Data mining

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Data_mining 

 

Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

Neurohacking is a subclass of biohacking, focused specifically on the brain. Neurohackers seek to better themselves or others by “hacking the brain” to improve reflexes, learn faster, or treat psychological disorders. The modern neurohacking movement has been around since the 1980s. However, herbal supplements have been used to increase brain function for hundreds of years. After a brief period marked by a lack of research in the area, neurohacking started regaining interest in the early 2000s. Currently, most neurohacking is performed via do-it-yourself (DIY) methods by in-home users.

Simple uses of neurohacking include the use of chemical supplements to increase brain function. More complex medical devices can be implanted to treat psychological disorders and illnesses.

The term "data mining" is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics—or, when referring to actual methods, artificial intelligence and machine learning—are more appropriate.

The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.

The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data; in contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.

The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

Etymology

In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "data mining" was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies in 1983. Lovell indicates that the practice "masquerades under a variety of aliases, ranging from "experimentation" (positive) to "fishing" or "snooping" (negative).

The term data mining appeared around 1990 in the database community, generally with positive connotations. For a short time in 1980s, a phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego-based company, to pitch their Database Mining Workstation; researchers consequently turned to data mining. Other terms used include data archaeology, information harvesting, information discovery, knowledge extraction, etc. Gregory Piatetsky-Shapiro coined the term "knowledge discovery in databases" for the first workshop on the same topic (KDD-1989) and this term became more popular in AI and machine learning community. However, the term data mining became more popular in the business and press communities. Currently, the terms data mining and knowledge discovery are used interchangeably.

In the academic community, the major forums for research started in 1995 when the First International Conference on Data Mining and Knowledge Discovery (KDD-95) was started in Montreal under AAAI sponsorship. It was co-chaired by Usama Fayyad and Ramasamy Uthurusamy. A year later, in 1996, Usama Fayyad launched the journal by Kluwer called Data Mining and Knowledge Discovery as its founding editor-in-chief. Later he started the SIGKDD Newsletter SIGKDD Explorations. The KDD International conference became the primary highest quality conference in data mining with an acceptance rate of research paper submissions below 18%. The journal Data Mining and Knowledge Discovery is the primary research journal of the field.

Background

The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, specially in the field of machine learning, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees and decision rules (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns. in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.

Process

The knowledge discovery in databases (KDD) process is commonly defined with the stages:

1. Selection
2. Pre-processing
3. Transformation
4. Data mining
5. Interpretation/evaluation.

It exists, however, in many variations on this theme, such as the Cross-industry standard process for data mining (CRISP-DM) which defines six phases:

1. Business understanding
2. Data understanding
3. Data preparation
4. Modeling
5. Evaluation
6. Deployment

or a simplified process such as (1) Pre-processing, (2) Data Mining, and (3) Results Validation.

Polls conducted in 2002, 2004, 2007 and 2014 show that the CRISP-DM methodology is the leading methodology used by data miners. The only other data mining standard named in these polls was SEMMA. However, 3–4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models, and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.

Pre-processing

Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.

Data mining

Data mining involves six common classes of tasks:

• Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
• Association rule learning (dependency modeling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
• Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
• Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".
• Regression – attempts to find a function that models the data with the least error that is, for estimating the relationships among data or datasets.
• Summarization – providing a more compact representation of the data set, including visualization and report generation.

Results validation

An example of data produced by data dredging through a bot operated by statistician Tyler Vigen, apparently showing a close link between the best word winning a spelling bee competition and the number of people in the United States killed by venomous spiders. The similarity in trends is obviously a coincidence.

Data mining can unintentionally be misused, and can then produce results that appear to be significant; but which do not actually predict future behavior and cannot be reproduced on a new sample of data and bear little use. Often this results from investigating too many hypotheses and not performing proper statistical hypothesis testing. A simple version of this problem in machine learning is known as overfitting, but the same problem can arise at different phases of the process and thus a train/test split—when applicable at all—may not be sufficient to prevent this from happening.

The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by data mining algorithms are necessarily valid. It is common for data mining algorithms to find patterns in the training set which are not present in the general data set. This is called overfitting. To overcome this, the evaluation uses a test set of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set, and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish "spam" from "legitimate" emails would be trained on a training set of sample e-mails. Once trained, the learned patterns would be applied to the test set of e-mails on which it had not been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify. Several statistical methods may be used to evaluate the algorithm, such as ROC curves.

If the learned patterns do not meet the desired standards, subsequently it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge.

Research

The premier professional body in the field is the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining (SIGKDD). Since 1989, this ACM SIG has hosted an annual international conference and published its proceedings, and since 1999 it has published a biannual academic journal titled "SIGKDD Explorations".

Computer science conferences on data mining include:

• CIKM Conference – ACM Conference on Information and Knowledge Management
• European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
• KDD Conference – ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Data mining topics are also present on many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases

Standards

There have been some efforts to define standards for the data mining process, for example, the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006 but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.

For exchanging the extracted models—in particular for use in predictive analytics—the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover (for example) subspace clustering have been proposed independently of the DMG.

Notable uses

Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance.

Privacy concerns and ethics

While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to peoples' behavior (ethical and otherwise).

The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.

Data mining requires data preparation which uncovers information or patterns which compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation involves combining data together (possibly from various sources) in a way that facilitates analysis (but that also might make identification of private, individual-level data deducible or otherwise apparent). This is not data mining per se, but a result of the preparation of data before—and for the purposes of—the analysis. The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous.

It is recommended to be aware of the following before data are collected:

• The purpose of the data collection and any (known) data mining projects;
• How the data will be used;
• Who will be able to mine the data and use the data and their derivatives;
• The status of security surrounding access to the data;
• How collected data can be updated.

Data may also be modified so as to become anonymous, so that individuals may not readily be identified. However, even "anonymized" data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.

The inadvertent revelation of personally identifiable information leading to the provider violates Fair Information Practices. This indiscretion can cause financial, emotional, or bodily harm to the indicated individual. In one instance of privacy violation, the patrons of Walgreens filed a lawsuit against the company in 2011 for selling prescription information to data mining companies who in turn provided the data to pharmaceutical companies.

Situation in Europe

Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers. However, the U.S.–E.U. Safe Harbor Principles, developed between 1998 and 2000, currently effectively expose European users to privacy exploitation by U.S. companies. As a consequence of Edward Snowden's global surveillance disclosure, there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to the National Security Agency, and attempts to reach an agreement with the United States have failed.

In the United Kingdom in particular there have been cases of corporations using data mining as a way to target certain groups of customers forcing them to pay unfairly high prices. These groups tend to be people of lower socio-economic status who are not savvy to the ways they can be exploited in digital market places.

Situation in the United States

In the United States, privacy concerns have been addressed by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA). The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses. According to an article in Biotech Business Week, "'[i]n practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,' says the AAHC. More importantly, the rule's goal of protection through informed consent is approach a level of incomprehensibility to average individuals." This underscores the necessity for data anonymity in data aggregation and mining practices.

U.S. information privacy legislation such as HIPAA and the Family Educational Rights and Privacy Act (FERPA) applies only to the specific areas that each such law addresses. The use of data mining by the majority of businesses in the U.S. is not controlled by any legislation.

Copyright law

Situation in Europe

Under European copyright and database laws, the mining of in-copyright works (such as by web mining) without the permission of the copyright owner is not legal. Where a database is pure data in Europe, it may be that there is no copyright—but database rights may exist so data mining becomes subject to intellectual property owners' rights that are protected by the Database Directive. On the recommendation of the Hargreaves review, this led to the UK government to amend its copyright law in 2014 to allow content mining as a limitation and exception. The UK was the second country in the world to do so after Japan, which introduced an exception in 2009 for data mining. However, due to the restriction of the Information Society Directive (2001), the UK exception only allows content mining for non-commercial purposes. UK copyright law also does not allow this provision to be overridden by contractual terms and conditions.

The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe. The focus on the solution to this legal issue, such as licensing rather than limitations and exceptions, led to representatives of universities, researchers, libraries, civil society groups and open access publishers to leave the stakeholder dialogue in May 2013.

Situation in the United States

US copyright law, and in particular its provision for fair use, upholds the legality of content mining in America, and other fair use countries such as Israel, Taiwan and South Korea. As content mining is transformative, that is it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the Google Book settlement the presiding judge on the case ruled that Google's digitization project of in-copyright books was lawful, in part because of the transformative uses that the digitization project displayed—one being text and data mining.

Software

Free open-source data mining software and applications

The following applications are available under free/open-source licenses. Public access to application source code is also available.

• Carrot2: Text and search results clustering framework.
• Chemicalize.org: A chemical structure miner and web search engine.
• ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language.
• GATE: a natural language processing and language engineering tool.
• KNIME: The Konstanz Information Miner, a user-friendly and comprehensive data analytics framework.
• Massive Online Analysis (MOA): a real-time big data stream mining with concept drift tool in the Java programming language.
• MEPX - cross-platform tool for regression and classification problems based on a Genetic Programming variant.
• ML-Flex: A software package that enables users to integrate with third-party machine-learning packages written in any programming language, execute classification analyses in parallel across multiple computing nodes, and produce HTML reports of classification results.
• mlpack: a collection of ready-to-use machine learning algorithms written in the C++ language.
• NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python language.
• OpenNN: Open neural networks library.
• Orange: A component-based data mining and machine learning software suite written in the Python language.
• R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project.
• scikit-learn is an open-source machine learning library for the Python programming language
• Torch: An open-source deep learning library for the Lua programming language and scientific computing framework with wide support for machine learning algorithms.
• UIMA: The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video – originally developed by IBM.
• Weka: A suite of machine learning software applications written in the Java programming language.

Proprietary data-mining software and applications

The following applications are available under proprietary licenses.

• Angoss KnowledgeSTUDIO: data mining tool
• LIONsolver: an integrated software application for data mining, business intelligence, and modeling that implements the Learning and Intelligent OptimizatioN (LION) approach.
• Megaputer Intelligence: data and text mining software is called PolyAnalyst.
• Microsoft Analysis Services: data mining software provided by Microsoft.
• NetOwl: suite of multilingual text and entity analytics products that enable data mining.
• Oracle Data Mining: data mining software by Oracle Corporation.
• PSeven: platform for automation of engineering simulation and analysis, multidisciplinary optimization and data mining provided by DATADVANCE.
• Qlucore Omics Explorer: data mining software.
• RapidMiner: An environment for machine learning and data mining experiments.
• SAS Enterprise Miner: data mining software provided by the SAS Institute.
• SPSS Modeler: data mining software provided by IBM.
• STATISTICA Data Miner: data mining software provided by StatSoft.
• Tanagra: Visualisation-oriented data mining software, also for teaching.
• Vertica: data mining software provided by Hewlett-Packard.
• Google Cloud Platform: automated custom ML models managed by Google.
• Amazon SageMaker: managed service provided by Amazon for creating & productionising custom ML models.

 

Educational data mining

From Wikipedia, the free encyclopedia

Educational data mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems). At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings. In doing so, EDM has contributed to theories of learning investigated by researchers in educational psychology and the learning sciences. The field is closely tied to that of learning analytics, and the two have been compared and contrasted.

Definition

Educational data mining refers to techniques, tools, and research designed for automatically extracting meaning from large repositories of data generated by or related to people's learning activities in educational settings. Quite often, this data is extensive, fine-grained, and precise. For example, several learning management systems (LMSs) track information such as when each student accessed each learning object, how many times they accessed it, and how many minutes the learning object was displayed on the user's computer screen. As another example, intelligent tutoring systems record data every time a learner submits a solution to a problem. They may collect the time of the submission, whether or not the solution matches the expected solution, the amount of time that has passed since the last submission, the order in which solution components were entered into the interface, etc. The precision of this data is such that even a fairly short session with a computer-based learning environment (e.g. 30 minutes) may produce a large amount of process data for analysis.

In other cases, the data is less fine-grained. For example, a student's university transcript may contain a temporally ordered list of courses taken by the student, the grade that the student earned in each course, and when the student selected or changed his or her academic major. EDM leverages both types of data to discover meaningful information about different types of learners and how they learn, the structure of domain knowledge, and the effect of instructional strategies embedded within various learning environments. These analyses provide new information that would be difficult to discern by looking at the raw data. For example, analyzing data from an LMS may reveal a relationship between the learning objects that a student accessed during the course and their final course grade. Similarly, analyzing student transcript data may reveal a relationship between a student's grade in a particular course and their decision to change their academic major. Such information provides insight into the design of learning environments, which allows students, teachers, school administrators, and educational policy makers to make informed decisions about how to interact with, provide, and manage educational resources.

History

While the analysis of educational data is not itself a new practice, recent advances in educational technology, including the increase in computing power and the ability to log fine-grained data about students' use of a computer-based learning environment, have led to an increased interest in developing techniques for analyzing the large amounts of data generated in educational settings. This interest translated into a series of EDM workshops held from 2000 to 2007 as part of several international research conferences. In 2008, a group of researchers established what has become an annual international research conference on EDM, the first of which took place in Montreal, Quebec, Canada.

As interest in EDM continued to increase, EDM researchers established an academic journal in 2009, the Journal of Educational Data Mining, for sharing and disseminating research results. In 2011, EDM researchers established the International Educational Data Mining Society to connect EDM researchers and continue to grow the field.

With the introduction of public educational data repositories in 2008, such as the Pittsburgh Science of Learning Centre's (PSLC) DataShop and the National Center for Education Statistics (NCES), public data sets have made educational data mining more accessible and feasible, contributing to its growth.

Goals

Ryan S. Baker and Kalina Yacef identified the following four goals of EDM:

1. Predicting students' future learning behavior – With the use of student modeling, this goal can be achieved by creating student models that incorporate the learner's characteristics, including detailed information such as their knowledge, behaviours and motivation to learn. The user experience of the learner and their overall satisfaction with learning are also measured.
2. Discovering or improving domain models – Through the various methods and applications of EDM, discovery of new and improvements to existing models is possible. Examples include illustrating the educational content to engage learners and determining optimal instructional sequences to support the student's learning style.
3. Studying the effects of educational support that can be achieved through learning systems.
4. Advancing scientific knowledge about learning and learners by building and incorporating student models, the field of EDM research and the technology and software used.

Users and stakeholders

There are four main users and stakeholders involved with educational data mining. These include:

• Learners – Learners are interested in understanding student needs and methods to improve the learner's experience and performance. For example, learners can also benefit from the discovered knowledge by using the EDM tools to suggest activities and resources that they can use based on their interactions with the online learning tool and insights from past or similar learners. For younger learners, educational data mining can also inform parents about their child's learning progress. It is also necessary to effectively group learners in an online environment. The challenge is using the complex data to learn and interpret these groups through developing actionable models.
• Educators – Educators attempt to understand the learning process and the methods they can use to improve their teaching methods. Educators can use the applications of EDM to determine how to organize and structure the curriculum, the best methods to deliver course information and the tools to use to engage their learners for optimal learning outcomes. In particular, the distillation of data for human judgment technique provides an opportunity for educators to benefit from EDM because it enables educators to quickly identify behavioural patterns, which can support their teaching methods during the duration of the course or to improve future courses. Educators can determine indicators that show student satisfaction and engagement of course material, and also monitor learning progress.
• Researchers – Researchers focus on the development and the evaluation of data mining techniques for effectiveness. A yearly international conference for researchers began in 2008, followed by the establishment of the Journal of Educational Data Mining in 2009. The wide range of topics in EDM ranges from using data mining to improve institutional effectiveness to student performance.
• Administrators – Administrators are responsible for allocating the resources for implementation in institutions. As institutions are increasingly held responsible for student success, the administering of EDM applications are becoming more common in educational settings. Faculty and advisors are becoming more proactive in identifying and addressing at-risk students. However, it is sometimes a challenge to get the information to the decision makers to administer the application in a timely and efficient manner.

Phases

As research in the field of educational data mining has continued to grow, a myriad of data mining techniques have been applied to a variety of educational contexts. In each case, the goal is to translate raw data into meaningful information about the learning process in order to make better decisions about the design and trajectory of a learning environment. Thus, EDM generally consists of four phases:

1. The first phase of the EDM process (not counting pre-processing) is discovering relationships in data. This involves searching through a repository of data from an educational environment with the goal of finding consistent relationships between variables. Several algorithms for identifying such relationships have been utilized, including classification, regression, clustering, factor analysis, social network analysis, association rule mining, and sequential pattern mining.
2. Discovered relationships must then be validated in order to avoid overfitting.
3. Validated relationships are applied to make predictions about future events in the learning environment.
4. Predictions are used to support decision-making processes and policy decisions.

During phases 3 and 4, data is often visualized or in some other way distilled for human judgment. A large amount of research has been conducted in best practices for visualizing data.

Main approaches

Of the general categories of methods mentioned, prediction, clustering and relationship mining are considered universal methods across all types of data mining; however, Discovery with Models and Distillation of Data for Human Judgment are considered more prominent approaches within educational data mining.

Discovery with models

In the Discovery with Model method, a model is developed via prediction, clustering or by human reasoning knowledge engineering and then used as a component in another analysis, namely in prediction and relationship mining. In the prediction method use, the created model's predictions are used to predict a new variable. For the use of relationship mining, the created model enables the analysis between new predictions and additional variables in the study. In many cases, discovery with models uses validated prediction models that have proven generalizability across contexts.

Key applications of this method include discovering relationships between student behaviors, characteristics and contextual variables in the learning environment. Further discovery of broad and specific research questions across a wide range of contexts can also be explored using this method.

Distillation of data for human judgment

Humans can make inferences about data that may be beyond the scope in which an automated data mining method provides. For the use of education data mining, data is distilled for human judgment for two key purposes, identification and classification.

For the purpose of identification, data is distilled to enable humans to identify well-known patterns, which may otherwise be difficult to interpret. For example, the learning curve, classic to educational studies, is a pattern that clearly reflects the relationship between learning and experience over time.

Data is also distilled for the purposes of classifying features of data, which for educational data mining, is used to support the development of the prediction model. Classification helps expedite the development of the prediction model, tremendously.

The goal of this method is to summarize and present the information in a useful, interactive and visually appealing way in order to understand the large amounts of education data and to support decision making. In particular, this method is beneficial to educators in understanding usage information and effectiveness in course activities. Key applications for the distillation of data for human judgment include identifying patterns in student learning, behavior, opportunities for collaboration and labeling data for future uses in prediction models.

Applications

A list of the primary applications of EDM is provided by Cristobal Romero and Sebastian Ventura. In their taxonomy, the areas of EDM application are:

• Analysis and visualization of data
• Providing feedback for supporting instructors
• Recommendations for students
• Predicting student performance
• Student modeling
• Detecting undesirable student behaviors
• Grouping students
• Social network analysis
• Developing concept maps
• Constructing courseware – EDM can be applied to course management systems such as open source Moodle. Moodle contains usage data that includes various activities by users such as test results, amount of readings completed and participation in discussion forums. Data mining tools can be used to customize learning activities for each user and adapt the pace in which the student completes the course. This is in particularly beneficial for online courses with varying levels of competency.
• Planning and scheduling

New research on mobile learning environments also suggests that data mining can be useful. Data mining can be used to help provide personalized content to mobile users, despite the differences in managing content between mobile devices and standard PCs and web browsers.

New EDM applications will focus on allowing non-technical users use and engage in data mining tools and activities, making data collection and processing more accessible for all users of EDM. Examples include statistical and visualization tools that analyzes social networks and their influence on learning outcomes and productivity.

Courses

1. In October 2013, Coursera offered a free online course on "Big Data in Education" that taught how and when to use key methods for EDM. This course moved to edX in the summer of 2015, and has continued to run on edX annually since then. A course archive is now available online.
2. Teachers College, Columbia University offers a MS in Learning Analytics.

Publication venues

Considerable amounts of EDM work are published at the peer-reviewed International Conference on Educational Data Mining, organized by the International Educational Data Mining Society.

• 1st International Conference on Educational Data Mining (2008) – Montreal, Canada
• 2nd International Conference on Educational Data Mining (2009) – Cordoba, Spain
• 3rd International Conference on Educational Data Mining (2010) – Pittsburgh, PA, USA
• 4th International Conference on Educational Data Mining (2011) – Eindhoven, Netherlands
• 5th International Conference on Educational Data Mining (2012) – Chania, Greece
• 6th International Conference on Educational Data Mining (2013) – Memphis, TN, USA
• 7th International Conference on Educational Data Mining (2014) – London, UK
• 8th International Conference on Educational Data Mining (2015) – Madrid, Spain
• 9th International Conference on Educational Data Mining (2016) – Raleigh, NC, USA
• 10th International Conference on Educational Data Mining (2017) – Wuhan, China
• 11th International Conference on Educational Data Mining (2018) – Buffalo, NY, USA

EDM papers are also published in the Journal of Educational Data Mining (JEDM).

Many EDM papers are routinely published in related conferences, such as Artificial Intelligence and Education, Intelligent Tutoring Systems, and User Modeling, Adaptation, and Personalization.

In 2011, Chapman & Hall/CRC Press, Taylor and Francis Group published the first Handbook of Educational Data Mining. This resource was created for those that are interested in participating in the educational data mining community.

Contests

In 2010, the Association for Computing Machinery's KDD Cup was conducted using data from an educational setting. The data set was provided by the Pittsburgh Science of Learning Center's DataShop, and it consisted of over 1,000,000 data points from students using a cognitive tutor. Six hundred teams competed for over 8,000 USD in prize money (which was donated by Facebook). The goal for contestants was to design an algorithm that, after learning from the provided data, would make the most accurate predictions from new data. The winners submitted an algorithm that utilized feature generation (a form of representation learning), random forests, and Bayesian networks.

Costs and challenges

Along with technological advancements are costs and challenges associated with implementing EDM applications. These include the costs to store logged data and the cost associated with hiring staff dedicated to managing data systems. Moreover, data systems may not always integrate seamlessly with one another and even with the support of statistical and visualization tools, creating one simplified version of the data can be difficult. Furthermore, choosing which data to mine and analyze can also be challenging, making the initial stages very time consuming and labor-intensive. From beginning to end, the EDM strategy and implementation requires one to uphold privacy and ethics for all stakeholders involved.

Criticisms

• Generalizability – Research in EDM may be specific to the particular educational setting and time in which the research was conducted, and as such, may not be generalizable to other institutions. Research also indicates that the field of educational data mining is concentrated in western countries and cultures and subsequently, other countries and cultures may not be represented in the research and findings. Development of future models should consider applications across multiple contexts.
• Privacy – Individual privacy is a continued concern for the application of data mining tools. With free, accessible and user-friendly tools in the market, students and their families may be at risk from the information that learners provide to the learning system, in hopes to receive feedback that will benefit their future performance. As users become savvy in their understanding of online privacy, administrators of educational data mining tools need to be proactive in protecting the privacy of their users and be transparent about how and with whom the information will be used and shared. Development of EDM tools should consider protecting individual privacy while still advancing the research in this field.
• Plagiarism – Plagiarism detection is an ongoing challenge for educators and faculty whether in the classroom or online. However, due to the complexities associated with detecting and preventing digital plagiarism in particular, educational data mining tools are not currently sophisticated enough to accurately address this issue. Thus, the development of predictive capability in plagiarism-related issues should be an area of focus in future research.
• Adoption – It is unknown how widespread the adoption of EDM is and the extent to which institutions have applied and considered implementing an EDM strategy. As such, it is unclear whether there are any barriers that prevent users from adopting EDM in their educational settings.

Every Student Succeeds Act

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Every_Student_Succeeds_Act

Every Student Succeeds Act

Long title	An original bill to reauthorize the Elementary and Secondary Education Act of 1965 to ensure that every child achieves.
Acronyms (colloquial)	ESSA
Enacted by	the 114th United States Congress
Citations
Public law	Pub.L. 114–95 (text) (pdf)
Codification
Acts amended	Elementary and Secondary Education Act of 1965
Acts repealed	No Child Left Behind Act
Titles amended	20 U.S.C.: Education
U.S.C. sections amended	20 U.S.C. ch. 28 § 1001 et seq. 20 U.S.C. ch. 70
Legislative history
Introduced in the United States Senate by Lamar Alexander (R-TN) on April 30, 2015 Committee consideration by HELP Passed the United States House of Representatives on December 2, 2015 (359–64) Passed the United States Senate on December 9, 2015 (85–12) Signed into law by President Barack Obama on December 10, 2015

The Every Student Succeeds Act (ESSA) is a US law passed in December 2015 that governs the United States K–12 public education policy. The law replaced its predecessor, the No Child Left Behind Act (NCLB), and modified but did not eliminate provisions relating to the periodic standardized tests given to students. Like the No Child Left Behind Act, ESSA is a reauthorization of the 1965 Elementary and Secondary Education Act, which established the federal government's expanded role in public education.

The Every Student Succeeds Act passed both chambers of Congress with bipartisan support.

Overview

President Barack Obama signs the Act into law, December 2015

The bill is the first to narrow the United States federal government's role in elementary and secondary education since the 1980s. The ESSA retains the hallmark annual standardized testing requirements of the 2001 No Child Left Behind Act but shifts the law's federal accountability provisions to states. Under the law, students will continue to take annual tests between the third and eighth grades.

ESSA leaves significantly more control to the states and districts in determining the standards students are held to. States are required to submit their goals and standards and how they plan to achieve them to the US Department of Education, which must then submit additional feedback, and eventually approve. In doing so, the DOE still holds states accountable by ensuring they are implementing complete and ambitious, yet feasible goals. Students will then be tested each year from third through eighth grade and then once again their junior year of high school. These standardized tests will determine each student's capabilities in the classroom, and the success of the state in implementing its plans. The states are also left to determine the consequences low-performing schools might face and how they will be supported in the following years. The USDOE defines low-performing schools as those in the bottom ten percent of the state, based on the number of students who successfully graduate or the number of students who test proficient in reading or language arts and mathematics.

All states must have a multiple-measure accountability system, which include the following four indicators: achievement and/or growth on annual reading/language arts and math assessments; English language proficiency, an elementary and middle school academic measure of student growth; and high school graduation rates. All states also had to include at least one additional indicator of school quality or student success, commonly called the fifth indicator. Most states use chronic absenteeism as their fifth indicator.

Another primary goal of the ESSA is preparing all students, regardless of race, income, disability, ethnicity, or proficiency in English, for a successful college experience and fulfilling career. Therefore, ESSA also requires schools to offer college and career counseling and advanced placement courses to all students.

History

ESSA vote

Senate		House
Rep.	Dem.	Rep.	Dem.
40–12	45–0	178–64	181–0

The No Child Left Behind Act was due for reauthorization in 2007, but was not pursued for a lack of bipartisan cooperation. Many states failed to meet the NCLB's standards, and the Obama Administration granted waivers to many states for schools that showed success but failed under the NCLB standards. However, these waivers usually required schools to adopt academic standards such as the Common Core. The NCLB was generally praised for forcing schools and states to become more accountable for ensuring the education of poor and minority children. However, the increase in standardized testing that occurred during the presidencies of Bush and Obama met with resistance from many parents, and many called for a lessened role for the federal government in education. Similarly, the president of the National Education Association decried the NCLB's "one-size-fits-all model ... of test, blame and punish."

Following his 2014 re-election, Senate HELP Committee Chairman Lamar Alexander (R-TN), who had served as Education Secretary under President George H.W. Bush, decided to pursue a major rewrite of No Child Left Behind. Alexander and Patty Murray (D-WA), the ranking member of the HELP committee, collaborated to write a bipartisan bill that could pass the Republican-controlled Congress and earn the signature of President Barack Obama. At the same time, John Kline (R-MN), chairman of the House Committee on Education and the Workforce, pushed his own bill in the House. In July 2015, each chamber of the United States Congress passed their own renewals of the Elementary and Secondary Education Act. President Obama remained largely outside of the negotiations, though Alexander did win Obama's promise to not threaten to veto the bill during negotiations. As the House and Senate negotiated for the passage of a single bill in both houses, Bobby Scott (D-VA), the ranking member of the House Committee on Education and the Workforce, became a key player in ensuring Democratic votes in the House. By September 2015, the House and Senate had been able to resolve most of the major differences, but continued to differ on how to evaluate schools and how to respond to schools that perform poorly. House and Senate negotiators agreed to a proposal from Scott to allow the federal government to mandate specific circumstances in which states had to intervene in schools, while broadly giving states leeway in how to rate schools and in how to help struggling schools. Other major provisions included a pre-K program (at the urging of Murray), a provision to help ensure that states would not be able to exempt large swaths of students from testing (at the behest of civil rights groups), and restrictions on the power of the Education Secretary (at the urging of Alexander and Kline). The surprise resignation of Speaker John Boehner nearly derailed the bill, but incoming Speaker Paul Ryan's support of the bill helped ensure its passage. In December 2015, the House passed the bill in a 359–64 vote; days later, the Senate passed the bill in an 85–12 vote. President Obama signed the bill into law on December 10, 2015.

Students with disabilities

The Every Student Succeeds Act also sets new mandates on expectations and requirements for students with disabilities. Most students with disabilities will be required to take the same assessments and will be held to the same standards as other students. ESSA allows for only one percent of students, accounting for ten percent of students with disabilities, to be excused from the usual standardized testing. This one percent is reserved for students with severe cognitive disabilities, who will be required to take an alternate assessment instead. This is a smaller percentage of students than under past mandates, mainly because there is not enough staff available to administer the assessments to the students one-on-one. The Department of Education does not define disabled, rather, each state decides its own definition in order to determine which students will be allowed to take the alternate assessment. This could prove to be more challenging, though, when it comes to comparing students to one another because not all states will define disabled the same way. The ESSA has also recognized that bullying and harassment in schools disproportionately affects students with disabilities. Because of this, the ESSA requires states to develop and implement plans on how they will combat and attempt to reduce bullying incidents on their campuses.

Reception and opinion

Play media

President Obama explains why he signed the Act

Journalist Libby Nelson wrote that the ESSA was a victory for conservatives who wished to see federal control of school accountability transferred to states, and that states "could scale back their efforts to improve schools for poor and minority children".

Researchers from the Thomas B. Fordham Institute also approved of "grant[ing] states more authority over their accountability systems." However, they also expressed concern that, in an effort to set proficiency levels that low-performing students could pass, states would neglect the needs of high-performing students, which would disproportionately affect high-performing, low-income students.

State testing under ESSA

According to the October 24, 2015 U.S. Department of Education Fact Sheet: Testing Action Plan, state testing programs implemented under No Child Left Behind and Race to the Top were "draining creative approaches from our classrooms", "consuming too much instructional time" and "creating undue stress for educators and students."

Federal mandates and incentives were cited as partly responsible for students spending too much time taking standardized tests. ESSA provided states with flexibility to correct the balance and unwind "practices that have burdened classroom time or not served students or educators well."

The Every Student Succeeds Act statute, regulations and guidance give states broad discretion to design and implement assessment systems. Neither the statute nor the regulations apply any specific limits on test design, however United States Department of Education guidance documents say it is essential to ensure that tests "take up the minimum necessary time."

Section 1111(b)(2)(B)(viii)(1) of ESSA presents states with the opportunity to meet all Federal academic assessment requirements with a single comprehensive test. As of 2018-19 some states like Maryland continue to fulfill ESSA assessment requirements by administering four or more content-specific state standardized tests with testing windows that stretch from December through June.

The Every Student Succeeds Act prohibits any officer or employee of the Federal Government from using grants, contracts or other cooperative agreements to mandate, direct or control a state's academic standards and assessments. It also explicitly prohibited any requirement, direction or mandate to adopt the Common Core State Standards and gave states explicit permission to withdraw from the Common Core State Standards or otherwise revise their standards. On January 31, 2019, Florida's Governor signed an executive order "eliminating Common Core and the vestiges of Common Core" from Florida's public schools.

The following list is an incomplete enumeration of state testing initiatives designed to satisfy the requirements of the ESSA

• California Assessment of Student Performance and Progress (CASPP), replaced California Standardized Testing and Reporting Program (STAR) in 2014
• Connecticut Mastery Test (CMT)
• Hawaii Smarter Balanced Assessment (SBA). The SBA is a test set developed by the Smarter Balanced Assessment Consortium of US states http://www.hawaiipublicschools.org/TeachingAndLearning/Testing/StateAssessment/Pages/home.aspx
• Massachusetts Comprehensive Assessment System (MCAS)
• Pennsylvania System of School Assessment (PSSA)

Suspension of accountability requirements

An inauguration day directive on January 20, 2017, from President Donald Trump's Assistant to the President and Chief of Staff "Regulatory Freeze Pending Review" delayed implementation of new regulations, including portions of the Every Student Succeeds Act. On February 10, 2017, U.S. Secretary of Education Betsy DeVos wrote to chief state school officers that "states should continue their work" in developing their ESSA plans and noted that a revised template may be issued. In March 2017, Republican lawmakers with the support of the Trump administration used the Congressional Review Act to eliminate the Obama administration's accountability regulations.