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Educational Data Mining and Learning Analytics: differences, similarities, and time evolution

Educational Data Mining and Learning Analytics: differences, similarities, and time evolution

There are two fields of research devoted to analyzing this data: Educational Data Mining (EDM) and Learning Analytics (LA). Their overwhelming popularity is almost certainly due to several factors: (a) there is interest in employing a data-driven approach to make better decisions, as it is usual in business intelligence or analytics (Daradoumis, Rodríguez-Ardura, Faulin, & Martínez-López, 2010b); (b) there are powerful statistical, machine-learning and data- mining methods and techniques to search for patterns in data and construct predictive models or decision rules that can be easily adapted to educational data; (c) generating data is relatively easy, and current computer capacity allows its storage and processing; (d) because of the financial crisis and fierce competition, universities are under pressure to reduce costs and increase income by exploiting the growing educational demands from developing countries, reducing dropout rates and improving course quality.
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Predictive Modelling Analytics through Data Mining

Predictive Modelling Analytics through Data Mining

Predictive analytics comprises of varied statistical trends and techniques ranging from machine learning and predictive modelling to data mining to efficiently analyze the historical data and information so as to process them to create predictions about the unknown future events [1][2]. As per the business aspect of predictive analytics, predictive analytics help in exploiting the patterns found in the historical business data to identify the risks and opportunities [3]. It captures the relationships between various factors to provide the assessment of risk or a potential threat and help guide the business through important decision making steps. Predictive analytics is sometimes described in reference to predictive modelling and forecasting. Predictive analytics is confined to the following three model that outlines the techniques for forecasting [26].
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PREDICTIVE ANALYTICS IN DATA MINING WITH BIG DATA: A LITERATURE SURVEY

PREDICTIVE ANALYTICS IN DATA MINING WITH BIG DATA: A LITERATURE SURVEY

John A. Keane [2] in 2013 proposed a framework in which big data applications can be developed. The framework consist of three stages (multiple data sources, data analysis and modelling, data organization and interpretation) and seven layers (visualisation/presentation layer, service/query/access layer, modelling/ statistical layer, processing layer, system layer, data layer/multi model) to divide big data application into blocks. The main motive of this paper is to manage and architect a massive amount of big data applications. The advantage of this paper is big data handles heterogeneous data and data sources in timely to get high performance and Framework Bridge the gap with business needs and technical realities. The disadvantage of this paper is too difficult to integrate existing data and systems. 2. Xin Luna Dong [5] in 2013 explained challenges of big data integration (schema mapping, record linkage and data fusion). These challenges are explained by using examples and techniques for data integration in addressing the new challenges raised by big data, includes volume and number of sources, velocity, variety and veracity. The advantage of this paper is identifying the data source problems to integrate existing data and systems. The disadvantage of this paper is big data integration such as integrating data from markets, integrating crowd sourcing data, providing an exploration tool for data sources. 3. Jun Wang [17] in 2013 proposed the Data-g Rouping-Aware (DRAW) data placement scheme to improve the problems like performance, efficiency, execution and latency. It could cluster many grouped data into a small number of nodes as compared to map reduce/hadoop. the three main phases of DRAW defined in this paper are: cluster the data-grouping matrix, learning data grouping information from system logs and recognizing the grouping data. The advantage
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Use Of Data Mining In Business Analytics To Support Business Competitiveness

Use Of Data Mining In Business Analytics To Support Business Competitiveness

he business world we work and operate in has changed dramatically over the past 20 years. With the computer and the Internet becoming ubiquitous tools in almost all business organizations now, the often-mentioned information age has really come of age. The capacity to collate data and present information efficiently in real time and the ability to make it readily accessible to everyone has been a major catalyst for many organizations to embrace globalization and improve productivity. For many consumers, they have benefited from the changing landscape that has evolved over the years in the business environment, in particular the new business models that have been introduced by many traditional companies as well as new companies. Consumers are now able to buy most products on the Internet. We use the hand-phones to conduct meetings, order tickets, and check stock prices, among a host of other tasks. Computers, information systems and telecommunications have been the vanguard for these new business models. But often, not many of us realize that although computers, information systems and communications are the basic building blocks of the information age, data is actually the primary driver for the information age. Without relevant data, the need for computers and communications would be much reduced.
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A Review on Big Data Analytics in Business

A Review on Big Data Analytics in Business

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.
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The Elements of Statistical Learning in Colon Cancer Datasets: Data Mining, Inference and Prediction

The Elements of Statistical Learning in Colon Cancer Datasets: Data Mining, Inference and Prediction

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to focus on the most important information in their data warehouses. Data mining is an iterative process with in which progress is defined by discovery through either automatic or manual methods[15]. Advances in Data Mining bring together the latest research in statistics, databases, machine learning, and artificial intelligence which are part of the rapidly growing field of Knowledge Discovery and Data Mining. It include fundamental issues, classification and clustering, trend and deviation analysis, dependency modelling, integrated discovery systems, next generation database systems, and application case studies[16]. Machine Learning is the study of methods for programming computers to learn a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software[17]. Machine Learning is its significant real-world applications, such as Speech recognition, Computer vision, Bio-surveillance, Robot control, Accelerating empirical sciences.[18]
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Statistical Data Analytics Of Football For. Beating The Odds

Statistical Data Analytics Of Football For. Beating The Odds

A bookmaker can be defined as anyone offering odds on the outcome of an event; however, a more appropriate definition would describe a bookmaker as “a person or a business that provide an odds market for one or more events, with prices available for all possible event outcomes, adjusted according to the demand of punters” (Buchdal, 2003). The bookmakers then manage and adjust the probabilities of the potential outcomes with the purpose of making a profit; this is technically referred as overround.

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Use of Log Data for Predictive Analytics through Data Mining
                 

Use of Log Data for Predictive Analytics through Data Mining  

Predictive analytics is the use of intelligence data for forecasting and modeling. It is a way to use predictive analysis data to predict future patterns. It is used widely in the business area such as insurance, medical and credit industries. Now using web log access data the process of predictive analysis can be used to improve architecture of web site so that any individual user can access web site very easily. These can be perform by administrator of that particular website by performing predictive analysis using some tools that works on web mining techniques. Using access data of the past, administrator are able to estimate the likelihood of future events and perhaps can make the availability of data. Data mining aids predictive analysis by providing a record of the past that can be analyzed and used to predict which access pattern is most likely to access later, can be followed later and services.Proper data mining techniques, algorithms and predictive modeling can cover the hidden pattern about website access and will allow tailoring ads to each online user as he or she navigates particular site.Predictive analytics can aid in choosing modeling methods more efficiently. In the best cases, predictive analytics can reduce the amount of money spent to provide the data by provide or sharing maximum bandwidth of the network.At its most effective,data mining can present data on demographics which may have been previously overlooked.
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Keywords: Interoperability, Standards, Learning Analytics, Educational Data Mining, Sharing Data Sets. 1. Introduction.

Keywords: Interoperability, Standards, Learning Analytics, Educational Data Mining, Sharing Data Sets. 1. Introduction.

By using tools and data that are already in place the community could benefit right away from the development of new knowledge and new designs. This is the basic idea behind the approach of reaping the low-hanging fruits. To extend the fruits metaphor, one should refrain from extensive pruning (e.g., changing the context or the system) until the gardener knows more about the trees and the garden. Within Learning Analytics and Educational Data Mining this may make sense, since it is difficult getting data out of information systems. However, the hunger for tasting the benefits of LA is great; the potential data sources are diverse; and the range of methods and experience is growing (Cooper, 2013b). By going for the low-hanging fruits we allow stakeholders time to argue their case for specific LA solutions before deciding on approaches with far-reaching implications.
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Data Analytics: Answering business questions with data

Data Analytics: Answering business questions with data

Correlation refers to any of a broad class of statistical relationships involving Correlation refers to any of a broad class of statistical relationships involving dependence. Dependence refers to any statistical relationship between two random variables or two sets of data.

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Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence

Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence

As seen from table 4, detection, identification and modeling of students’ learning behavior is a primary research objective. More specifically, the authors seek to identify learning strategies and when they occur, and model affective and metacognitive states (Abdous, He & Yen, 2012; Baker et al., 2008; Blikstein, 2011; Jeong & Biswas, 2008; Levy & Wilensky, 2011; Shih, Koedinger & Scheines, 2008). For example, Abdous, He and Yen (2012) and He (2013) tried to correlate interactions within a Live Video Streaming (LVS) environment to students’ final grades in order to predict their performance, discover behavior patterns in LVSs that lead to increased performance, and understand the ways students are engaged into online activities. In another case study, Blikstein (2011) logged automatically-generated data during programming activity in order to understand students’ trajectories and detect programming strategies within Open-Ended Learning Environments (OELEs). Furthermore, Shih, Koedinger and Scheines, (2008) used worked examples and logged response times to model the students’ time-spent in terms of “thinking about a hint” and “reflecting on a hint” for capturing behaviors that are related to reasoning and self- explanation during requesting hints within a CT environment. In another self-reasoning example, Jeong and Biswas (2008) tried to analyze students’ behavior based on the sequence of actions, and to infer learning strategies within a teachable agent environment.
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Dashboard using Data Analytics and Statistical          Modeling

Dashboard using Data Analytics and Statistical Modeling

In descriptive statistics, a box plot or boxplot is a con- venient way of graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending vertically from the boxes (whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box and- whisker diagram. Outliers may be plotted as individual points. Box plots are non-parametric: they display variation in samples of a sta- tistical population without making any assumptions of the underlying statistical distribution. The spacing‘s between he different parts of the box indicate the degree of dispersion (spread) and skewness in the data, and show outliers.[5,8]
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A Statistical Perspective on Data Mining

A Statistical Perspective on Data Mining

An oft-stated goal of data mining is the discovery of patterns and relationships among different variables in the database. This is no different from some of the goals of statistical inference: consider for instance, simple linear regression. Similarly, the pair-wise relation- ship between the products sold above can be nicely represented by means of an undirected weighted graph, with products as the nodes and weighted edges for the presence of the partic- ular product pair in as many transactions as proportional to the weights. While undirected graphs provide a graphical display, directed acyclic graphs are perhaps more interesting – they provide understanding of the phenomena driving the relationships between the variables. The nature of these relationships can be analyzed using classical and modern statistical tools such as regression, neural networks and so on. Section 3 illustrates this concept. Closely related to this notion of knowledge discovery is that of causal dependence models which can be studied using Bayesian belief networks. Heckerman (1996) introduces an example of a car that does not start and proceeds to develop the goal of finding the most likely cause for the malfunction as an illustration for this concept. The building blocks are elementary statistical tools such as Bayes’ theorem and conditional probability statements, but as we shall see in Section 3, the use of these concepts to make a pass at explaining causality is unique. Once again, the problem becomes more acute with large numbers of variables as in many complex systems or processes.
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Big data analytics and organisational change  The case of
learning analytics

Big data analytics and organisational change The case of learning analytics

One of the most prevalent unintended effects of the introduction of LA was the fact that LA data led to changes in work and working practices, as already signalled by critical researchers of education, whose arguments I have summarised in the background literature. Indeed, also in the case studied, for example, teaching staff found that “it [LA] detracts from the job of educating” (I_Teaching_011) and introduces a host of different data-related activities which ultimately take away time they would otherwise spend teaching or interacting with students. Importantly, a number of interviewees have experienced what they called “the move towards e-learning” (I_Teaching_011), that is, an impression or encouragement they received that e- learning elements should be introduced even in face-to-face teaching, with some residential modules introducing two or three weeks of online classes with an explicit connection to “the move towards using the data that you get from e-learning” (I_Teaching_011). While it could be argued that the move towards e-learning can have other causes, such as savings, resourcing, and the immense profitability of distance learning programmes, the conviction with which some interviewees expressed their view that they were being almost forced to introduce distance learning components in their face-to-face modules seems to confirm the attribution of these changes to the LA system: “Maybe the data can strengthen them more to having more like more online programmes. Or also to have the campus based programmes to move closer to the distance learning approaches, I guess” (I_Academic_007). It has been pointed out that “The university seems to have become a lot more open to online learning as a way of engaging students, not as a way of just disseminating information. And I feel that part of that is to do with the ability to monitor the analytics and understand the students better” (I_Teaching_002 Follow-up). One interviewee in particular, puzzled as to why she was asked to introduce a few weeks of distance learning into her residential course, arrived at the conclusion that it was due to the trackability and traceability of online actions as opposed to classroom activity.
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Cyber risk prediction through social media big data analytics and statistical machine learning

Cyber risk prediction through social media big data analytics and statistical machine learning

Scholars have been notified that cyber risk prediction can be performed through SML, which is a discipline that synergizes the fields of mathematics, statistics, and computer science [23–26]. The purpose of SML is to create an algorithm that not only “learns” from the data but compiles stochastic models that can generate predictions and deci- sions [27]. Regarding data analysis, there are two different cultural approaches: the data modeling culture (traditional statistics and econometrics) and the algorithmic modeling culture (machine learning) [28]. The data modeling approach, used by 98% of academic statisticians, makes conclusions about the data model, instead of the problem/phenom- enon. This approach often yields dubious results, since assumptions are frequently made without evaluating the model itself. Moreover, validation is generally performed by con- ducting goodness-of-fit testing and residual examinations. Conversely, the algorithmic modeling approach is used by 2% of academic statisticians, yet it is commonly used by computer scientists and industrial statisticians. This approach can be applied for large and complex data analyses as well as smaller data analyses. In this case, validation is usu- ally performed by measuring the accuracy rate of the model(s). According to Breiman, the statistical community is too committed to the exclusive use of the data modeling approach, which, in turn, prevents statisticians from solving actual interesting problems. As the saying goes, “If all a man has is a hammer, then every problem looks like a nail.” [28].
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Closing the Loop of Big Data Analytics: the Case of Learning Analytics

Closing the Loop of Big Data Analytics: the Case of Learning Analytics

The concern about reverse engineering related to LA has been expressed well by one of our interview- ees: “’I want to do well, I want to get a first. I’d put more work into these or I’ll set…’ you know, they’ll set themselves up in a way [while] one of the virtues of the degree is that it is a sort of very rounded measure of accomplishments, but the moment we start giving really quite high-resolution da- ta…”, exemplifying the fact that students may then lose focus from the degree and its value and start studying to the data. A similar idea was expressed by another interviewee who stated “the more we add data analytics […] the more we’re instrumentalising their understanding of learning, you know, it’s a box to tick, it isn’t a concept or an idea to engage with, to understand, it’s a ‘what do I need to do to get my 2:1 at the end of it and this is going to help me’”. However, it is also interesting to note how staff themselves plan to increase the amounts of LA data on their modules by reverse engineering stu- dent activity and motivating students to get onto the platform: “we’ll be giving certificates out to peo- ple who achieved completion of at least 75% and suddenly […] all the numbers were like ‘vroom’”. Similarly, staff would reach out to students showing as low activity on the system to get their activity higher, or even display LA statistics in lecture rooms to encourage students to view and comment more frequently. These interventions are symptomatic of thinking how the LA data can show more interaction and engagement, and taking appropriate steps aimed at increasing the numbers in the LA system, which is a clear example of reverse engineering.
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A Novel Approach to Enhance Teaching and Learning Through Mining and Learning Analytics

A Novel Approach to Enhance Teaching and Learning Through Mining and Learning Analytics

The data set has been pre-processed; The K-Means and DBSCAN algorithms are applied over the data to partioning the data into (K = 5) different clusters. The Random initialization method and the ten as the initial seed value are used to form the clusters. In K-Means, the Euclidean Distance and in DBSCAN, the Manhattan Distance metrics are used to measure distance between of an observation and the initial cluster centroids.

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Machine Learning for Big Data Analytics

Machine Learning for Big Data Analytics

As a result of the huge data explosion, largely due to the widespread presence of the Internet, there is an exigency to automate large-scale data analytics. Big data analytics processes diverse data from various distributed data source producing complete data set.Big Data technologies present a new rank of technologies and architectures, constructed so organizations can prudentlyextract value from voluminous and disparate data by high-velocity seizure, discovery or analysis. Advances in Machine Learning hitherto, have tackled this need by exploiting statistical methods that learn from data. Some methods such as Supervised and Unsupervised Learning can handle problems such as classification and clustering. Given their manifold applications, these Machine Learning techniques attract a large audience in computing. Researchers in Computer Security employ these techniques to discern peculiarbehaviour in streaming data. In the field of Green Computing and Smart Energy, Machine Learning approach is employed to grasp energy usage patterns and correspond them with real-time demands. Computational Biologists are applying Machine Learning for time-series data design to unravel the puzzle, that is, the human genome. Intricate graphical frameworks are being adopted by Linguists to unearth syntactic structures in written languages and words. Also outside the realm of Computer Science, kernel process and Bayesian predictions have aided financial analysts to fruitful proprietary trading strategies, and astronomers to cluster stars. Most of these applications have credence on years of human cognizance transfer amongst developments, and different empirical refinements to the designs, to equip Machine Learning techniques so that they work in any particular sphere.
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Applied Data Mining Statistical Methods for Business and Industry Giudici P (2003) pdf

Applied Data Mining Statistical Methods for Business and Industry Giudici P (2003) pdf

only in a limited area of the space related to the input variable, as in nearest- neighbour methods. This allows a better separation of the input information and generally a faster learning speed. Support vector machines are a powerful alterna- tive to multilayer perceptrons. The classification rules determined by multilayer perceptrons find a non-linear hyperplane separating the observations, assuming the classes are perfectly separable, but support vector machines generalise this to more complex observation spaces by allowing variable transformations to be per- formed. Support vector machines optimise the location of the decision boundary between classes. Research is still in progress on these methods. For more details consult Vapnik (1995, 1998). Recently researchers have developed complex sta- tistical models that closely resemble neural networks, but with a more statistical structure. Examples are projection pursuit models, generalised additive models and MARS models (multivariate adaptive regression splines). They are reviewed in Cheng and Titterington (1994) and Hastie, Tibshirani and Friedman (2001). Nearest-neighbour models provide a rather flexible predictive algorithm using memory-based reasoning. Instead of fitting a global model, they fit a local model for the neighbourhood of the observation that is the prediction target. They are related to the descriptive methodology of kernel methods (Section 5.2).
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Sentiment Analysis  A tool for Data Mining in Big Data Analytics

Sentiment Analysis A tool for Data Mining in Big Data Analytics

Sentiments are feelings, emotions, opinions, likes and dislikes whereas sentiment analysis is the key to understanding verbal and written communication that represents several mixed opinions about varied topics. Sentiment Analysis can be interpreted as the task of detecting the perspective of various authors on particular datasets [10]. It raises the question of importance of opinions shared by those who actually use the product or service offered. It is imperative for those who are looking for opinions or social media acceptance. Sentiment analysis frameworks are being connected in pretty much every business and social space since judgements are fundamental to every human pursuit and are key influencers of our practices. Our convictions and view of the real world, and the decisions we make, are to a great extent molded on how others see and assess the world [11]. The phrase text mining has generally been related with business applications, data examination is currently giving noteworthy bits of knowledge to numerous portions of the business, government, service agencies and even political exercises [16]. Rising complains regarding customer care in banking institutions go unheard and the gap between the management and the end users goes on increasing. Sentiment analysis can be the solution that gets the two parties back on
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