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From Adaptive to Adaptable: The Next Generation for Personalized Learning

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From Adaptive to Adaptable: The Next Generation for Personalized Learning

This paper comprises several sequential articles contributed by participants in the IMS Global Learning Consortium Adaptive Learning Innovation Leadership Network.

We would like to thank the following individuals who lent their personal expertise to develop this resource, which is meant to inform and help facilitate conversations on adaptive learning. The hope is that its high-level approach will support institutional leaders and other stakeholders as they advance their practices and continue to evolve adaptive learning in higher education.
 
Melissa Edwards, Purdue University Dale Johnson, Arizona State University
Cristi Ford, University of Maryland, University College Lou Pugliese, Arizona State University
John Fritz, University of Maryland, Baltimore County Samantha Birk, IMS Global Learning Consortium 

 

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Table of Contents

Introduction: A Personal Example of Why Adaptive Learning Works

 

Introduction: A Personal Example of Why Adaptive Learning Works

By John Fritz, Ph.D.,Associate Vice President, Instructional Technology, UMBC

When I first started my Ph.D. eight years ago, I had to make an early decision that I knew would loom large: was my dissertation going to be a qualitative or quantitative study? Having studied English in college and grad school, a qualitative study might have better suited my skill set, but since I was interested in doing a data mining study of our campus’ learning management system (LMS), a quantitative study made more sense methodologically. My only concern was a spotty math background due in part to a cross-country move as a teenager and attending three different high schools. I knew I could write, but my preferred dissertation topic meant I also had to compute. And that meant I had to fill some gaps I’d simply learned to work around for too long.

Fortunately, I was blessed with an encouraging, patient professor for my two doctoral stats classes. I was okay with key concepts and could follow her procedural steps in class, but I struggled to replicate them on my own or on exams. About this time I came across Sal Khan’s fascinating 2011 TED talk on how he built Khan Academy (KA) based on lessons learned from tutoring his cousin with short screencasts of him working out problems that he put on YouTube1. Like his cousin and many others since, I loved watching Sal explain concepts I was fuzzy about, like linear regression, interaction effects, or the Central Limit Theorem. Also, I could play, pause and replay him over and over again, on my own time. Better yet, by the time I stumbled across KA, he had developed practice problems I could use to apply the concepts he explained. If I answered ten in a row correctly, I was declared “proficient” and encouraged to move on to a new topic. If I got stuck, I could watch a related video that would only pause my 10-in-a-row streak, or request a hint that made me start a new one. I also enjoyed his The One World School House (2012) and cited it in my dissertation that I completed in 2016.

Why is this important?

At its best, adaptive learning is really just a means to an end: developing a student’s ability to honestly and accurately self-assess his or her current knowledge, skills or abilities. Indeed, Barry Zimmerman has described two keys to students becoming self-regulated learners: (1) a willingness to take ownership of his or her conceptual problems, and (2) remediation that is focused and specific to the student’s particular gap, instead of general aphorisms such as “study harder."2 Adaptive learning is really about the remediation environment we provide to our students through course design and related technology (#2), but it can’t work unless students are “ready” (#1). Like others who have extolled the virtues of Khan Academy,3 I was ready and able to use it for my Ph.D. data mining study because I knew I had deficits. But it only worked because it could tailor itself to help address specific gaps I demonstrated in my answers to its practice questions.

While I’m a KA fan, adaptive or personalized learning or even “programmed” or “differentiated” instruction has been around ever since B.F. Skinner’s teaching machine.4 I even remember a programmed instruction grammar textbook I used in Freshmen English, and some may enjoy Audrey Watters’ short history of the Science Research Associates (SRA) “reading boxes” that were popular in grade school classrooms during the sixties and seventies.5 If you’ve never used Khan Academy, of course, I recommend it, but even more so for this white paper about adaptive learning. Assuming the old adage is true—that “teachers teach the way they were taught”—I’m going to also assume that most weren’t taught with adaptive learning. If so, Khan Academy is a great way to experience what adaptive learning is like as a student, to help think about how to use adaptive learning as an instructor, or someone who supports one.6

And yet, not everyone is a fan of Khan Academy, including some instructors who question its accuracy.7 My daughter’s high school math teacher even said she should be careful because it is “too professional,” whatever that means. Perhaps more importantly, Khan Academy has not always been easy to integrate into other campus systems likely to be in use by instructors. All this is to say that like any pedagogical innovation, we need to understand what it is and an inevitable “build vs. buy” decision we must make if we want to scale. The deep dives that follow by Dale Johnson and Lou Pugliese from Arizona State University should really help institutions frame the pros and cons of this question.

In my opinion, adaptive learning is the holy grail of educational technology—well, besides a Holodeck, of course. Instead of one-size fits all approaches to teaching and learning, adaptive learning can personalize learning at scale. We’re still in the early stages, but adaptive technologies can help facilitate responsibility for learning, which is ultimately what distinguishes a student from a customer.

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1 https://youtu.be/gM95HHI4gLk
http://chronicle.com/article/Struggling-Students-Can/64004/
https://www.khanacademy.org/stories 
http://americanhistory.si.edu/collections/search/object/nmah_690062 
http://hackeducation.com/2015/03/19/sra 
You can pick any subject, but I suggest the KA “class” on probability. Start by selecting “test yourself.”
http://www.chronicle.com/blognetwork/castingoutnines/2013/10/23/khan-academy-using-contractors-for-accuracy-checking-some-thoughts/  

 

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Arizona State University logo

Making a Case for an Adaptable Learning Platform

By Dale Johnson, Adaptive Program Manager, Arizona State University

The growing use of adaptive courseware has led to significant questions about the role of faculty in the process of developing, validating, and using them in their courses. Statements about artificial intelligence and advanced algorithms replacing instructors increase the risk of this discussion devolving into an unproductive “people versus technology” debate. Early efforts to create and implement adaptive courseware have clearly demonstrated that faculty are critical to the process and that these systems function best as part of an educational ecosystem—not as separate entities for educating students.

Faculty have always been the principal creators and disseminators of the knowledge at the heart of the educational process. By necessity, they play the same role in the development of adaptive courseware. Written explanations of complex concepts, videos to elucidate ideas, and assessment activities to challenge students are all examples of components in adaptive systems being generated by the creative minds of the faculty. They are the ones responsible for organizing this content into coherent lessons and sequencing those lessons for effective educational outcomes, whether that is in a traditional course or in an adaptive courseware.

To increase the adoption of adaptive courseware, we will need to answer the question, “what is the most effective way to help faculty use adaptive systems that can improve educational outcomes?” It is physically and financially unrealistic for each instructor to create a unique adaptive system. That would be equivalent to each one writing their own textbook. However, it is realistic to believe that each may contribute their knowledge to the creation of a better courseware for their students. Scholar-sourcing information from thousands of faculty members could provide a new model for developing these next generation, “adaptable” educational systems.

Early in my work on adaptive courseware, I had an experience that highlighted the need for adaptable systems that faculty can work with in order to create adaptive courseware for the students.

The lead faculty member on an introduction to biology course was a professor who had taught this subject to over 30,000 students during his career. Based on his experience, he said that students learn this subject better when the concepts are ordered from “macro” (biomes) to “micro” (DNA) in the curriculum. We videotaped all of his lectures in that sequence, and the instructional design team configured the courseware as directed. After two semesters in that format, a new lead faculty member was assigned to the course. She had different ideas about the best way to teach introduction to biology and proceeded to reconfigure the lesson sequence from micro to macro. Making those changes in our courseware consumed most of our four-week winter break. We had to move every piece of content and assessment to its new location through a very tedious manual process and then rewrite all of the exams to align with the new curriculum. That inefficient process opened our eyes to the need for more nimble systems that can be adapted by faculty members as they learn what works best for the students. 

That is why a primary objective of the IMS Global adaptive courseware Innovation Leadership Network is to create adaptable systems that can be reconfigured easily by the faculty as they experiment and learn what works for students. The design of that system has to start at the core with a standard definition of a “lesson” which can be moved in a system or shared among systems. For our purposes, a lesson is defined as a single learning objective supported by multiple instructional resources and assessment activities.  

An adaptable system will allow the faculty to configure these lessons into a curriculum and test the efficacy of each of the components as well as the whole. It will support the scientific process of hypothesizing, creating, testing, and improving the educational outcomes from the system. 

 

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Arizona State University logo

The Visualization for an Ideal Adaptable Learning Ecosystem

By Lou Pugliese, Director, Action Lab, Arizona State University

Adaptive learning is a technology and data-driven system of integrating instructional resources, learning objectives, and assessment activities into single, progressive modular learning elements that can be adapted to individual learners, reordered, or shared between learning systems. In short, the adaptive learning platform market is in the nascent stage of development, which creates confusion about what “adaptive” or “personalized” learning really is, what vendors are developing to what purposeful use and why faculty should be compelled to engage in experimentation. This environment of uncertainty causes apprehension among early adopter faculty impeding the advancement and benefits of adaptive learning into the mainstream of higher education. While there are broad applications for adaptive architecture  for varying content, students and course objectives, we have attempted to define the following four general adaptive categorical frameworks representing various levels of complexity;

Decision Tree Adaptive Systems
Decision Trees (DT) adaptive systems are the most basic type of “adaptive” environment (terms used loosely). This adaptive category is designed around the concept of a “tree” of pre-prescribed content modules and assessments to test for mastery. DT systems don’t take into consideration a specific learner profile and simply move students through a content test-fail/test-master sequence. DT systems simply use a set of rules from a pre-prescribed set of content modules organized in pre-prescribed sets of assessments and answer banks. Using intervals of data and feedback, learner workflows are created and individualized work streams assigned throughout a set pace.
 
Rules Based Adaptive Systems
Rules Based (RB) adaptive systems work on a preconceived set of rules and do not precisely adapt to an individual learner using Machine Learning (ML) like scientific methods, and RB is not designed around an algorithmic approach. In RB, a particular learning path is predetermined by rule sets that can change for individual learners, and feedback is provided once a learning unit is concluded. Students may take a differentiated path through assessment of prior knowledge and can progress individually in a differentiated way and the students can determine pacing. RB systems don’t use learner profile information and learning characteristics. Ongoing feedback is provided and remediation is prescribed based on the predetermined set of rules.
 
Machine Learning-Based Adaptive Systems
Machine learning-based adaptive platforms are the most advanced method in which to establish a truly adaptive state. Machine learning uses techniques that are equivalent, pattern recognition, statistical modeling, predictive analytics. ML-based systems use programmed algorithms to make real-time predictions about a learner’s mastery. The term “algorithm” is used very liberally. Simply stated, an algorithm is just a sequence of instructions telling the computer what to do. These ML learners continually harvest data in real time, determining students' proficiency in mastering learning objective-specific content. They analyze the data in real time, make inferences, and use the data to automatically adjust the overall sequence of skills or the type of content that a student experiences.
 
Advanced Algorithm Adaptive Systems
Advanced Algorithm (AA) adaptive systems provide 1:1 computer-to-student interaction making it scalable depending on the type of content (usually mathematics and sciences). Content modules are also prescribed to students based on prior proven mastery of knowledge and applied knowledge activity. They are tied to a specific, individual learner profile. In AA systems, learning paths, feedback, and content are evaluated in real time by constantly analyzing data from the individual student and comparing it to other students exposed to the same or similar content. AA adaptive learning records and manages a huge amount data, tied to a learner profile, and records clickstreams, time intervals, assessment attempts and other transactional behavioral data. 

 

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Questions and Problems We are Trying to Solve to Reach an Adaptive Learning Ecosystem

By the IMS Global Learning Consortium Adaptive Learning Innovation Leadership Network

The diverse categorical frameworks offered in adaptive learning make evident that there is a need for a collaborative of key stakeholders to express the next iteration of requirements necessary in this adaptive learning space. The Adaptive Learning Innovation Leadership Network has established the following set of criteria and questions for further exploration by the field with the intention of engaging the vendor community on what the faculty can continue to engage in meaningful work in adaptive learning. The following areas offer some insights to the necessary challenges we hope to address and offer future directions of the adaptive learning ecosystem.

  • Able to adapt and conform to a non-traditional learner lifestyle in which the pace is variable and the learner has on-demand requirements.
  • Have statistically accurate cognitive models representing authenticity of skill and competency mastery.
    • How does the system achieve that adaptive state?
  • Have accurate content path placement that includes accurate assessment of learned knowledge that can be applied.
  • Able to correctly perform adaptive sequencing to precisely and continuously collect real-time data on a student’s performance and use it to automatically change a student’s learning experience.
    • Have this data in a sharable format.
  • Have the ability to accurately determine remediation and corrective action using adaptive assessments in both normed and criterion-referenced assessment design where applicable.
  • Have the correct algorithm design for weighing different multiple measurement factors.
    • Can you describe how the adaptive logic works in your system?
  • Designed using adaptive assessments or are assessments derived from traditional formative assessment item banks.
  • Have synchronous capabilities as a critical measurement of student engagement behavior, and content knowledge mastery.
  • Have the necessary provisioning architecture to scale over thousands of concurrent users when considering the complex calculations needed to accommodate adaptive learning.
  • Able to develop comprehensive competency frameworks that index learning standards and outcomes.
  • Able to articulate the relationship between adaptive learning models and competency-based framework.
  • Multiple levels of competencies, based on outcomes, rather than on assessments?

Figure 1. A conceptual model of the ideal adaptive/adaptable learning environment framework developed by Lou Pugliese, Director, Action Lab, Arizona State University

 

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IMS Global Standards for an Adaptive/Adaptable Learning Ecosystem

By Samantha Birk, HED Institutional Program Manager, IMS Global Learning Consortium

In a cohesive, extensible adaptive learning environment the interoperability of multiple technology systems is the linchpin to success. In an ideal configuration, systems, both campus and third-party, work in concert leveraging open interoperability standards to provide a seamless experience for student and instructor, while ubiquitously leveraging multiple data points to inform the learning flow. This conceptualization of the next generation adaptable learning environment has as its foundation a three-layer architecture for organizing instructional resources:

  • Concept Chart
  • Course Configuration
  • Content Curation
Concept Chart
IMS Global can provide standards that facilitate the discussion within each discipline about identifying core concepts and defining how they are related to one another. Some disciplines like nursing and accounting have highly developed concept charts with well-defined learning objectives. Others are engaged in exciting debates among faculty members about the best way to organize their domain knowledge. The key is to position an IMS Global standard as the way to track and share the concept charting information, not as the organizer of the domain knowledge. Professional associations will be responsible for developing the concept charts as they see fit.     
 
Course Configuration
This is the process which faculty members will use to create a course from the lessons in the Concept Chart.  IMS Global standards like LTI and Caliper will play a key role in linking these elements together and tracking the data created during the learning process.
 
Content Curation
This is the process faculty members will use to select or edit instructional resources within each lesson they include in their course.
 
Although there are many IMS Global open standards that would play a role in an ideal adaptable learning ecosystem, we are focusing on the standards that would be essential for transitioning to an adaptable and extensible ecosystem:

    It is theorized that leveraging four of the IMS Global standards when innovatively used in concert with a revisioning of an architecture for an adaptable learning ecosystem, a clear path is laid out.  

    This shift would be characterized as interoperable, seamlessly allowing institutions to cohesively utilize multiple adaptable systems on campus and empower the faculty with greater pedagogical choices that have the:

    • Ability to support flexible course design and content sequencing that can be easily manipulated by the instructor to meet pedagogical requirements and learning outcomes.
    • Collect and aggregate multiple data collection points, provide a holistic understanding of a student’s learning style and abilities.
    • Remove the limitations that proprietary systems create (walled gardens) that silo assessment, algorithms, and learning data.
    • Achieve this ideal immersive adaptable learning environment requires leveraging some open interoperability standards.

    Learning Tools Interoperability (LTI)

    What it is:

      • Basic adaptable learning ecosystem using LTI integrationIn its basic form, LTI enables the user to move from one system to another without logging in.

      Features:

      • Creates a seamless experience for the faculty and student to move from the LMS into a third-party tool through a secure pass of use data that informs the tools the user's role (e.g. student), ID, etc.
      • Ability to pass grade data from the adaptive system to the LMS.

      Current limitations:

      • None identified at this time.

      Role in Adaptable Learning Ecosystem

      Scenario 1:
      How students may access an adaptive or adaptable learning system directly, but more than likely they will be moving from an LMS or similar campus learning system to the adaptive content when the adaptive content is only available in a third-party platform. In this scenario, LTI would be used to seamlessly pass the student from the LMS into the adaptive content.



      Figure 2. Basic Adaptable Learning Ecosystem using LTI Integration

      Basic adaptable learning ecosystem using LTI integration and Gradebook service

       

       

       

      Scenario 2:

      In many instances, instructors need student grades captured while working in the third-party tool reported back into the LMS, usually to the course gradebook. Leveraging LTI and the Gradebook Extension supports the launch and pass of the student from the LMS into the adaptive or adaptable system while passing grades back from the system into the LMS.

       

       

       

       

       

       

       


       


      Figure 3. Basic adaptable learning ecosystem using LTI integration and Gradebook Service

       

      Caliper Analytics

      What it is:

      • A means for consistently capturing and presenting measures of learning activity in a common data format that when analyzed using a set of metric profiles, directs the learning path.

      Role in Adaptable Learning Ecosystem

      Adaptive and adaptable learning cannot function without the collection and analysis of student learning data. Data and analytics are the linchpins of an adaptive learning ecosystem. All current adaptive systems have been built around proprietary standards and algorithms that reinforce the silos, or "walled gardens." These make it nearly impossible for the educator, student or institution to see a truly holistic view what is happening across the learning environment. Since many curriculums ask students to work in multiple learning environments (and possibly multiple adaptive learning systems), allowing data to be collected, combined with other provider data points, shared and analyzed in a consolidated view is the only true way to understand the student's progress within the curriculum fully. As an open standard, Caliper Analytics supports this aggregation of cross-provider data and enables the institution to see more clearly and deeply into the learning process.
      Current Limitations:
      • In the current environment, no data is passed back to the LMS or the institutions regarding the student's progress through the adaptive system. Student(s) scores might be passed back via LTI Gradebook Services, but there's no learning data that the institution can use to track student progress across multiple systems (e.g. how many pages were read, what chapter was not completed, how many simulations were completed, etc.) or other data points that would inform and trigger academic interventions.
      Adaptable learning ecosystem that enables the institution to collect and analyze learning data across multiple systems to inform a range of institutional goals, including student intervention and a holistic view of learning
      Figure 4. Adaptable learning ecosystem that enables the institution to collect and analyze learning data across multiple systems to inform a range of institutional goals, including student intervention and a holistic view of learning

       

      Common Cartridge (CC) and Thin Common Cartridge (Thin CC)

      What it is:
      • CC - a standardized way to package, import, export, and exchange digital learning materials and assessments from an LMS, portal, learning object repository or other learning platforms.
      • Thin CC - a standardized way to exchange links and provide authorization to third party web-based learning tools via Learning Tools Interoperability.

      Role in Adaptable Learning Ecosystem

      Common Cartridge and Thin Common Cartridge are related standards and are a way to package and exchange digital learning materials and assessments. Most often, CC is used to import and export course materials to and from your LMS, learning portal, LOR (Learning Object Repository) or another platform. Since course content can include large files or those that need to be maintained on an external server Thin CC is used to exchange links and seamless access to third party web-based learning content via LTI. It is most commonly used to package publisher content and is an easy way to add this content to a course, often saving faculty and instructional designers countless hours of content development time.
       
      Current limitations:
      • Content cannot be rearranged.
      Adaptable learning ecosystem that integrates and present the learning content within the LMS course shell
      Figure 5. Adaptable learning ecosystem that integrates and presents the learning content within the LMS course shell
       
       

      Question and Test Interoperability (QTI) and Accessible Portable Item Protocol (APIP)

       
      What is it:
      • QTI and APIP are two tightly connected standards. QTI enables the interoperability of assessment items and tests and is used in Common Cartridge and formats assessments for importing and exporting content into/out of learning platforms.  

      Role in Adaptable Learning Ecosystem

      APIP uses QTI as the base assessment format and adds support for rich accessibility features that meet the personal needs and presentation preferences of students with a variety of accommodation requirements. QTI v3 (under development now) adds the accessibility functionality introduced in APIP and improves upon it with the latest web accessibility and HTML standards developed by the W3C.
       
      The pivot point for adaptive learning is the assessment items which govern the prescriptive next steps for a student in an adaptive learning system. The current limitation is that the algorithms that inform the learning path are tied to prescribed assessments, allowing for minimal if any content changes to support instructor and pedagogical choices. In an adaptable ecosystem, the marriage between algorithm and assessment item would remain intact but would incorporate a level of logic that would enable content to be reordered or subsidized as deemed by the instructor, designer, program manager or other authorized curriculum decision maker.
       
      Current limitations:
      • Neither the QTI or APIP standards accommodate an affiliation with a learning algorithm needed to prescribe a student’s overall learning path that is adaptable and characterizes an adaptive learning experience. QTI does support testing through Computer Adaptive Testing (CAT). The CAT standard is intended to support learning algorithms that can be connected to a number of systems or data sets such as psychometric data, constraints, stopping criteria, and others. It enables an assessment, such as an exam, to present increasingly more difficult or easier questions based on how the student is performing on the assessment.
      Conceptual diagram of an interoperable adaptable learning ecosystem leveraging IMS Global standards
      Figure 6. Conceptual diagram of an interoperable adaptable learning ecosystem leveraging IMS Global standards

       

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      Select Terms

      What is Adaptive Learning?
      Adaptive learning systems are software-based technologies that dynamically customize curriculum and instruction to the knowledge level and abilities of an individual learner. Machine algorithms actively assess student performance in real time and adjust content, pacing, intervention and faculty engagement on an individualized basis. Adaptive learning systems are based on competency or mastery-based individual student progression as opposed to traditional learning management systems designed to move groups of students through a course.
       
      Personalized Learning vs. Adaptive Learning
      Personalized learning is any customization of learning by an instructor [or technology], while "adaptive" refers to technology that monitors student progress in a course and uses that data to modify instruction in real time.
       
      Adaptable Learning
      This method of learning is similar to adaptive learning, however, it focuses on the learning choosing their learning path. The learning path is not completely determined by the technology.
       
      Adaptable Learning System
      Describes the technology and the degree in which the technology can be manipulated by the mentor.
       
      Adaptation Goal
      The pedagogical reason or instructional objective of the content that is defined in the adaptive system.
       
      Adaptation Strategy
      The steps that are taken to adapt the system to the learning, and how active or reactive the learner and the system are throughout the adaptation or learning process.
       
      Adaptation Target
      The aspect of the instructional system that is adapted to the information about the learner. (Telthesarus)
       
      Adaptive Assessment
      A regularly delivered assessment within an adaptive platform where test items are given to the student the change based on how the individual student answer each question. Differentiated from a “fixed-form assessments” where the same assessment is administered to every student.
       
      Adaptive Logic Models
      The mathematical, algorithmic engines that create unique, individualized learning experiences in an attempt to radically improve individual learning outcomes for participatory learners at scale.
       
      Adaptive System
      Sometimes referred to as an adaptive learning environment, is typically a technology-based system, similar to a learning management system (LMS) that aim at supporting learners in acquiring knowledge and skills in a particular learning domain. can adapt to the needs of the learner. Rather than being a repository for content an adaptive system can assign modules and track progress, based on learner needs, learning styles, competencies, and mastery levels.
       
      Algorithm
      A process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer. (Wikipedia)
       
      Assessment
      A combinations of benchmark, diagnostic and formative assessment on a more immediate and continuous evaluation.
       
      Automation
      The creation of a sequenced processes for learning that is determined through the analysis of learning data and delivered by a technical ecosystem that encompasses assessment, evaluation, remediation and competency attainment.
       
      Content Scaffolding (Content Branching)
      How instructional content is designed to move students progressively toward mastery. Scaffolding as a method of content sequencing of prerequisite knowledge.
       
      Formative Assessment
      A range of formal and informal assessment procedures conducted by teachers during the learning process in order to modify teaching and learning activities to improve student attainment. Formative assessment is used throughout adaptive systems as a diagnostic element to test for mastery.
       
      Item Response Theory (IRT)
      A paradigm for the design, analysis, and scoring of tests, questionnaires, and similar instruments measuring abilities, attitudes, or other variables. It is a theory of testing based on the relationship between individuals’ performances on a test item and the test takers’ levels of performance on an overall measure of the ability that item was designed to measure (Wikipedia)
       
      Machine Learning
      An artificial intelligence technology that provides systems with the ability to learn without being manually programmed
       
      Metadata
      Data which that provides information about other data. Information about that describes an individual learning resource for purposes such as discovery and identification. It can include elements such as content type, subject, learning context, state standards, outcomes, and keywords.
       
      Real-time Data Collection
      Data from an array of sources is collected, calculated, and evaluated with some assumed inference method in real or near real-time.
       
      Self-organizing
      The capacity to self-organize information and data results from inferences to form ongoing and persistent feedback in the teaching and learning cycle
       
      Sequencing
      Capabilities of creating sequenced progression of skills and competencies contained in a finite learning path in a term or non-term unit of time.
       
      Student Learning Profile
      A profile, or complete picture of a learner’s preferences, strengths, and deficiencies. The profile is informed by the student’s learning style, intelligence preference, culture, and gender. It is used to inform the adaptive system’s delivery of content to the learner.

       

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