Adaptive learning

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Adaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. [1] In professional learning contexts, individuals may "test out" of some training to ensure they engage with novel instruction. Computers adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions, tasks and experiences. The technology encompasses aspects derived from various fields of study including computer science, AI, psychometrics, education, psychology, and brain science.

Contents

Research conducted, particularly in educational settings within the United States, has demonstrated the efficacy of adaptive learning systems in promoting student learning. Among 37 recent studies that examined the effects of adaptive learning on learning outcomes, an overwhelming majority of 86% (32 studies) reported positive effects. [2]

Adaptive learning has been partially driven by a realization that tailored learning cannot be achieved on a large-scale using traditional, non-adaptive approaches. Adaptive learning systems endeavor to transform the learner from passive receptor of information to collaborator in the educational process. Adaptive learning systems' primary application is in education, but another popular application is business training. They have been designed as desktop computer applications, web applications, and are now being introduced into overall curricula. [3]

History

Adaptive learning or intelligent tutoring has its origins in the artificial-intelligence movement and began gaining popularity in the 1970s. At that time, it was commonly accepted that computers would eventually achieve the human ability of adaptivity. In adaptive learning, the basic premise is that the tool or system will be able to adjust to the student/user's learning method, which results in a better and more effective learning experience for the user. Back in the 70's the main barrier was the cost and size of the computers, rendering the widespread application impractical. Another hurdle in the adoption of early intelligent systems was that the user interfaces were not conducive to the learning process. The start of the work on adaptive and intelligent learning systems is usually traced back to the SCHOLAR system that offered adaptive learning for the topic of geography of South America. [4] A number of other innovative systems appeared within five years. A good account of the early work on adaptive learning and intelligent tutoring systems can be found in the classic book "Intelligent Tutoring Systems". [5]

Technology and methodology

Adaptive learning systems have traditionally been divided into separate components or 'models'. While different model groups have been presented, most systems include some or all of the following models (occasionally with different names): [6] [7]

Expert model

The expert model stores information about the material which is being taught. This can be as simple as the solutions for the question set but it can also include lessons and tutorials and, in more sophisticated systems, even expert methodologies to illustrate approaches to the questions.

Adaptive learning systems which do not include an expert model will typically incorporate these functions in the instructional model.

Student model

The simplest means of determining a student's skill level is the method employed in CAT (computerized adaptive testing). In CAT, the subject is presented with questions that are selected based on their level of difficulty in relation to the presumed skill level of the subject. As the test proceeds, the computer adjusts the subject's score based on their answers, continuously fine-tuning the score by selecting questions from a narrower range of difficulty.

An algorithm for a CAT-style assessment is simple to implement. A large pool of questions is amassed and rated according to difficulty, through expert analysis, experimentation, or a combination of the two. The computer then performs what is essentially a binary search, always giving the subject a question which is halfway between what the computer has already determined to be the subject's maximum and minimum possible skill levels. These levels are then adjusted to the level of the difficulty of the question, reassigning the minimum if the subject answered correctly, and the maximum if the subject answered incorrectly. Obviously, a certain margin for error has to be built in to allow for scenarios where the subject's answer is not indicative of their true skill level but simply coincidental. Asking multiple questions from one level of difficulty greatly reduces the probability of a misleading answer, and allowing the range to grow beyond the assumed skill level can compensate for possible misevaluations.

A further extension of identifying weaknesses in terms of concepts is to program the student model to analyze incorrect answers. This is especially applicable for multiple choice questions. Consider the following example:

Q. Simplify:
a) Can't be simplified
b)
c) ...
d) ...

Clearly, a student who answers (b) is adding the exponents and failing to grasp the concept of like terms. In this case, the incorrect answer provides additional insight beyond the simple fact that it is incorrect.

Instructional model

The instructional model generally looks to incorporate the best educational tools that technology has to offer (such as multimedia presentations) with expert teacher advice for presentation methods. The level of sophistication of the instructional model depends greatly on the level of sophistication of the student model. In a CAT-style student model, the instructional model will simply rank lessons in correspondence with the ranks for the question pool. When the student's level has been satisfactorily determined, the instructional model provides the appropriate lesson. The more advanced student models which assess based on concepts need an instructional model which organizes its lessons by concept as well. The instructional model can be designed to analyze the collection of weaknesses and tailor a lesson plan accordingly.

When the incorrect answers are being evaluated by the student model, some systems look to provide feedback to the actual questions in the form of 'hints'. As the student makes mistakes, useful suggestions pop up such as "look carefully at the sign of the number". This too can fall in the domain of the instructional model, with generic concept-based hints being offered based on concept weaknesses, or the hints can be question-specific in which case the student, instructional, and expert models all overlap.

Implementations

Learning management system

Many learning management systems have incorporated various adaptive learning features. A learning management system (LMS) is a software application for the administration, documentation, tracking, reporting and delivery of educational courses, training programs, or learning and development programs. Adaptive learning systems have previously been used, for instance, to help students develop their argumentative writing performance (Argument Mining). [8]

Distance learning

Adaptive learning systems [9] can be implemented on the Internet for use in distance learning and group collaboration. [10]

The field of distance learning is now incorporating aspects of adaptive learning. Initial systems without adaptive learning were able to provide automated feedback to students who are presented questions from a preselected question bank. That approach however lacks the guidance which teachers in the classroom can provide. Current trends in distance learning call for the use of adaptive learning to implement intelligent dynamic behavior in the learning environment.

During the time a student spends learning a new concept they are tested on their abilities and databases track their progress using one of the models. The latest generation of distance learning systems take into account the students' answers and adapt themselves to the student's cognitive abilities using a concept called 'cognitive scaffolding'. Cognitive scaffolding is the ability of an automated learning system to create a cognitive path of assessment from lowest to highest based on the demonstrated cognitive abilities. [11]

A current successful implementation of adaptive learning in web-based distance learning is the Maple engine of WebLearn by RMIT university. [12] WebLearn is advanced enough that it can provide assessment of questions posed to students even if those questions have no unique answer like those in the Mathematics field.

Adaptive learning can be incorporated to facilitate group collaboration within distance learning environments like forums or resource sharing services. [13] Some examples of how adaptive learning can help with collaboration include automated grouping of users with the same interests, and personalization of links to information sources based on the user's stated interests or the user's surfing habits.

Educational game design

In 2014, an educational researcher concluded a multi-year study of adaptive learning for educational game design. The research developed and validated the ALGAE (Adaptive Learning GAme dEsign) model, a comprehensive adaptive learning model based on game design theories and practices, instructional strategies, and adaptive models. The research extended previous researching in game design, instructional strategies, and adaptive learning, combining those three components into a single complex model.

The study resulted in the development of an adaptive educational game design model to serve as a guide for game designers, instructional designers, and educators with the goal of increasing learning outcomes. Survey participants validated the value of the ALGAE model and provided specific insights on the model's construction, use, benefits, and challenges. The current ALGAE model is based on these insights. The model now serves as a guideline for the design and development of educational computer games.

The model's applicability is assessed as being cross-industry including government and military agencies/units, game industry, and academia. The model's actual value and the appropriate implementation approach (focused or unfocused) will be fully realized as the ALGAE model's adoption becomes more widespread. [14]

Development tools

While adaptive learning features are often mentioned in the marketing materials of tools, the range of adaptivity can be dramatically different.

Entry-level tools tend to focus on determining the learner's pathway based on simplistic criteria such as the learner's answer to a multiple choice question. A correct answer may take the learner to Path A, whereas an incorrect answer may take them to Path B. While these tools provide an adequate method for basic branching, they are often based on an underlying linear model whereby the learner is simply being redirected to a point somewhere along a predefined line. Due to this, their capabilities fall short of true adaptivity.

At the other end of the spectrum, there are advanced tools which enable the creation of very complex adaptions based on any number of complex conditions. These conditions may relate to what the learner is currently doing, prior decisions, behavioral tracking, interactive and external activities to name a few. These higher end tools generally have no underlying navigation as they tend to utilize AI methods such as an inference engine. Due to the fundamental design difference advanced tools are able to provide rich assessment capabilities. Rather than taking a simple multiple choice question, the learner may be presented with a complex simulation where a number of factors are considered to determine how the learner should adapt.

See also

Related Research Articles

Instructional scaffolding is the support given to a student by an instructor throughout the learning process. This support is specifically tailored to each student; this instructional approach allows students to experience student-centered learning, which tends to facilitate more efficient learning than teacher-centered learning. This learning process promotes a deeper level of learning than many other common teaching strategies.

Situated learning is a theory that explains an individual's acquisition of professional skills and includes research on apprenticeship into how legitimate peripheral participation leads to membership in a community of practice. Situated learning "takes as its focus the relationship between learning and the social situation in which it occurs".

A cognitive tutor is a particular kind of intelligent tutoring system that utilizes a cognitive model to provide feedback to students as they are working through problems. This feedback will immediately inform students of the correctness, or incorrectness, of their actions in the tutor interface; however, cognitive tutors also have the ability to provide context-sensitive hints and instruction to guide students towards reasonable next steps.

<span class="mw-page-title-main">Constructivism (philosophy of education)</span> Philosophical viewpoint about the nature of knowledge; theory of knowledge

Constructivism in education is a theory that suggests that learners do not passively acquire knowledge through direct instruction. Instead, they construct their understanding through experiences and social interaction, integrating new information with their existing knowledge. This theory originates from Swiss developmental psychologist Jean Piaget's theory of cognitive development.

A learning management system (LMS) or virtual learning environment (VLE) is a software application for the administration, documentation, tracking, reporting, automation, and delivery of educational courses, training programs, materials or learning and development programs. The learning management system concept emerged directly from e-Learning. Learning management systems make up the largest segment of the learning system market. The first introduction of the LMS was in the late 1990s. LMSs have been adopted by almost all higher education institutions in the English-speaking world. Learning management systems have faced a massive growth in usage due to the emphasis on remote learning during the COVID-19 pandemic.

Educational technology is the combined use of computer hardware, software, and educational theory and practice to facilitate learning. When referred to with its abbreviation, "EdTech," it often refers to the industry of companies that create educational technology. In EdTech Inc.: Selling, Automating and Globalizing Higher Education in the Digital Age, Tanner Mirrlees and Shahid Alvi (2019) argue "EdTech is no exception to industry ownership and market rules" and "define the EdTech industries as all the privately owned companies currently involved in the financing, production and distribution of commercial hardware, software, cultural goods, services and platforms for the educational market with the goal of turning a profit. Many of these companies are US-based and rapidly expanding into educational markets across North America, and increasingly growing all over the world."

An intelligent tutoring system (ITS) is a computer system that imitates human tutors and aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one, personalized tutoring, in contexts where students would otherwise have access to one-to-many instruction from a single teacher, or no teacher at all. ITSs are often designed with the goal of providing access to high quality education to each and every student.

Computer-supported collaborative learning (CSCL) is a pedagogical approach wherein learning takes place via social interaction using a computer or through the Internet. This kind of learning is characterized by the sharing and construction of knowledge among participants using technology as their primary means of communication or as a common resource. CSCL can be implemented in online and classroom learning environments and can take place synchronously or asynchronously.

Adaptive hypermedia (AH) uses hypermedia which is adaptive according to a user model. In contrast to regular hypermedia, where all users are offered the same set of hyperlinks, adaptive hypermedia (AH) tailors what the user is offered based on a model of the user's goals, preferences and knowledge, thus providing links or content most appropriate to the current user.

User modeling is the subdivision of human–computer interaction which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, including modeling of their skills and declarative knowledge, for use in automatic software-tests. User-models can thus serve as a cheaper alternative to user testing but should not replace user testing.

E-learning theory describes the cognitive science principles of effective multimedia learning using electronic educational technology.

AutoTutor is an intelligent tutoring system developed by researchers at the Institute for Intelligent Systems at the University of Memphis, including Arthur C. Graesser that helps students learn Newtonian physics, computer literacy, and critical thinking topics through tutorial dialogue in natural language. AutoTutor differs from other popular intelligent tutoring systems such as the Cognitive Tutor, in that it focuses on natural language dialog. This means that the tutoring occurs in the form of an ongoing conversation, with human input presented using either voice or free text input. To handle this input, AutoTutor uses computational linguistics algorithms including latent semantic analysis, regular expression matching, and speech act classifiers. These complementary techniques focus on the general meaning of the input, precise phrasing or keywords, and functional purpose of the expression, respectively. In addition to natural language input, AutoTutor can also accept ad hoc events such as mouse clicks, learner emotions inferred from emotion sensors, and estimates of prior knowledge from a student model. Based on these inputs, the computer tutor determine when to reply and what speech acts to reply with. This process is driven by a "script" that includes a set of dialog-specific production rules.

Online tutoring is the process of tutoring in an online, virtual, or networked, environment, in which teachers and learners participate from separate physical locations. Aside from space, participants can also be separated by time.

An instructional simulation, also called an educational simulation, is a simulation of some type of reality but which also includes instructional elements that help a learner explore, navigate or obtain more information about that system or environment that cannot generally be acquired from mere experimentation. Instructional simulations are typically goal oriented and focus learners on specific facts, concepts, or applications of the system or environment. Today, most universities make lifelong learning possible by offering a virtual learning environment (VLE). Not only can users access learning at different times in their lives, but they can also immerse themselves in learning without physically moving to a learning facility, or interact face to face with an instructor in real time. Such VLEs vary widely in interactivity and scope. For example, there are virtual classes, virtual labs, virtual programs, virtual library, virtual training, etc. Researchers have classified VLE in 4 types:

Augmented learning is an on-demand learning technique where the environment adapts to the learner. By providing remediation on-demand, learners can gain greater understanding of a topic while stimulating discovery and learning. Technologies incorporating rich media and interaction have demonstrated the educational potential that scholars, teachers and students are embracing. Instead of focusing on memorization, the learner experiences an adaptive learning experience based upon the current context. The augmented content can be dynamically tailored to the learner's natural environment by displaying text, images, video or even playing audio. This additional information is commonly shown in a pop-up window for computer-based environments.

The expertise reversal effect refers to the reversal of the effectiveness of instructional techniques on learners with differing levels of prior knowledge. The primary recommendation that stems from the expertise reversal effect is that instructional design methods need to be adjusted as learners acquire more knowledge in a specific domain. Expertise is described as "the ability to perform fluently in a specific class of tasks."

Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis. When learners use an LMS, social media, or similar online tools, their clicks, navigation patterns, time on task, social networks, information flow, and concept development through discussions can be tracked. The rapid development of massive open online courses (MOOCs) offers additional data for researchers to evaluate teaching and learning in online environments.

Educational data mining (EDM) is a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings. 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.

<span class="mw-page-title-main">Pedagogical agent</span>

A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education, usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content, in an educational environment. A pedagogical agent is designed to model the type of interactions between a student and another person. Mabanza and de Wet define it as "a character enacted by a computer that interacts with the user in a socially engaging manner". A pedagogical agent can be assigned different roles in the learning environment, such as tutor or co-learner, depending on the desired purpose of the agent. "A tutor agent plays the role of a teacher, while a co-learner agent plays the role of a learning companion".

Vincent Aleven is a professor of human-computer interaction and director of the undergraduate program at Carnegie Mellon University's Human–Computer Interaction Institute.

References

This article incorporates material from the Citizendium article "Adaptive learning", which is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License but not under the GFDL.

  1. ↑ Andreas Kaplan (2021). Higher Education at the Crossroads of Disruption, The University of the 21st century. Emeral Publishers. ISBN   9781800715042.
  2. ↑ Wang, Shuai; Christensen, Claire; Cui, Wei; Tong, Richard; Yarnall, Louise; Shear, Linda; Feng, Mingyu (2023-02-17). "When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction". Interactive Learning Environments. 31 (2): 793–803. doi:10.1080/10494820.2020.1808794. ISSN   1049-4820. S2CID   225186350.
  3. ↑ Peter Brusilovsky (2003). "Adaptive and Intelligent Web-based Educational Systems". International Journal of Artificial Intelligence in Education. 13 (2–4): 159–172.
  4. ↑ J. R. Carbonell (1970). "AI in CAI: An Artificial-Intelligence Approach to Computer-Assisted Instruction". IEEE Transactions on Man Machine Systems. 11 (4): 190–202. doi:10.1109/TMMS.1970.299942.
  5. ↑ Derek H. Sleeman; John Seely Brown, eds. (1982). Intelligent tutoring systems. Academic Press. ISBN   9780126486803.
  6. ↑ Charles P. Bloom, R. Bowen Loftin Facilitating the Development and Use of Interactive Learning Environments, Lawrence Erlbaum Associates (1998).
  7. ↑ "A Proposed Student Model Algorithm for Student Modeling and its Evaluation". doi:10.1109/SICE.1995.526704. S2CID   61285822.{{cite journal}}: Cite journal requires |journal= (help)
  8. ↑ Wambsganss, Thiemo; Niklaus, Christina; Cetto, Matthias; Söllner, Matthias; Handschuh, Siegfried; Leimeister, Jan Marco (2020-04-21). "AL: An Adaptive Learning Support System for Argumentation Skills". Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Honolulu HI USA: ACM. pp. 1–14. doi:10.1145/3313831.3376732. ISBN   978-1-4503-6708-0. S2CID   218482749.
  9. ↑ "Adaptive Learning » EdAlive Online Learning". 2021-02-16. Retrieved 2023-06-01.
  10. ↑ "Personalised Elearning – Tailored Pathways" . Retrieved January 26, 2016.
  11. ↑ "Cognitive scaffolding for a web-based adaptive learning environment". Archived from the original on August 28, 2012. Retrieved August 17, 2008.
  12. ↑ "Addressing Different Cognitive Levels for On-line Learning" (PDF). Archived from the original (PDF) on September 18, 2010. Retrieved August 17, 2008.
  13. ↑ "Towards web-based adaptive learning communities" (PDF). Archived from the original (PDF) on June 4, 2006. Retrieved August 17, 2008.
  14. ↑ Lavieri, Edward (2014). A Study of Adaptive Learning for Educational Game Design. ISBN   9781321049602. ProQuest   1562778630.
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