework, which proposed eight fundamental processes involved in designing. This framework did not explicitly account for the dynamic character of the context in which designing takes place, described by the notion of situatedness. This paper describes this concept as a recursive interrelationship between different environments, which, together with a model of constructive memory, provides the foundation of a situated FBS framework. The eight fundamental processes are then reconstructed within this new framework to represent designing in a dynamic world.


Question: Innovativeness in Tertiary Education: Applying Creative Thinking to the Analysis of Textbook Chapters.

This study explores the impact of positive emotions on creative problem-solving abilities. Across four experiments, researchers observed that participants who experienced positive affect, induced through brief exposure to a comedy film or the surprise of receiving a small gift, demonstrated enhanced performance on tasks requiring creative ingenuity. These tasks included Duncker's classic candle problem and Mednick's Remote Associates Test. Notably, conditions that induced negative emotions or involved physical exercise, intended to provoke affectless arousal, did not yield similar improvements in creative performance. The study's findings contribute to a larger theoretical framework that elucidates how positive affect can influence cognitive organization, specifically in the context of creativity. This research underscores the role of positive emotions in fostering creative cognition, suggesting that a positive emotional state can act as a catalyst for innovative thinking in educational settings.

Education helps people develop as individuals. It not only helps build social skills but also enhances the problem solving and decision making skills of individuals. With the growing number of schools, colleges and universities around the globe, education now has taken new dimension. The main focus of higher Educational Institutes is to improve the overall quality and effectiveness of education. Predicting the performance of student and Predicting the Future in this world of Big Data is the need of Today in Education. The crude data from educational organizations act as a gold mine which facilitate the analysts and designers to make a system that will improve the overall teaching and learning process. This new field coined as Educational Data mining helps in Predicting the Future and Changing the Future. In this paper we have studied what is Educational Data Mining and what are the Application areas of EDM, various techniques of EDM and the factors affecting the students' Academic Performance and the Teaching-Learning process.

been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students’ attention and interest for hours. We have been working on a new generation of intelligent tutoring systems that hold mixedinitiative conversational dialogues with the learner. The tutoring systems present challenging problems and questions to the learner, the learner types in answers in English, and there is a lengthy multiturn dialogue as complete solutions or answers evolve. This article presents the tutoring systems that we have been developing. AUTOTUTOR is a conversational agent, with a talking head, that helps college students learn about computer literacy. ANDES, ATLAS, AND WHY2 help adults learn about physics. Instead of being mere information-delivery systems, our systems help students actively construct knowledge through conversations.

Comprehension is the goal of reading. However, students often encounter reading difficulties due to the lack of background knowledge and proper reading strategy. Unfortunately, print text provides very limited assistance to one’s reading comprehension through its static knowledge representations such as symbols, charts, and graphs. Integrating digital materials and reading strategy into paper-based reading activities may bring opportunities for learners to make meaning of the print material. In this study, QR codes were adopted in association with mobile technology to deliver supplementary materials and questions to support students’ reading. QR codes were printed on paper prints to provide direct access to digital materials and scaffolded questions. Smartphones were used to scan the printed QR codes to fetch predesigned digital resources and scaffolded questions over the Internet. A quasi-experiment was conducted to evaluate the effectiveness of direct access to the digital materials prepared by the instructor using QR codes and that of scaffolded questioning in improving students’ reading comprehension. The results suggested that direct access to digital resources using QR codes does not significantly influence students’ reading comprehension; however, the reading strategy of scaffolded questioning significantly improves students’ understanding about the text. The survey showed that most students agreed that the integrated print-and-digital-materialbased learning system benefits English reading comprehension but may not be as efficient as expected. The implications of the findings shed light on future improvement of the system. 2011 Elsevier Ltd. All rights reserved.

The field of artificial intelligence in education (AIED) uses techniques from AI and cognitive science to better understand the nature of learning and teaching and to build systems to help learners gain new skills or understand new concepts. This article studies metareviews and meta-analyses to make the case for blended learning, wherein the teacher can offload some work to AIED systems.

Collaborative creativity is traditionally supported by formal techniques, such as brainstorming. These techniques improve the idea-generation process by creating group synergies, but also suffer from a number of negative effects. Current electronic tools to support collaborative creativity overcome some of these problems, but introduce new ones, by either losing the benefits of face-to-face communication or the immediacy of simultaneous contribution. Using an interactive environment as a test bed, we are investigating how collaborative creativity can be supported electronically while maintaining face-to-face communication. What are the design-factors influencing such a system? We have designed a brainstorming application that uses an interactive table and a large wall display, and compared the results of using it to traditional paper-based brainstorming in a user study with 30 participants. From the considerations that went into the design and the observations during the study we derive a number of design guidelines for collaborative systems in interactive environments.

Addressing unresolved questions concerning computational thinking.

The idea that a new generation of students is entering the education system has excited recent attention among educators and education commentators. Termed ‘digital natives’ or the ‘Net generation’, these young people are said to have been immersed in technology all their lives, imbuing them with sophisticated technical skills and learning preferences for which traditional education is unprepared. Grand claims are being made about the nature of this generational change and about the urgent necessity for educational reform in response. A sense of impending crisis pervades this debate. However, the actual situation is far from clear. In this paper, the authors draw on the fields of education and sociology to analyse the digital natives debate. The paper presents and questions the main claims made about digital natives and analyses the nature of the debate itself. We argue that rather than being empirically and theoretically informed, the debate can be likened to an academic form of a ‘moral panic’. We propose that a more measured and disinterested approach is now required to investigate ‘digital natives’ and their implications for education. The one thing that does not change is that at any and every time it appears that there have been ‘great changes’. Marcel Proust, Within a Budding Grove

In this paper, we devise a diagrammatic conceptualization to describe and represent the complex interplay of a teacher’s knowledge (K), goals (G) and beliefs (B) in leveraging technology effectively in the classroom. The degree of coherency between the KGB region and the affordances of the technology serves as an indicator of the teachers’ developmental progression through the initiation, implementation and maturation phases of using technology in the classroom. In our study, two teachers with differing knowledge, goals and beliefs are studied as they integrated GroupScribbles technology in their classroom lessons over a period of 1 year. Our findings reveal that the transition between the teacher’s developmental states (as indicated by coherency diagrams) is nonlinear, and thus the importance of ensuring high coherency right at the initiation stage. Support for the teacher from other teachers and researchers remains an important factor in developing the teacher’s competency to leverage the technology successfully. The stability of the KGB region further ensures smooth progression of the teacher’s effective integration of technology in the classroom.

This article considers the developing field of learning analytics and argues that to move from small-scale practice to broad scale applicability, there is a need to establish a contextual framework that helps teachers interpret the information that analytics provides. The article presents learning design as a form of documentation of pedagogical intent that can provide the context for making sense of diverse sets of analytic data. We investigate one example of learning design to explore how broad categories of analytics—which we call checkpoint and process analytics—can inform the interpretation of outcomes from a learning design and facilitate pedagogical action.


Question: Innovativeness in tertiary education: Employing creative cognition for analyzing book chapters.

This paper delves into the debate over whether positive affect (PA) is the bipolar opposite or an independent entity from negative affect (NA). Historically, this discussion has been clouded by the misapprehension that a bipolar relationship implies a constant latent correlation between PA and NA. However, the predicted correlation is subject to fluctuation based on various factors including the time frame of measurement, the response format utilized, and the specific items chosen to represent PA and NA. Moreover, measurement errors inherently contribute to the variability of the observed correlation. When the bipolar model's actual predictions are examined and measurement error is accounted for, the evidence for the independence of these traditionally perceived opposites diminishes. Instead, the bipolarity model offers a more concise explanation that aligns well with the existing data, suggesting that PA and NA are not merely independent constructs but rather two ends of a single continuum.

With recent developments and advances in distance learning and MOOCs, the amount of open educational videos on the Internet has grown dramatically in the past decade. However, most of these videos are lengthy and lack of high-quality indexing and annotations, which triggers an urgent demand for efficient and effective tools that facilitate video content navigation and exploration. In this paper, we propose a novel visual navigation system for exploring open educational videos. The system tightly integrates multimodal cues obtained from the visual, audio and textual channels of the video and presents them with a series of interactive visualization components. With the help of this system, users can explore the video content using multiple levels of details to identify content of interest with ease. Extensive experiments and comparisons against previous studies demonstrate the effectiveness of the proposed system.

In 2 experiments, students who lacked prior knowledge about car mechanics read a passage about vehicle braking systems that either contained labeled illustrations of the systems, illustrations without labels, labels without illustrations, or no labeled illustrations. Students who received passages that contained labeled illustrations of braking systems recalled more explanative than nonexplanative information as compared to control groups, and performed better on problem solving transfer but not on verbatim recognition as compared to control groups. Results support a model of meaningful learning in which illustrations can help readers to focus their attention on explanative information in text and to reorganize the information into useful mental models.

Programming is recognized as one of seven grand challenges in computing education. Decades of research have shown that the major problems novices experience are composition-based---they may know what the individual programming language constructs are, but they do not know how to put them together. Despite this fact, textbooks, educational practice, and programming education research hardly address the issue of teaching the skills needed for systematic development of programs.
 We provide a conceptual framework for incremental program development, called Stepwise Improvement, which unifies best practice in modern software development such as test-driven development and refactoring with the prevailing perspective of programming methodology, stepwise refinement. The conceptual framework enables well-defined characterizations of incremental program development.
 We utilize the conceptual framework to derive a programming process, STREAM, designed specifically for novices. STREAM is a carefully down-scaled version of a full and rich agile software engineering process particularly suited for novices learning object-oriented programming. In using it we hope to achieve two things: to help novice programmers learn faster and better while at the same time laying the foundation for a more thorough treatment of more advanced aspects of software engineering. In this article, two examples demonstrate the application of STREAM.
 The STREAM process has been taught in the introductory programming courses at our universities for the past three years and the results are very encouraging. We report on a small, preliminary study evaluating the learning outcome of teaching STREAM. The study indicates a positive effect on the development of students’ process competences.

SUMM AR Y Outcome-based education, a performance-based approach at the cutting edge of curr iculum development, offers a powerful and appealing way of reforming and managing medical education. The emphasis is on the productÐ what sort of doctor will be producedÐ rather than on the educational process. In outcome-based education the educational outcomes are clearly and unambiguously speci ® ed. These determine the curr iculum content and its organisation, the teaching methods and strategies, the courses offered, the assessment process, the educational environment and the curriculum timetable.They also provide a framework for curr iculum evaluation. A doctor is a unique combination of different kinds of abilities. A three-circle model can be used to present the learning outcomes in medical education, with the tasks to be performed by the doctor in the inner core, the approaches to the performance of the tasks in the middle area, and the growth of the individual and his or her role in the practice of medicine in the outer area. Medical schools need to prepare young doctors to practise in an increasingly complex healthcare scene with changing patient and public expectations, and increasing demands from employing authorities. Outcome-based education offers many advantages as a way of achieving this. It emphasises relevance in the curriculum and accountability, and can provide a clear and unambiguous framework for curr iculum planning which has an intuitive appeal. It encourages the teacher and the student to share responsibility for learning and it can guide student assessment and course evaluation. What sort of outcomes should be covered in a curriculum, how should they be assessed and how should outcome-based education be implemented are issues that need to be addressed.

This report describes the results of a ground-breaking research study that measured the antecedents and consequents of effective human capital management. The research sample consisted of 76 senior executives from 25 companies in the financial services industry. The results of the study yielded a holistic causal map that integrated constructs from the fields of intellectual capital, knowledge management, human resources, organizational behaviour, information technology and accounting. The integration of both quantitative and qualitative measures in an overall conceptual model yielded several research implications. The resulting structural equation model allows participating organizations and researchers to gauge the effectiveness of an organization’s human capital capabilities. This will allow practitioners and researchers to more efficiently allocate resources with regard to human capital management. The potential outcomes of the study are limitless, since a program of consistent re-evaluation can lead to the establishment of causal relationships between human capital management and economic and business results. Introduction Today’s knowledge-based world consists of universal dynamic change and massive information bombardment. By the year 2010, the codified information base of the world is expected to `̀ double every 11 hours’’ (Bontis, 1999, p. 435). Information storage capacities continue to expand enormously. In 1950, IBM’s Rama C tape contained 4.4 megabytes and they were able to store as many as 50 of these tapes together. At that time, 220 megabytes represented the frontiers of information storage. Many of today’s standard desktop computers are being sold with 40 gigabytes of hard disk space. It is sobering to remember that full motion video in uncompressed form requires 1 gigabyte per minute and that the 83 minutes of Snow White digitized in full colour amount to 15 terabytes of space. Unfortunately, the conscious mind is only capable of processing somewhere between 16 and 40 bits of information (ones and zeros) per second. How do we reconcile this information bombardment conundrum when it seems that human beings are the bottle-neck? The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/1469-1930.htm The authors would like to acknowledge the following organizations for their financial support: Accenture, Saratoga Institute and the Institute for Intellectual Capital Research. The authors would also like to highlight the contribution of Vanessa Yeh, who administered the data collection phase of this research.

This paper presents the results of a field experiment on online examinations facilitated by collaboration support systems. In particular, it examines collaborative learning and virtual teams through online examinations as an assessment procedure, compared to traditional examinations. Assessment is increasingly regarded as an important part of the learning process. Applying constructivism and collaborative-learning theories, the collaborative examination process features students' active participation in various phases of the exam process through small group activities online. A 1 times 3 field experiment evaluated the collaborative online exam compared with the traditional in-class exam, and the participatory exam, where students participated in the online exam processes without groups. Data analysis using results from 485 students indicates that collaborative examinations significantly enhance interactions and the sense of an online learning community and result in significantly higher levels of perceived learning

Education helps people develop as individuals. It not only helps build social skills but also enhances the problem solving and decision making skills of individuals. With the growing number of schools, colleges and universities around the globe, education now has taken new dimension. The main focus of higher Educational Institutes is to improve the overall quality and effectiveness of education. Predicting the performance of student and Predicting the Future in this world of Big Data is the need of Today in Education. The crude data from educational organizations act as a gold mine which facilitate the analysts and designers to make a system that will improve the overall teaching and learning process. This new field coined as Educational Data mining helps in Predicting the Future and Changing the Future. In this paper we have studied what is Educational Data Mining and what are the Application areas of EDM, various techniques of EDM and the factors affecting the students' Academic Performance and the Teaching-Learning process.

BOOK REVIEW: Csikszentmihalyi, M. (2008). Flow: The Psychology of Optimal Experience. New York, NY: HarperCollins. 336 pp. ISBN 978-0-06-133920-2.

We introduce Slant, a system that integrates more than a decade of research into computational creativity, and specifically story generation, by connecting subsystems that deal with plot, figuration, and the narrative discourse using a blackboard. The process of integrating these systems highlights differences in the representation of story and has led to a better understanding of how story can be usefully abstracted. The plot generator MEXICA and a component of Curveship are used with little modification in Slant, while the figuration subsystem Fig-S and the template generator GRIOT-Gen, inspired by GRIOT, are also components. The development of the new subsystem Verso, which deals with genre, shows how different genres can be computationally modeled and applied to in-development stories to generate results that are surprising in terms of their connections and valuable in terms of their relationship to cultural questions. Example stories are discussed, as is the potential of the system to allow for broader collaboration, the empirical testing of how subsystems interrelate, and possible contributions in literary and artistic contexts.

The progress registered in the use of video games as educational tools has not yet been successfully transferred to the classroom. In an attempt to close this gap, a framework was developed that assists in the design and classroom integration of educational games. The framework addresses both the educational dimension and the ludic dimension. The educational dimension employs Bloom’s revised taxonomy to define learning objectives and applies the classroom multiplayer presential game (CMPG) pedagogical model while the ludic dimension determines the gaming elements subject to constraints imposed by the educational dimension. With a view to validating the framework, a game for teaching electrostatics was designed and experimentally implemented in a classroom context. An evaluation based on pre/post testing found that the game increased the average number of correct answers by students participating in the experiment from 6.11 to 10.00, a result found to be statistically significant. Thus validated, the framework offers a promising basis for further exploration through the development of other games and fine-tuning of its components. 2011 Elsevier Ltd. All rights reserved.


Question: The application of creative cognition for analyzing book sections in higher education studies.

This paper explores the complex interplay between culture and the categorization of emotions, a topic that has sparked significant debate among scholars. Some argue that there is a universal human tendency to differentiate emotions from other experiences and to categorize them into distinct types, such as happiness, anger, and fear. Others, however, contend that these categorizations are not universal but are instead shaped by cultural and linguistic factors. A comprehensive review of ethnographic and cross-cultural research on three key areas—emotion lexicons, emotions conveyed through facial expressions, and dimensions involved in comparative judgments of emotions—highlights both commonalities and variations across different languages and cultures in how emotions are understood and classified.

The paper then evaluates five prominent hypotheses concerning the cultural and universal aspects of emotion categorization. The first hypothesis posits that while there are basic categories of emotion that are shared across cultures, the specific subcategories within these basic types may vary from one culture to another. The second hypothesis suggests that emotional focal points, or the core aspects of emotional experiences, are universal, but the precise boundaries and distinctions between emotions can be culturally determined. The third hypothesis proposes that emotion categories have evolved from a single, fundamental category of physiological arousal, which has diversified over time and across cultures. The fourth hypothesis argues that although many emotion categories are culture-specific, they can be defined using a set of universal semantic primitives that underlie human understanding of emotions. Lastly, the fifth hypothesis envisions emotion categories as scripts that contain both culture-specific elements and universal components, reflecting the dual influence of cultural context and human universals on emotional experience.

In summary, this paper delves into the intricate relationship between culture and emotion categorization, offering a nuanced perspective that acknowledges both the universal aspects of emotional experience and the significant role played by cultural and linguistic factors in shaping how emotions are understood and expressed across different societies.

As known, adventure and experience about lesson, entertainment, and knowledge can be gained by only reading a book. Even it is not directly done, you can know more about this life, about the world. We offer you this proper and easy way to gain those all. We offer many book collections from fictions to science at all. One of them is this computer vision algorithms and applications that can be your partner.

Structured Abstract Purpose – Designing a model and tools that are (a) capable of representing and handling personal knowledge in different degrees of structuredness and formalisation and (b) usable and extensible by end-users. Design/methodology/approach – This paper presents the result of analysing literature and various data-models and formalism used to structure information on the desktop. Findings – The unified data model (CDS) is capable of representing structures from various information tools, e. g. documents, file system, hypertext, tagging, and mind maps. The five knowledge axes of CDS are: identity, order, hierarchy, annotation and linking. Research limitations / implications – The CDS model is based on text. Extensions for multimedia annotations have not been investigated. Practical implications – Future PKM tools should take the mentioned shortcoming of existing PKM tools into account. Implementing the CDS model can be a way to make PKM tools interoperable. Originality/value – This paper presents research combining cognitive psychology, personal knowledge management and semantic web technologies. The CDS model shows a way to let end-users work on different levels of granularity and different levels of formality in one environment. Introduction " The most important contribution of management in the 20th century was to increase manual worker productivity fifty-fold. The most important contribution of management in the 21st century will be to increase knowledge worker productivity – hopefully by the same percentage. [...] The methods, however, are totally different from those that increased the productivity of manual workers. " Drucker (1999a, p. 79) What might these methods be? The field of knowledge management investigates since 1995 (Stankosky, 2005) how people and knowledge work together. North (2007) defines knowledge work as work based on knowledge with an immaterial result; value creation is based on processing, generating and communicating knowledge. Polanyi (1966) makes a distinction between explicit knowledge encoded in artefacts such as books or web pages, and tacit knowledge which resides in the individual. The SECI-model of Nonaka (1994) describes knowledge transfers between humans and artefacts. The field of knowledge management has focused on knowledge transfer between people, either via socialisation or via externalised artefacts. The high expectations of central enterprise knowledge repositories have often not been met (Braganza and Mollenkramer, 2002). The following wave of expert finders and corporate white pages focused mostly on connecting the right people and let them communicate. Today, knowledge workers are flooded with information (Alvarado et al., 2003). The field of Personal …

This qualitative study explored the feelings of students about using the library for research. Personal writing, collected in beginning composition courses over a two-year period, was analyzed for recurrent themes. It was found that 75 to 85 percent of the students in these courses described their initial response to library research in terms of fear. Three concepts emerged from these descriptions: (1) students generally feel that their own library-use skills are inadequate while the skills of other students are adequate, (2) the inadequacy is shameful and should be hidden, and (3) the inadequacy would be revealed by asking questions. A grounded theory of library anxiety was constructed from these data.

By reading, you can know the knowledge and things more, not only about what you get from people to people. Book will be more trusted. As this rethinking education in the age of technology review, it will really give you the good idea to be successful. It is not only for you to be success in certain life you can be successful in everything. The success can be started by knowing the basic knowledge and do actions.

In this paper we propose a theoretical framework of distributed representations and a methodology of representational analysis for the study of distributed cognitive tasksÑtasks that require the processing of information distributed across the internal mind and the external environment. The basic principle of distributed representations is that the representational system of a distributed cognitive task is a set of internal and external representations, which together represent the abstract structure of the task. The basic strategy of representational analysis is to decompose the representation of a hierarchical task into its component levels so that the representational properties at each level can be independently examined. The theoretical framework and the methodology are used to analyze the hierarchical structure of the Tower of Hanoi problem. Based on this analysis, four experiments are designed to examine the representational properties of the Tower of Hanoi. Finally, the nature of external representations is discussed. This research was supported by a grant to Donald Norman and Edwin Hutchins from the Ames Research Center of the National Aeronautics & Space Agency, Grant NCC 2-591 in the Aviation Safety/Automation Program, technical monitor, Everett Palmer. Additional support was provided by funds from the Apple Computer Company and the Digital Equipment Corporation to the Affiliates of Cognitive Science at

Live streaming-an emerging practice of broadcasting video of oneself in real time to an online audience-is often used by people to portray themselves engaged in a craft such as programming. Viewers of these 'creative streams' gather to watch the streamer at work and to interact with the streamer and other audience members. However, little is known about how creative streamers engage with their audience, how their viewership communities form and operate, and how creative streams may support learning. In this study, we used a participant-observer method to study game development streams on the live streaming site Twitch.tv. We found that live streams support the growth of learning-focused communities that mentor both the streamer and each other during and after streams. We show the influence of streamers in creating a space for learning and motivating learners. Finally, we discuss implications for online education and communities of practice.

Addressing unresolved questions concerning computational thinking.

This case study examines the physical aspects of a particular university classroom, and what affect specific changes to the classroom had on the perceptions of students, instructors and observers regarding the room as an effective learning space. We compare survey and focus group data collected from students taking courses in the classroom prior to changes to the physical environment with comparable data from students taking courses in the same classroom after specific changes had been made. Immediately following changes to the classroom, notable increases were observed in reported perceptions of student satisfaction with the physical environment, including perceptions of the classroom as a more effective and engaging learning space. Similar perceptions of improvement as a teaching-learning space were reported by instructors and observers. However, subsequent follow-up data collection and analyses suggested little if any sustained increase in perceptions of efficacy of the room as a learning space; indeed, most reported variables returned to baseline levels. The implications of these findings and their relevance to classroom design nevertheless may provide insight regarding the manner in which physical space might support or even enhance teaching and learning. Keywords: learning spaces, active learning, classrooms, teaching and learning environment, classroom design For a number of years there has been an on-going pedagogical shift in higher education away from a traditional content delivery model of instruction to more active models of learning in which students play more involved and interactive roles within the classroom (Cornell, 2002; Brown, 2006). This movement has been coupled with the recognition that the traditional university classroom, with its unidirectional design and tiered, fixed theatre-like seating, is insufficient to accommodate what have increasingly become more varied teaching and learning practices. This growing realization that as the nature of teaching and learning evolves so too must teaching and learning spaces has, in recent years, resulted in a heightened interest among scholars in the examination of classroom space and, more specifically, the inquiry into the connection between classroom design and pedagogy and learning (Brooks, 2011). This study presents findings from an on-going research project examining classroom spaces on a small university campus. The present case study, which is one component of the larger project, compares perceptions of a specific classroom as an effective learning space before and after relatively extensive changes were made to the physical environment of the room. These changes were guided by feedback from students and instructors as well as by existing literature on innovative classroom designs. In particular, we were interested in assessing if these changes 1
  University
  of
  Lethbridge,
  Lethbridge,
  Alberta,
  Canada

Part I supports these sentences with findings from cognitive science "that human reason is a form of animal reason, a reason inextricably tied to our bodies and the peculiarities of our brains" and "that our bodies, brains, and interactions with our environment provide the mostly unconscious basis for our everyday metaphysics, that is, our sense of what is real." The third sentence is a summary of their earlier work on metaphor (Lakoff and Johnson 1980), which in Part II of this book they extend to a more detailed analysis of the metaphors underlying basic philosophical issues, such as time, events and causes, mind, self, and morality. Part III applies that analysis to the metaphors tacitly assumed by philosophers ranging from the Presocratics to Noam Chomsky. Part IV presents arguments for "empirically responsible philosophy" and its potential for understanding "who we are, how we experience our world, and how we ought to live." Finally, the appendix summarizes research inspired by this philosophy that has produced computational simulations of certain aspects of embodied minds. Given such a broad scope, the authors have not been able to cover all the topics with equal success. Instead of challenging all of western philosophy, they should have concentrated on their major opponent, Noam Chomsky and his philosophy of language. Lakoff began his career in linguistics as a student and later a teacher of Chomsky's version of transformational grammar. But in the late 1960s, he joined with other former students to promote generative semantics as an alternative to Chomsky's generative syntax. The result was a series of "linguistic wars," whose history has been retold by various participants over the past twenty years. Chapter 22 of this book presents a strong case against Chomsky's "autonomous syntax" and for an approach that bases syntax on semantics and semantics on the bodily mechanisms of perception and action. The philosophical and linguistic arguments of Chapter 22 are reinforced in the appendix by a summary of the Neural Theory of Language (NTL), which is being developed by Jerome Feldman, George Lakoff, Lokendra Shastri, and their students. To date, the NTL group has undertaken three major tasks in language understanding

In this paper, a machine learning approach to support the user during the correction of the layout analysis is proposed. Layout analysis is the process of extracting a hierarchical structure describing the layout of a page. In our approach, the layout analysis is performed in two steps: firstly, the global analysis determines possible areas containing paragraphs, sections, columns, figures and tables, and secondly, the local analysis groups together blocks that possibly fall within the same area. The result of the local analysis process strongly depends on the quality of the results of the first step. We investigate the possibility of supporting the user during the correction of the results of the global analysis. This is done by allowing the user to correct the results of the global analysis and then by learning rules for layout correction from the sequence of user actions. Experimental results on a set of multi-page documents are reported and commented. 1. Background and motivation Strategies for the extraction of layout analysis have been traditionally classified as top-down or bottom-up [10]. In top-down methods, the document image is repeatedly decomposed into smaller and smaller components, while in bottom-up methods, basic layout components are extracted from bitmaps and then grouped together into larger blocks on the basis of their characteristics. In WISDOM++, a document image analysis system that can transform paper documents into XML format [1], the applied page decomposition method is hybrid, since it combines a top-down approach to segment the document image, and a bottom-up layout analysis method to assemble basic blocks into frames. Some attempts to learn the layout structure from a set of training examples have also been reported in the literature [2,3,4,7,11]. They are based on ad-hoc learning algorithms, which learn particular data structures, such as geometric trees and tree grammars. Results are promising, although it has been proven that good layout structures could also be obtained by exploiting generic knowledge on typographic conventions [5]. This is the case of WISDOM++, which analyzes the layout in two steps: 1. A global analysis, in order to determine possible areas containing paragraphs, sections, columns, figures and tables. This step is based on an iterative process, in which the vertical and horizontal histograms of text blocks are alternately analyzed, in order to detect columns and sections/paragraphs, respectively. 2. A local analysis to group together blocks that possibly fall within the same area. Generic knowledge on west-style typesetting conventions is exploited to group blocks together, such as “the first line of a paragraph can be indented” and “in a justified text, the last line of a paragraph can be shorter than the previous one”. Experimental results proved the effectiveness of this knowledge-based approach on images of the first page of papers published in conference proceedings and journals [1]. However, performance degenerates when the system is tested on intermediate pages of multi-page articles, where the structure is much more variable, due to the presence of formulae, images, and drawings that can stretch over more than one column, or are quite close. The majority of errors made by the layout analysis module were in the global analysis step, while the local analysis step performed satisfactorily when the result of the global analysis was correct. In this paper, we investigate the possibility of supporting the user during the correction of the results of the global analysis. This is done by allowing the user to correct the results of the global analysis and then by learning rules for layout correction from his/her sequence of actions. This approach is different from those that learn the layout structure from scratch, since we try to correct the result of a global analysis returned by a bottom-up algorithm. Furthermore, we intend to capture knowledge on correcting actions performed by the user of the document image processing system. Other document processing systems allow users to correct the result of the layout analysis; nevertheless WISDOM++ is the only one that tries to learn correcting actions from user interaction with the system. Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003) 0-7695-1960-1/03 $17.00 © 2003 IEEE In the following section, we describe the layout correction operations. The automated generation of training examples is explained in Section 3. Section 4 introduces the learning strategy, while Section 5 presents some experimental results. 2. Correcting the layout Global analysis aims at determining the general layout structure of a page and operates on a tree-based representation of nested columns and sections. The levels of columns and sections are alternated (Figure 1), which means that a column contains sections, while a section contains columns. At the end of the global analysis, the user can only see the sections and columns that have been considered atomic, that is, not subject to further decomposition (Figure 2). The user can correct this result by means of three different operations: • Horizontal splitting: a column/section is cut horizontally. • Vertical splitting: a column/section is cut vertically. • Grouping: two sections/columns are merged together. The cut point in the two splitting operations is automatically determined by computing either the horizontal or the vertical histogram on the basic blocks returned by the segmentation algorithm. The horizontal (vertical) cut point corresponds to the largest gap between two consecutive bins in the horizontal (vertical) histogram. Therefore, splitting operations can be described by means of a unary function, split(X), where X represents the column/section to be split and the range is the set {horizontal, vertical, no_split}. The grouping operation, which can be described by means of a binary predicate group(A,B), is applicable to two sections (columns) A and B and returns a new section (column) C, whose boundary is determined as follows. Let (leftX, topX) and (bottomX, rightX) be the coordinates of the top-left and bottom-right vertices of a column/section X, respectively. Then: leftC= min(leftA, leftB), rightC=max(rightA,rightB), topC=min(topA,topB), bottomC=max(bottomA,bottomB). Grouping is possible only if the following two conditions are satisfied: 1. C does not overlap another section (column) in the document. 2. A and B are nested in the same column (section). After each splitting/grouping operation, WISDOM++ recomputes the result of the local analysis process, so that the user can immediately perceive the final effect of the requested correction and can decide whether to confirm the correction or not. 3. Representing corrections From the user interaction, WISDOM++ implicitly generates some training observations describing when and how the user intended to correct the result of the global analysis. These training observations are used to learn correction rules of the result of the global analysis, as explained in the next section. The simplest representation describes, for each training observation, the page layout at the i-th correction step and the correcting operation performed by the user on that layout. Therefore, if the user performs n-1 correcting operations, n observations are generated. The last one corresponds to the page layout accepted by the user. In the learning phase, this representation may lead the system to generate rules which strictly take into account the exact user correction sequence. However, several alternative correction sequences, which lead to the same result, may be also possible. If they are not considered, the learning strategy will suffer from data overfitting problems. This issue was already discussed in a preliminary work [9]. A more sophisticated representation, which takes into account alternative correction sequences, is based on the Column level