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.

Evidence-based education

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Evidence-based_education

Molecular paleontology refers to the recovery and analysis of DNA, proteins, carbohydrates, or lipids, and their diagenetic products from ancient human, animal, and plant remains. The field of molecular paleontology has yielded important insights into evolutionary events, species' diasporas, the discovery and characterization of extinct species. By applying molecular analytical techniques to DNA in fossils, one can quantify the level of relatedness between any two organisms for which DNA has been recovered.

Advancements in the field of molecular paleontology have allowed scientists to pursue evolutionary questions on a genetic level rather than relying on phenotypic variation alone. Using various biotechnological techniques such as DNA isolation, amplification, and sequencing scientists have been able to gain expanded new insights into the divergence and evolutionary history of countless organisms.

The evidence-based education movement has its roots in the larger movement towards evidence-based practices, and has been the subject of considerable debate.

The United Kingdom author and academic David H. Hargreaves presented a lecture in 1996 in which he stated "Teaching is not at present a research-based profession. I have no doubt that if it were it would be more effective and satisfying". He compared the fields of medicine and teaching, saying that physicians are expected to keep up to date on medical research, whereas many teachers may not even be aware of the importance of research to their profession. In order for teaching to become more research-based, he suggested, educational research would require a "radical change" and teachers would have to become more involved in the creation and application of research.

Following that lecture, English policy makers in education tried to bring theory and practice closer together. At the same time, existing education research faced criticism for its quality, reliability, impartiality and accessibility.

In 2000 and 2001 two international, evidence-based, studies were created to analyze and report on the effectiveness of school education throughout the world: the Programme for International Student Assessment (PISA) in 2000 and the Progress in International Reading Literacy Study (PIRLS) in 2001.

Also, around the same time three major evidence-based studies about reading were released highlighting the value of evidence in education: the USA National Reading Panel in 2000, the Australian report on Teaching reading in 2005, and the Independent review of the teaching of early reading (Rose Report 2006), England. Approximately a year before the Rose Report, the Scottish Executive Education Department (SEED) published the results of a study entitled A Seven Year Study of the Effects of Synthetic Phonics Teaching on Reading and Spelling Attainment (Clackmannanshire Report), comparing synthetic phonics with analytic phonics.

Scientifically based research (SBR) (also evidence-based practice in education) first appeared in United States Federal legislation in the Reading Excellence Act and subsequently in the Comprehensive School Reform program. However, it came into prominence in the U.S.A. under the No child left behind act of 2001 (NCLB), intended to help students in kindergarten through grade 3 who are reading below grade level. Federal funding was made available for education programs and teacher training that are "based on scientifically based reading research". NCLB was replaced in 2015 by the Every Student Succeeds Act (ESSA).

In 2002 the U.S. Department of Education founded the Institute of Education Sciences (IES) to provide scientific evidence to guide education practice and policy .

The State driven Common Core State Standards Initiative was developed in the United States in 2009 in an attempt to standardize education principles and practices. There appears to have been some attempt to incorporate evidence-based practices. For example, the core standards website has a comprehensive description of the specific details of the English Language Arts Standards that include the areas of the alphabetic principle, print concepts, phonological awareness, phonics and word recognition, and fluency. However, it is up to the individual States and school districts to develop plans to implement the standards, and the National Governors Guide to Early Literacy appears to lack details. As of 2020, 41 States had adopted the standards, and in most cases it has taken three or more years to have them implemented. For example, Wisconsin adopted the standards in 2010 and implemented them in the 2014–2015 school year, yet in 2020 the state Department of Public Instruction was in the process of developing materials to support the standards in teaching phonics.

According to reports, the Common Core State Standards Initiative does not appear to have led to a significant national improvement in students' performance. The Center on Standards, Alignment, Instruction, and Learning (C-SAIL) conducted a study of how the Common Core is received in schools. It reported these findings: a) there is moderately high buy-in for the standards among teachers, principals, and superintendents, but buy-in was significantly lower for teachers, b) there is wide variation in teachers’ alignment to the standards by content area and grade level, c) specificity is desired by some educators, however states and districts are reluctant to provide too much specificity, d) State officials generally agree that accountability changes under ESSA have allowed them to adopt a “smart power” message that is less punitive and more supportive.

Subsequently, in England the Education Endowment Foundation of London was established in 2011 by The Sutton Trust, as the lead charity of the government-designated What Works Centre for high quality evidence in UK Education.

In 2012 the Department for Education in England introduced an evidence-based "phonics reading check" to help support primary students with reading. (In 2016, the Minister for Education reported that the percentage of primary students not meeting reading expectations reduced from 33% in 2010 to 20% in 2016.)

Evidence-based education in England received a boost from the 2013 briefing paper by Dr. Ben Goldacre. It advocated for systemic change and more randomized controlled trials to assess the effects of educational interventions. He said this was not about telling teachers what to do, but rather “empowering teachers to make independent, informed decisions about what works”.  Following that a U.K. based non-profit, researchED, was founded to offer a forum for researchers and educationalists to discuss the role of evidence in education.

Discussion and criticism ensued. Some said research methods that are useful in medicine can be entirely inappropriate in the sphere of education. 

In 2014 the National Foundation for Educational Research, Berkshire, England published a report entitled ‘’Using Evidence in the Classroom: What Works and Why’’.  The review synthesises effective approaches to school and teacher engagement with evidence and discusses challenges, areas for attention and action. It is intended to help the teaching profession to make the best use of evidence about what works in improving educational outcomes.

In 2014 the British Educational Research Association (BERA) and the Royal Society of Arts )RSA) conducted an inquiry into the role of research in teacher education in England, Northern Ireland, Scotland and Wales. The final report made it clear that research and teacher inquiry were of paramount importance in developing self-improving schools. It advocated for a closer working partnership between teacher-researchers and the wider academic research community.

The 2015 Carter Review of Initial Teaching Training in the UK suggested that teacher trainees should have access and skills in using research evidence to support their teaching. However, they do not receive training in utilizing research.

NCLB in the USA was replaced in 2015 by the Every Student Succeeds Act (ESSA) that replaced "scientifically based research" with “evidence-based interventions” (any “activity, strategy, or intervention that shows a statistically significant effect on improving student outcomes or other relevant outcomes”). ESSA has four tiers of evidence that some say gives schools and policy makers greater control because they can choose the desired tier of evidence. The evidence tiers are as follows:

• Tier 1 – Strong Evidence: supported by one or more well-designed and well-implemented randomized controlled experimental studies.
• Tier 2 – Moderate Evidence: supported by one or more well-designed and well-implemented quasi-experimental studies.
• Tier 3 – Promising Evidence: supported by one or more well-designed and well-implemented correlational studies (with statistical controls for selection bias).
• Tier 4 – Demonstrates a Rationale: practices that have a well-defined logic model or theory of action, are supported by research, and have some effort underway by state educational agencies (SEA), local educational agencies (LEA), or outside research organization to determine their effectiveness.

In 2016 the Department for Education in England published the White Paper Educational Excellence Everywhere. It states its intention to support an evidence-informed teaching profession by increasing teachers’ access to and use of “high quality evidence”. It will also establish a new British education journal and expand the Education Endowment Foundation.  In addition, on October 4, 2016 the Government announced an investment of around £75 million in the Teaching and Leadership Innovation Fund, to support high-quality, evidence-informed, professional development for teachers and school leaders. A research report on July 2017 entitled Evidence-informed teaching: an evaluation of progress in England  concluded this was necessary, but not sufficient. It said that the main challenge for policy makers and researchers was the level of leadership capacity and commitment to make it happen. In other words, the attitudes and actions of school leaders influence how classroom teachers are supported and held accountable for using evidence informed practices.

In 2017 the British Educational Research Association (BERA) examined the role of universities in professional development, focusing especially on teacher education and medical education.

Critics continue, saying “Education research is great but never forget teaching is a complex art form.”  In 2018, Dylan Wiliam, Emeritus Professor of Educational Assessment at University College London, speaking at researchED stated that “Educational research will never tell teachers what to do; their classrooms are too complex for this ever to be possible.” Instead, he suggests, teachers should become critical users of educational research and “aware of when even well-established research findings are likely to fail to apply in a particular setting”. 

Reception

Acceptance

Since many educators and policy makers are not experienced in evaluating scientific studies and studies have found that "teachers’ beliefs are often guided by subjective experience rather than by empirical data", several non-profit organizations have been created to critically evaluate research studies and provide their analysis in a user-friendly manner. They are outlined in research sources and information.

EBP has not been readily adopted in all parts of the education field, leading some to suggest the K-12 teaching profession has suffered a loss of respect because of its science-aversive culture and failure to adopt empirical research as the major determinant of its practices. Speaking in 2017, Harvey Bischof, Ontario Secondary School Teachers' Federation (OSSTF), said there is a need for teacher-centred education based upon what works in the classroom. He suggested that Ontario education "lacks a culture of empiricism" and is vulnerable to gurus, ideologues and advocates promoting unproven trends and fads. 

Neuroscientist Mark Seidenberg, University of Wisconsin–Madison, stated that “A stronger scientific ethos (in education) could have provided a much needed defense against bad science”, particularly in the field of early reading instruction. Other influential researchers in psychopedagogy, cognitive science and neuroscience, such as Stanislas Dehaene and Michel Fayol have also supported the view of incorporating science into educational practices.

Critics and skeptics

Skeptics point out that EBP in medicine often produces conflicting results, so why should educators accept EBP in education?  Others feel that EBE "limits the opportunities for educational professionals to exert their judgment about what is educationally desirable in particular situations".

Some suggest teachers should not pick up research findings and implement them directly into the classroom; instead they advocate for a modified approach some call evidence-informed teaching that combines research with other types of evidence plus personal experience and good judgement.  (To be clear, some use the term evidence-informed teaching to mean "practice that is influenced by robust research evidence".)

Still others say there is “a mutual interdependence between science and education”, and teachers should become better trained in research science and “take science sufficiently seriously” to see how its methods might inform their practice. Straight talk on evidence has suggested that  reports about evidence in education need to be scrutinized for accuracy or subjected to Metascience (research on research). 

In a 2020 talk featured on ResearchED, Dylan Wiliam argues that when looking at the cost, benefit and practicality of research, more impact on student achievement will come from a knowledge-rich curriculum and improving teachers’ pedagogical skills.

Concerns

There has also been some discussion of a philosophical nature about the validity of scientific evidence. This led James M. Kauffman, University of Virginia, and Gary M. Sasso, University of Iowa, to respond in 2006 suggesting that problems arise with the extreme views of a) the "unbound faith in science" (i.e. scientism) or b) the "criticism of science" (that they label as the "nonsense of postmodernism"). They go on to say that science is "the imperfect but best tool available for trying to reduce uncertainty about what we do as special educators".

Meta-analysis

A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. A concern of some researchers is the unreliability of some of these reports due to mythological features. For example, it is suggested that some meta-analyses findings are not credible because they do not exclude or control for studies with small sample sizes or very short durations, and where the researchers are doing the measurements. Such reports can yield "implausible" results. According to Robert Slavin, of the Center for Research and Reform in Education at Johns Hopkins University and Evidence for ESSA, "Meta-analyses are important, because they are widely read and widely cited, in comparison to individual studies. Yet until meta-analyses start consistently excluding, or at least controlling for studies with factors known to inflate mean effect sizes, then they will have little if any meaning for practice."

Research sources and information

The following organizations evaluate research on educational programs, or help educators to understand the research.

Best Evidence Encyclopedia (BEE)

Best Evidence Encyclopedia (BEE) is a free website created by the Johns Hopkins University School of Education's Center for Data-Driven Reform in Education (established in 2004) and is funded by the Institute of Education Sciences, U.S. Department of Education.  It gives educators and researchers reviews about the strength of the evidence supporting a variety of English programs available for students in grades K–12. The reviews cover programs in areas such as Mathematics, Reading, Writing, Science, Comprehensive school reform, and Early childhood Education; and includes such topics as effectiveness of technology and struggling readers.

BEE selects reviews that meet consistent scientific standards and relate to programs that are available to educators. 

Educational programs in the reviews are rated according to the overall strength of the evidence supporting their effects on students as determined by the combination the quality of the research design and their effect size. The BEE website contains an explanation of their interpretation of effect size and how it might be viewed as a percentile score. It uses the following categories of ratings:

• Strong evidence of effectiveness
• Moderate evidence of effectiveness
• Limited evidence of effectiveness: Strong evidence of modest effects
• Limited evidence of effectiveness: Weak evidence with notable effect
• No qualifying studies

Reading programs

In 2019, BEE released a review of research on 61 studies of 48 different programs for struggling readers in elementary schools. 84% were randomized experiments and 16% quasi-experiments.  The vast majority were done in the USA, the programs are replicable, and the studies, done between 1990 and 2018, had a minimum duration of 12 weeks. Many of the programs used phonics-based teaching and/or one or more of the following: cooperative learning, technology-supported adaptive instruction (see Educational technology), metacognitive skills, phonemic awareness, word reading, fluency, vocabulary, multisensory learning, spelling, guided reading, reading comprehension, word analysis, structured curriculum, and balanced literacy (non-phonetic approach). Significantly, table 5 (pg. 88) shows the mean weighted effect sizes of the programs by the manner in which they were conducted (i.e. by school, by classroom, by technology-supported adaptive instruction, by one-to-small-group tutoring, and by one-to-one tutoring).  Table 8 (pg. 91) lists the 22 programs meeting ESSA standards for strong and moderate ratings, and their effect size.

The review concludes that a) outcomes were positive for one-to-one tutoring, b) outcomes were positive but not as large for one-to-small group tutoring, c) there were no differences in outcomes between teachers and teaching assistants as tutors, d) technology-supported adaptive instruction did not have positive outcomes, e) whole-class approaches (mostly cooperative learning) and whole-school approaches incorporating tutoring obtained outcomes for struggling readers as large as those found for one- to-one tutoring, and benefitted many more students, and f) approaches mixing classroom and school improvements, with tutoring for the most at-risk students, have the greatest potential for the largest numbers of struggling readers.

The site also offers a newsletter  from Robert Slavin, Director of the Center for Research and Reform in Education  containing information on education around the world.

Blueprints for healthy youth development

Blueprints for Healthy Youth Development, University of Colorado Boulder, offers a registry of evidence-based interventions with "the strongest scientific support" that are effective in promoting a healthy course of action for youth development.

Education Endowment Foundation

The Education Endowment Foundation of London, England was established in 2011 by The Sutton Trust, as a lead charity in partnership with Impetus Trust, together being the government-designated What Works Centre for UK Education.  It offers an online, downloadable Teaching & Learning Toolkit evaluating and describing a variety of educational interventions according to cost, evidence and impact.  As an example, it evaluates and describes a 2018 phonics reading program with low cost, extensive evidence and moderate impact. 

Evidence for ESSA

Evidence for ESSA  began in 2017 and is produced by the Center for Research and Reform in Education (CRRE) at Johns Hopkins University School of Education, Baltimore, MD. It is reported to have received "widespread support ", and offers free up-to-date information on current PK-12 programs in reading, math, social-emotional learning, and attendance that meet the standards of the Every Student Succeeds Act (ESSA) (the United States K–12 public education policy signed by President Obama in 2015).. It also provides information on programs that do meet ESSA standards as well as those that do not.

Evidence-based PK-12 programs

There are three program categories 1) whole class, 2) struggling readers and 3) English learners. Programs can be filtered by a) ESSA evidence rating (strong, moderate, and promising), b) school grade, c) community (rural, suburban, urban), d) groups (African American, Asian American, Hispanic, White, free and reduced price lunch, English learners, and special education), and e) a variety of features such as cooperative learning, technology, tutoring, etc.

For example, as of June 2020 there were 89 reading programs in the database. After filtering for strong results, grades 1-2, and free and reduced-price lunches, 23 programs remain. If it is also filter for struggling readers, the list is narrowed to 14 programs. The resulting list is shown by the ESSA ratings, Strong, Moderate or Promising. Each program can then be evaluated according to the following: number of studies, number of students, average effect size, ESSA rating, cost, program description, outcomes, and requirements for implementation.

Social programs that work and Straight Talk on Evidence

Social programs that work and Straight Talk on Evidence are administered by the Arnold Ventures LLC’s evidence-based policy team, with offices in Houston, Washington, D.C., and New York City. The team is composed of the former leadership of the Coalition for Evidence-Based Policy, a nonprofit, nonpartisan organization advocating the use of well-conducted randomized controlled trials (RCTs) in policy decisions. It offers information on twelve types of social programs including education.

Social programs that work evaluates programs according to their RCTs and gives them one of three ratings:

• Top Tier: Programs with two or more replicable and well conducted RCTs (or one multi-site RTC), in a typical community settings producing sizable sustained outcomes.
• Near Top Tier: Programs that meet almost all elements of the Top Tier standard but need another replication RCT to confirm the initial findings.
• Suggestive Tier: Programs appearing to be a strong candidate with some shortcomings. They produce sizeable positive effects based on one or more well conducted RCTs (or studies that almost meet this standard); however, the evidence is limited by factors such as short-term follow-up or effects that are not statistically significant.

Education programs include K-12 and postsecondary. The programs are listed under each category according to their rating and the update date is shown. For example, as of June 2020 there were 12 programs under K-12; two were Top Tier, five were Near Top Tier, and the remainder were Suggestive Tier. Each program contains information about the program, evaluation methods, key findings and other data such as the cost per student. Beyond the general category, there does not appear to be any way to filter for only the type of program of interest, however the list may not be especially long.

Straight Talk on Evidence seeks to distinguish between programs that only claim to be effective and other programs showing credible findings of being effective. It reports mostly on randomized controlled trial (RCT) evaluations, recognizing that RCTs offer no guarantee that the study was implemented well, or that its reported results represented the true findings. The lead author of a study is given an opportunity to respond to their report prior to its publication.

What Works Clearinghouse (WWC)

What Works Clearinghouse (WWC) of Washington, DC, was established in 2002 and evaluates numerous educational programs in twelve categories by the quality and quantity of the evidence and the effectiveness. It is operated by the federal National Center for Education Evaluation and Regional Assistance (NCEE), part of the Institute of Education Sciences (IES) 

Publications

WWC publications are available for a variety of topics (e.g. literacy, charter schools, science, early childhood, etc.) and Type (i.e. Practice guide or Intervention report).

Practice guides, tutorials, videos and webinars

Practice guides with recommendations are provided covering a wide variety of subjects such as Using Technology to Support Postsecondary Student Learning and Assisting Students Struggling with Reading, etc. Other resources such as tutorials, videos and webinars are also available.

Reviews of individual studies

Individual studies are available that have been reviewed by WWC and categorized according to the evidence tiers of the United States Every student succeeds act (ESSA). Search filters are available for the following:

• WWC ratings (e.g. meets WWC standards with or without reservations, meets WWC standards without reservations, etc.)
• Topic (e.g. behavior, charter schools, etc.)
• Studies meeting certain design standards (e.g. Randomized controlled trial, Quasi-experiment design, etc.)
• ESSA ratings (e.g. ESSA Tier 1, ESSA Tier 2, etc.)
• Studies with one or more statistically positive findings

Intervention reports, programs and search filters

Intervention reports are provided for programs according to twelve topics (e.g. literacy, mathematics, science, behavior, etc.).

The filters are helpful to find programs that meet specific criteria. For example, as of July 2020 there were 231 literacy programs in the WWC database. (Note: these are literacy programs that may have several individual trials and some of the trials were conducted as early as 2006.) If these programs are filtered for outcomes in Literacy-Alphabetics the list is narrowed to 25 programs that met WWC standards for evidence and had at least one "potentially positive" effectiveness rating. If the list is further filtered to show only programs in grades one or two, and delivery methods of individual, or small group, or whole class the list is down to 14 programs; and five of those have an effectiveness rating of "strong evidence that intervention had a positive effect on outcomes" in alphabetics.

The resulting list of programs can then be sorted by a) evidence of effectiveness, or b) alphabetically, or c) school grades examined. It is also possible to select individual programs to be compared with each other; however it is advisable to recheck each individual program by searching on the Intervention Reports page. The resulting programs show data in the following areas:

• outcome domain (e.g. alphabetics, oral language, general mathematics achievement, etc.)
• effectiveness rating (e.g. positive, potentially positive, mixed, etc.)
• number of studies meeting WWC standards
• grades examined (e.g. K-4)
• number of students in studies that met the WWC standards, and
• improvement index (i.e. the expected change in percentile rank).

It is also possible to view the program's Evidence snapshot, detailed Intervention report and Review protocols. For other independent "related reviews", go to the evidence snapshot then the WWC Summary of Evidence.

The following chart, updated in July 2020, shows some programs that had "strong evidence" of a "positive effect on outcomes" in the areas specified. The results may have changed since that time, however current information is available on the WWC website, including the outcome domains that did not have "strong evidence".

Some of the concerns expressed about WWC are that it appears to have difficulty keeping up with the research so it may not be current; and when a program is not listed on their database, it may be that it did not meet their criteria or they have not yet reviewed it, but you don't know which. In addition Straight Talk on Evidence, authored by the Arnold Ventures LLC’ Evidence-Based Policy team , on January 16, 2018 expressed concerns about the validity of the ratings provided by WWC. It says WWC in some cases reported a "preliminary outcome when high-quality RCTs found no significant effects on more important and final educational outcomes".

A summary of the January 2020 changes to the WWC procedures and standards is available on their site.

Other sources of information

• The British Educational Research Association (BERA) claims to be the home of educational research in the United Kingdom. It is a membership association that aims to improve the knowledge of education by advancing research quality, capacity and engagement. Its resources include a quarterly magazine, journals, articles, and conferences.

• Campbell Collaboration is a nonprofit organization that promotes evidence-based decisions and policy through the production of systematic reviews and other types of evidence synthesis. It has wide spread international support, and allows users to easily search by topic area (e.g. education) or key word (e.g. reading).

• Doing What Works is provided by WestEd, a San Francisco-based nonprofit organization, and offers an online library  that includes interviews with researchers and educators, in addition to materials and tools for educators. WestEd was criticized in January 2020, claiming they did not interview all interested parties prior to releasing a report.

• Early childhood Technical Assistance Center (ECTA), of Chapel Hill, NC, provides resources on evidence-based practices in areas specific to early childhood care and education, professional development, early intervention and early childhood special education.

• Florida Center for Reading Research is a research center at Florida State University that explores all aspects of reading research. Its Resource Database allows you to search for information based on a variety of criteria.

• Institute of Education Sciences (IES), Washington, DC, is the statistics, research, and evaluation arm of the U.S. Department of Education. It funds independent education research, evaluation and statistics. It published a Synthesis of its Research on Early Intervention and Early Childhood Education in 2013. Its publications and products can be searched by author, subject, etc.

• The International Initiative for Impact Evaluation (3ie) is a registered non-governmental organisation, since 2008, with offices in New Delhi, London and Washington, DC. Its self-described vision is to improve lives through evidence-informed action in developing countries. In 2016 their researchers synthesised evidence from 238 impact evaluations and 121 qualitative research studies and process evaluations in 52 low-and middle-income countries (L&MICs). It looked at children’s school enrolment, attendance, completion and learning.The results can be viewed in their report entitled The impact of education programmes on learning and school participation in low- and middle-income countries.

• National Foundation for Educational Research (NFER) is a non-profit research and development organization based in Berkshire, England. It produces independent research and reports about issues across the education system, such as Using Evidence in the Classroom: What Works and Why.

• Office for Standards in Education (Ofsted), in England, conducts research on schools, early education, social care, further education and skills. 

• The Ministry of Education, Ontario, Canada offers a site entitled What Works? Research Into Practice. It is a collection of research summaries of promising teaching practice written by experts at Ontario universities.

• RAND Corporation, with offices throughout the world, funds research on early childhood, K-12, and higher education.

• ResearchED, a U.K. based non-profit since 2013 has organized education conferences around the world (e.g. Africa, Australia, Asia, Canada, the E.U., the Middle East, New Zealand, the U.K. and the U.S.A.) featuring researchers and educators in order to "promote collaboration between research-users and research-creators". It has been described as a "grass-roots teacher-led project that aims to make teachers research-literate and pseudo-science proof". It also publishes an online magazine featuring articles by practicing teachers and others such as professor Daniel T. Willingham (University of Virginia) and Professor Dylan Wiliam (Emeritus professor, UCL Institute of Education). And finally, it offers frequent, free online video presentations  on subjects such as curriculum design, simplifying your practice, unleashing teachers' expertise, the bridge over the reading gap, education post-corona, remote teaching, teaching critical thinking, etc. The free presentations are also available on its YouTube channel.  ResearchED has been featured in online debates about so called "teacher populism".

• Research 4 Schools, University of Delaware is supported by the Institute of Education Sciences, U.S. Department of Education and offers peer reviewed research about education.

Evidence-based learning techniques

The following are some examples of evidence-based learning techniques.

Spaced repetition

In the Leitner system, correctly answered cards are advanced to the next, less frequent box, while incorrectly answered cards return to the first box.

Spaced repetition is a theory that repetitive training that includes long intervals between training sessions helps to form long-term memory. It is also referred to as spaced training, spacing effect and spaced learning). Such training has been known since the seminal work of Hermann Ebbinghaus to be superior to training that includes short inter-trial intervals (massed training or massed learning) in terms of its ability to promote memory formation. It is a learning technique that is performed with flashcards. Newly introduced and more difficult flashcards are shown more frequently while older and less difficult flashcards are shown less frequently in order to exploit the psychological spacing effect. The use of spaced repetition has been proven to increase rate of learning. Although the principle is useful in many contexts, spaced repetition is commonly applied in contexts in which a learner must acquire a large number of items and retain them indefinitely in memory. It is, therefore, well suited for the problem of vocabulary acquisition in the course of second language learning. A number of spaced repetition softwares have been developed to aid the learning process. It is also possible to perform spaced repetition with flash cards using the Leitner system.

Errorless learning

Errorless learning was an instructional design introduced by psychologist Charles Ferster in the 1950s as part of his studies on what would make the most effective learning environment. B. F. Skinner was also influential in developing the technique, and noted: "errors are not necessary for learning to occur. Errors are not a function of learning or vice versa nor are they blamed on the learner. Errors are a function of poor analysis of behavior, a poorly designed shaping program, moving too fast from step to step in the program, and the lack of the prerequisite behavior necessary for success in the program." Errorless learning can also be understood at a synaptic level, using the principle of Hebbian learning ("Neurons that fire together wire together").

Interest from psychologists studying basic research on errorless learning declined after the 1970s. However, errorless learning attracted the interest of researchers in applied psychology, and studies have been conducted with both children (e.g., educational settings) and adults (e.g. Parkinson's patients). Errorless learning continues to be of practical interest to animal trainers, particularly dog trainers.

Errorless learning has been found to be effective in helping memory-impaired people learn more effectively. The reason for the method's effectiveness is that, while those with sufficient memory function can remember mistakes and learn from them, those with memory impairment may have difficulty remembering not only which methods work, but may strengthen incorrect responses over correct responses, such as via emotional stimuli. See also the reference by Brown to its application in teaching mathematics to undergraduates.

N-back training

The n-back task is a continuous performance task that is commonly used as an assessment in cognitive neuroscience to measure a part of working memory and working memory capacity. The n-back was introduced by Wayne Kirchner in 1958.

A 2008 research paper claimed that practicing a dual n-back task can increase fluid intelligence (Gf), as measured in several different standard tests. This finding received some attention from popular media, including an article in Wired. However, a subsequent criticism of the paper's methodology questioned the experiment's validity and took issue with the lack of uniformity in the tests used to evaluate the control and test groups. For example, the progressive nature of Raven's Advanced Progressive Matrices (APM) test may have been compromised by modifications of time restrictions (i.e., 10 minutes were allowed to complete a normally 45-minute test). The authors of the original paper later addressed this criticism by citing research indicating that scores in timed administrations of the APM are predictive of scores in untimed administrations.

The 2008 study was replicated in 2010 with results indicating that practicing single n-back may be almost equal to dual n-back in increasing the score on tests measuring Gf (fluid intelligence). The single n-back test used was the visual test, leaving out the audio test. In 2011, the same authors showed long-lasting transfer effect in some conditions.

Two studies published in 2012 failed to reproduce the effect of dual n-back training on fluid intelligence. These studies found that the effects of training did not transfer to any other cognitive ability tests. In 2014, a meta-analysis of twenty studies showed that n-back training has small but significant effect on Gf and improve it on average for an equivalent of 3-4 points of IQ. In January 2015, this meta-analysis was the subject of a critical review due to small-study effects. The question of whether n-back training produces real-world improvements to working memory remains controversial.