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Please read the question and attached pdf first and then start work on my question and dont forget to add referenceData mining is the exploration and analysis of large data to discover meaningful patterns and rules. Data mining aims to predict future outcomes. Additionally, data mining techniques are used to build machine learning (ML) models that power modern artificial intelligence (AI) applications such as search engine algorithms and recommendation systems.Benefits of Data Mining:Automated Decision-MakingAccurate Prediction and ForecastingCost ReductionCustomer InsightsWhile a powerful process, data mining is hindered by the increasing quantity and complexity of big data. Where exabytes of data are collected by firms every day, decision-makers need ways to extract, analyze, and gain insight from their abundant repository of data.Data mining has two primary processes: supervised and unsupervised learning. Data mining software is a tool to convert raw and unstructured data into useful information in order to optimize the decision making ability. This software offers enterprises an ability of predictive analysis which helps them forecasting marketing strategy and consumers behavior. Steps involved in data mining include data collection, data processing and then software sort the data depending on user’s result in the form of graph or table.To do:Read the data mining paperWatch the two insightful videosParticipate early and often in the discussion forum!As always, please reach out with any questions/concerns/issues.There is much discussion regarding Data Analytics and Data Mining. Sometimes these terms are used synonymously but there is a difference. What is the difference between Data Analytics vs Data Mining? Please provide an example of how each is used. Also explain how you may use data analytics and data mining in a future career. Lastly, be sure to utilize at least one scholarly source from either the UC library or Google Scholar. Please make your initial post and two response posts substantive. A substantive post will do at least TWO of the following:Ask an interesting, thoughtful question pertaining to the topicAnswer a question (in detail) posted by another student or the instructorProvide extensive additional information on the topicExplain, define, or analyze the topic in detailShare an applicable personal experienceProvide an outside source (for example, an article from the UC Library) that applies to the topic, along with additional information about the topic or the source (please cite properly in APA)Make an argument concerning the topic.At least one scholarly source should be used in the initial discussion thread. Be sure to use information from your readings and other sources from the UC Library. Use proper citations and references in your post. big_data_analytics_vs_educational_data_mining.pdf Unformatted Attachment Preview Learning Analytics or Educational Data Mining? This is the Question... Daniela Marcu Ștefan cel Mare University of Suceava Str. Universității 13, Suceava 720229 Phone: 0230 216 147 mdaniela.marcu@yahoo.ro Mirela Danubianu Ștefan cel Mare University of Suceava Str. Universității 13, Suceava 720229 Phone: 0230 216 147 mdanub@eed.usv.ro Abstract In full expansion, a vital area such as education could not remain indifferent to the use of information and communication technology. Over the past two decades we have witnessed the emergence and development of e-learning systems, the proliferation of MOOCs, and generally the rise of Technology Enhanced Education. All of these contributed to generation and storage of unprecedented volumes of data concerning all areas of learning. At the same time, domains such as data mining and big data analytics have emerged and developed. Their applications in education have spawned new areas of research such as educational data mining or learning analytics. As an interdisciplinary research area Educational Data Mining (EDM) aims to explore data from educational environment to build models based on which students behavior and results are better understood. In fact, EDM is a complex process that consists of a few steps grouped in three stages: data preprocessing, modelling and postprocessing. It transforms raw data from educational environments in useful information that could influence in a positive way the educational process. According to Society for Learning Analytics Research (SoLAR) which took over the wording of the first International Conference on Learning Analytics and Knowledge, learning analytics is ”the measurement, collection, analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occurs” (Siemens, 2011). This paper proposes a comparative study of the two concepts: EDM and learning analytics. Due to certain voices in the scientific environment that claim that the two terms refer to the same thing, we want to emphasize the similarities and differences between them, and how each one can serve to raise the quality in educational processes. Keywords : EDM; LA; Data Mining; Education. 1. Introduction The educational community has an interest in the great potential of education. Why are researchers so enthusiastic about this? The answer is simple. Seeing the impact of applying data mining to exploiting large data volumes and analyzing data from areas such as the business environment, social media, and other scientific areas, we can think of the benefits for the education system. If we could adapt the methods of finding models in the data, used for analyzing the online activity of clients and social media users for the educational environment, we could get closer evidence of reality on the activities of the training system. The widespread use of computer-based pre-university learning, the development of Webbased courses, are additional reasons for EDM and LA research. Designing educational policies based on practical evidence provided by researchers can bring benefits to the educational system. 1 BRAIN – Broad Research in Artificial Intelligence and Neuroscience Volume 10, Special Issue 2 (October, 2019), ISSN 2067-3957 The exploitation of large volumes of data from different domains is done using specific techniques and methods. It helps to develop tools to facilitate progress in these areas. The science of extracting useful information from large volumes of data is called Data Mining (DM) (Hand, Mannila & Smyth, 2001). The concept is based on three key areas: statistics, artificial intelligence and machine learning (Figure 1). Figure 1. Data Mining Initially, DM used statistical algorithms. Specific techniques such as decision trees, association rules, clustering, artificial neural networks, and others have been developed (Șușnea, 2012). Applying exploitation methods for educational system data to build models to better understand students behavior and outcomes is named Educational Data Mining (EDM). Since data and education issues are different from those in other areas, classical DM methods have been improved and supplemented with EDM specific methods (Romero & Ventura, 2007). According to some authors, there are four areas of application of EDM aimed at: improving student modeling and domain modeling, e-learning and scientific research (Baker, 2012). In order to better understand learning, data from pupils and from the educational environment is measured, collected and analyzed. This is the learning analysis and is a related field of EDM. Among the Learning Analytics (LA) methods we can list:  content analysis  discourse analysis  analyzing the social dimension of learning (Ferguson & Buckingham Shum, 2012). In the following sections we propose to detail relevant aspects about EDM and LA in order to provide viable arguments in a comparative study of the two concepts. 2. Educational Data Mining Over the past 10 years, the field of research aimed to exploit the unique types of data from education has developed quite internationally. In 2011, in Massachusetts USA, the International EDM Working Group (established in 2007) created the International Society for EDM (online: http://educationaldatamining.org/about/). Romania is, however, at a pioneering stage in EDM. There is currently a growing interest in using computers in learning and Web-based training. With the rapid increase in the volume of learning software resources, the Romanian educational system also accumulates huge amounts of data from students, teachers, parents, libraries, secretariats, etc. Getting the information needed to build models to improve the quality of managerial decisions becomes one of the greatest challenges of the present. Traditional research in the field of education is time-consuming and often non-ecological through the waste of material resources. Developing an experimental study, such as combating school absenteeism, involves firstly the selection of schools, teachers and pupils. It follows the definition of strategies that lead to the identification of sources of school stress, increasing the 2 D. Marcu, M. Danubianu - Learning Analytics or Educational Data Mining? This is the Question... motivation of students to attend classes, trust in school, family, and so on. However, the studies depend on context, class, geography, economic development, teacher-student relationships. Changing any parameter can lead to very different conclusions. Soon there may be new factors that could not be taken into consideration earlier in the demotivation of students towards school. Making traditional new studies for this topic involves the use of important temporal resources. By comparison, EDM proves to be more efficient. The analysis of existing data in the educational system through the use of specific EDM methods allows the identification of new models for new contexts. An enormous advantage is that the same methods can be applied to different data generating specific results without the need for new analysis strategies. More specifically, lets take the example of a course designed for web-based training (Romero, Ventura, De Bra, 2004). Traditionally, evaluating the effectiveness of a course is done by analyzing the results obtained by the student upon completion of the course, which does not necessarily lead to the improvement of the material or methods and teaching tools used for the future course versions. In fact, in the Romanian pre-university system, the updating of educational programs and educational resources does not present the periodicity expected by the society. What would it be like the knowledge of EDM data exploitation? EDM methods aim at discovering correlation rules between course components (content, questions, various activities) and student activities. In the Knowledge Discovery with Genetic Programming for providing feedback to the courseware author, C. Romero, S. Ventura and P. Bra describe the four main steps in building a software based on EDM (Romero, Ventura, De Bra, 2004): development, use, discovering knowledge, improving Other classification has three stages: preprocessing, data exploitation and post processing [3]. The cycle of these steps is illustrated in Figure 2. Figure 2. Stages of the process of converting data into information If we refer again to the analysis of the efficiency of a course, in the first stage, the preprocessing is performed various operations such as:  the teacher creates the content and provides information on pedagogical and methodological aspects  the teacher creates course support  the student uses the course  the EDM software records information about: the students time spent in the course, the sections visited, the scores obtained and other interactions  the information collected is converted into data with a format appropriate for processing. In the next step, EDM-specific algorithms are applied to obtain different correlation rules. The models will provide information in different formats for analysis: numerical results of the coefficients, tables, diagrams, correlation matrices (an example is illustrated in Appendix 1 Correlation matrix obtained with the DataLab application based on the results of the Olympiad of computer science). One of the most important rules for discovering knowledge is if-else. Several such rules can be defined in EDM: Association, Classification and Prediction (Klosgen & Zytkow, 2002). 3 BRAIN – Broad Research in Artificial Intelligence and Neuroscience Volume 10, Special Issue 2 (October, 2019), ISSN 2067-3957 The teacher will analyze the results of the analyzes and study the degree of achievement of the initial goals. Depending on the conclusions, it may take the decision to improve the course and resume its evaluation process. This may prove to be a difficult process because opinions can differ significantly from one teacher to another in relation to the material and the way of interaction with the student the course offers. 3. Methods of data exploitation There are currently a wide variety of methods of exploiting data in the education system. These can be categorized into two broad categories according to the ways to achieve the objectives:  predictive: Prediction, Classification, Regression, Outlier Detecting  descriptive: Clustering, Determination of association rules, Discovery of data for human judgment (Sasu, 2014). Many of these are general DM methods: prediction, classification, grouping, exploitation of texts and others. But there are also specific EDM methods such as nonnegative matrix factorization and Knowledge tracing (KT) (Romero & Ventura, 2012). Here are some of these: Prediction The method can be used in education to predict students behavior and outcomes. It is based on the creation of predictive models. In the training phase, they learn to make predictions about a set of variables called predictors by analyzing them in combination with other variables. Once the enrollment phase is completed, the patterns can be applied to the data sets for which the prediction is to be applied. It is known the study by Baker, Gowda, Corbett - Automatically detecting the students preparation for future learning: help use is key (Baker, Gowda & Corbett, 2011). The authors create a tool for automatically predicting a students future performance on the basis of establishing positive or negative correlations between various features such as: student test results, time spent in response, time elapsed between receiving a clue and typing the answer, and others. It is experienced on a group of students, and then applied to another group. The results are then compared to those obtained using the Bayesian Knowledge Tracing (BKT) model. Classification The method involves building a predictive model. The data in the training set is characterized by certain attributes. The model must identify belonging to a class based on the set of attributes. Suppose we built an educational software as an interactive game for a given theme. Based on user attributes such as age, gender, geographic area, duration until the game is completed, number of attempts we can build a classifier, and determine the users belonging to a specific class. The model will learn to identify students. The analyzes can provide information on the need to use this educational method for certain age groups, interests and education. Methods that use the classification are: decision trees, neural networks, bayesian classifications, and others. Clustering The method involves building patterns that identify data clustering after certain similarities. For the model to provide quality predictions, the similarities inside class must be maximized and similarities between classes minimized. The use of this method in Romanian high school education could aim at grouping pupils according to the pupils learning style (auditory, visual, practical - kinesthesis) based on the analysis of behavior in relation to certain educational products and pupils characteristics. The prediction of such a model could lead to an effective recommendation of how to learn educational content. Thus, the instructional process could be carried out efficiently in relation to the learning particularities of each student. At present, there is an attempt to unfold the lessons in a way appropriate to the 4 D. Marcu, M. Danubianu - Learning Analytics or Educational Data Mining? This is the Question... students learning styles, but the reality is that identifying learning styles is superficial. The results of the questionnaires are attached to the class catalog, but this does not lead, in most cases, to the improve teaching methods and techniques used in the lesson. In the absence of clear alternatives, the teacher has to improvise. The method is successfully used in the detection of plagiarism (Text Mining) and is also applied in the educational sphere. Outlier Detection The method involves creating patterns that detect data that have different features than others. In Romanian education, this method could be used to detect students with content assimilation problems, or those with aberrant behavior. In general, not only one EDM method is used in case studies. Outlier Detection methods can be used, for example, with data clustering techniques and decision tree classification as presented in the study by Ajith, Sai and Tejaswi (2013) - Evaluation of student performance: an outlier detection perspective (Ajith, Sai & Tejaswi, 2013). The study aims to identify learners with special learning needs to reduce the school failure rate. Input data are collected from: participation in student lessons, tests, notes on initial tests. In order to achieve the proposed objective, they try to find models for classifying students who will be helpful in setting up study groups. At present, in Romania, students in the high school education of state do not have the opportunity to trace the course matter in other groups than the classes they belong to. Moreover, pupils diagnosed as having special educational needs participate in classes with other colleagues. The teachers create for them specially programs. Then the courses are held by under the guidance of a single teacher who does not have any pedagogical and methodical experience related to the learning situation! There are special requirements for conducting the educational process. This based on grouping students within the same educational space within the same timeframe to go through different course materials. In the absence of a proper classification, alternative methods and means, and teachers with such experience, things happen more or less in a manner that leads to the best results. Discovery with Models Discovery with Models is the fifth category presented in Bakers Taxonomy (Baker, 2012). It is also one of the most widely used methods of data exploitation in the field of education. It is based on the use of a previously validated model as a component in analyzes that use prediction or exploitation of relationships in new contexts (Baker & Yacef, 2009). In this way information on educational materials that contribute most to educational progress can be obtained. A study carried out by Beck and Mostow in 2008 - How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students (Beck & Mostow, 2008) - on the analysis of different types of learners demonstrates that the method supports identifying relationships between student behavior and characteristics of variables used. Nonnegative Matrix Factorization (or Decomposition) There are several algorithms used for factoring the nonnegative matrix. This transforms (decomposes, factorizes) a matrix V into two W and H matrices with the property that they all have non-negative elements. This is very useful in applications such as determining the effectiveness of an evaluation system in which matrices contain elements related to: exams, abilities, and items. Matrix V is obtained from the product of the two smaller matrices as can be seen in Figure 3. (Non-negative matrix factorization, 2019). Figure 3. Illustration of approximate non-negative matrix factorization. Source: wikipedia.org 5 BRAIN – Broad Research in Artificial Intelligence and Neuroscience Volume 10, Special Issue 2 (October, 2019), ISSN 2067-3957 X H students 1 1 1 0 1 1 0 0 1 1 0 0 ≈ items W skills 0 1 1 0 1 0 1 1 skills Items We propose to study the evaluation of two specific abilities defined on the columns of the matrix W for 4 work requirements (items), defined in the W matrix on the four lines. Matrix H will contain two lines representing the two abilities and 6 columns representing the assessed students. The result will be recorded in Matrix V that has 4 lines for each of the 4 items and 6 columns for each of the 6 students. A value of 1 in the W matrix indicates the need for a certain skill (Figure 4) (Desmarais, 2012). 0 1 1 1 0 1 1 1 V students 1 1 1 0 1 0 2 1 0 1 1 1 0 1 1 1 Figure 4. Non-negative matrix factorization - example The first item requires the ability 2, W [1] [2] = 1. Only the 2 and 3 students have the ability 2, so item 1 will not be promoted by students 1, 2, 4 and 5. To promote Item 4 both skills are required. Only one of the candidates will promote this item with the maximum score. Using computerized analysis methods, interpretations can be obtained in a much shorter time and with great accuracy because machines are faster and more accurate than humans. 4. Learning Analysis (LA) Learning is the product of an interaction between learners and the learning environment, between among students / educators / teachers and others (Elias & Lias, 2011). The evaluation of learning, in the traditional sense, is based on the evaluation of student / pupil outcomes. This involves assessing knowledge but also trying to answer questions such as: how well this student needs, how can be improved, how to change the course interface to make it more accessible. At present, especially in the pre-university system, learning evaluation is based on questionnaires. Obtaining feed-back is lasting because the non-automatic data processing takes time and the analysis possibilities are quite limited. The desire to improve the quality of learning and assessment in the educational system is increasing at the international level, but also in our country. Traditional systems are confronted by huge amounts of data and their diversity. Learning Analytics (LA) attempts to answer questions about how this data can be used and how it can be transformed and analyzed to provide useful information that can give value to the learning process (Liu & Fan, 2014). In 2011, at the first International Conference on Learning A ... Purchase answer to see full attachment