Cognitive tutor

Last updated

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.

Contents

Introduction

The name of Cognitive Tutor now usually refers to a particular type of intelligent tutoring system produced by Carnegie Learning for high school mathematics based on John Anderson's ACT-R theory of human cognition. However, cognitive tutors were originally developed to test ACT-R theory for research purposes since the early 1980s and they are developed also for other areas and subjects such as computer programming and science. [1] Cognitive Tutors can be implemented into classrooms as a part of blended learning that combines textbook and software activities.

The Cognitive Tutor programs utilize cognitive model and are based on model tracing and knowledge tracing. Model tracing means that the cognitive tutor checks every action performed by students such as entering a value or clicking a button, while knowledge tracing is used to calculate the required skills students learned by measuring them on a bar chart called Skillometer. [2] Model tracing and knowledge tracing are essentially used to monitor students' learning progress, guide students to correct path to problem solving, and provide feedback.

The Institute of Education Sciences published several reports regarding the effectiveness of Carnegie Cognitive Tutor. A 2013 report concluded that Carnegie Learning Curricula and Cognitive Tutor was found to have mixed effects on mathematics achievement for high school students. [3] The report identified 27 studies that investigate the effectiveness of Cognitive Tutor , and the conclusion is based on 6 studies that meet What Works Clearinghouse standards. Among the 6 studies included, 5 of them show intermediate to significant positive effect, while 1 study shows statistically significant negative effect. Another report published by Institute of Education Sciences in 2009 found that Cognitive Tutor Algebra I to have potentially positive effects on math achievement based on only 1 study out of 14 studies that meets What Works Clearinghouse standards.it should be understood that What Works Clearinghouse standards call for relatively large numbers of participants, true random assignments to groups, and for a control group receiving either no treatment or a different treatment. Such experimental conditions are difficult to meet in schools, and thus only a small percentage of studies in education meet the standards of this clearinghouse, even though they may still be of value. [4]

Theoretical foundations

Four-component architecture

Intelligent tutoring systems (ITS) have a four-component architecture: a domain model, a student model, a tutoring model [5] and an interface component.

The domain model contains the rules, concepts, and knowledge related to the domain to be learned. It helps to evaluate students' performance and detect students' errors by setting a standard of domain expertise.[ citation needed ]

The student model, the central component of an ITS, is expected to contain knowledge about the students: their cognitive and affective states, and their progress as they learn. The function of the student model is threefold: to gather data from and about the learner, to represent the learner's knowledge and learning process, and to perform diagnostics of a student's knowledge and select optimal pedagogical strategies. [6]

The tutoring model uses the data gained from the domain model and student model to make decisions about tutoring strategies such as whether or not to intervene, or when and how to intervene. Functions of the tutoring model include instruction delivery and content planning. [7]

The interface component reflects the decisions made by the tutoring model in different forms such as Socratic dialogs, feedback and hints. Students interact with the tutor through the learning interface, also known as communication. The interface provides domain knowledge elements. [7]

Cognitive model

A cognitive model tries to model the domain knowledge in the same way knowledge is represented in the human mind. Cognitive model enables intelligent tutoring systems to respond to problem-solving situations as the learner would. [8] A tutoring system adopting a cognitive model is called a cognitive tutor.

Cognitive model is an expert system which hosts a multitude of solutions to the problems presented to students. The cognitive model is used to trace each student's solution through complex problems, enabling the tutor to provide step-by-step feedback and advice, and to maintain a targeted model of the student's knowledge based on student performance. [9]

Cognitive Tutors

Cognitive Tutors provide step-by-step guidance as a learner develops a complex problem-solving skill through practice. [10] Typically, cognitive tutors provide such forms of support as: (a) a problem-solving environment that is designed rich and "thinking visible"; (b) step-by-step feedback on student performance; (c) feedback messages specific to errors; (d) context-specific next-step hints at student's request, and (e) individualized problem selection. [11]

Cognitive Tutors accomplish two of the principal tasks characteristic of human tutoring: (1) monitors the student's performance and providing context-specific individual instruction, and (2) monitors the student's learning and selects appropriate problem-solving activities. [12]

Both cognitive model and two underlying algorithms, model tracing and knowledge tracing, are used to monitor the student's learning. In model tracing, the cognitive tutor uses the cognitive model in complex problems to follow the student's individual path and provide prompt accuracy feedback and context-specific advice. In knowledge tracing, the cognitive tutor uses a simple Bayesian method of evaluating the student's knowledge and uses this student model to select appropriate problems for individual student. [12]

Cognitive architecture

Cognitive tutor development is guided by ACT-R cognitive architecture, which specifies the underlying framework developing the cognitive model or expert component of a cognitive tutor.

ACT-R, a member of the ACT family, is the most recent cognitive architecture, devoted primarily to modelling human behavior. ACT-R includes a declarative memory of factual knowledge and a procedural memory of production rules. The architecture functions by matching productions on perceptions and facts, mediated by the real-valued activation levels of objects, and executing them to affect the environment or alter declarative memory. ACT-R has been used to model psychological aspects such as memory, attention, reasoning, problem solving, and language processing. [13]

Application and utilization

The first real world applications of cognitive tutors were a geometry proof tutor used by high school students and a mini course in Computer Science at Carnegie Mellon University using the LISP tutor with college students. [1]

Since then, cognitive tutors have been used in a variety of scenarios, with a few organizations developing their own cognitive tutor programs. These programs have been used with students spanning elementary school through university level, though primarily in the subject areas of Computer Programming, Mathematics, and Science. [14]

One of the first organizations to develop a system for use within the school system was the PACT Center at Carnegie Mellon University. Their aim was to "...develop systems that provide individualized assistance to students as they work on challenging real-world problems in complex domains such as computer programming, algebra and geometry". [14] PACT's most successful product was the Cognitive Tutor Algebra course. Originally created in the early 1990s, this course was in use in 75 schools through the U.S. by 1999, and then its spin-off company, Carnegie Learning, now offers tutors to over 1400 schools in the U.S. [14]

The Carnegie Mellon Cognitive Tutor has been shown to raise students' math test scores in high school and middle-school classrooms, [15] and their Algebra course was designated one of five exemplary curricula for K-12 mathematics educated by the US Department of Education. [14] There were several research projects conducted by the PACT Center to utilize Cognitive tutor for courses in Excel and to develop an intelligent tutoring system for algebra expression writing, called Ms. Lindquist. [16] Further, in 2005, Carnegie Learning released Bridge to Algebra, a product intended for middle schools that was piloted in over 100 schools. [17]

Cognitive tutoring software is continuing to be used. According to a Business Insider Report article, Ken Koedinger, a professor of human-computer interaction and psychology at Carnegie Mellon University, describes how teachers can integrate cognitive tutoring software into the classroom. [18] He suggests that teachers use it in a computer lab environment or during classes. Cognitive tutors can understand the many ways that a student might answer a problem, and then assist the student at the exact time that the help is required. Further, the cognitive tutor can customize exercises specific to the student's needs. [18]

Limitations

At this time it is unclear whether Cognitive Tutor is effective at improving student performance. [3] Cognitive Tutor has had some commercial success however, there may be limitations inherently linked to its design and the nature of intelligent tutoring systems. The following section discusses limitations of Cognitive Tutor which may also apply to other intelligent tutoring systems.

Curriculum

At this time, creating a Cognitive Tutor for all subject areas is not practical or economical. Cognitive Tutor has been used successfully but is still limited to tutoring algebra, computer programming and geometry because these subject areas have an optimal balance of production rules, complexity and maximum benefit to the learner. [1] [19]

The focus of Cognitive Tutor development has been the design of the software to teach specific production rules and not on the development of curricular content. Despite many years of trials, improvements, and a potential to advance learning objectives, the creators continue to rely primarily on outside sources for curricular direction. [1]

Design

The complexity of Cognitive Tutor software requires designers to spend hundreds of hours per instructional hour to create the program. Despite the time invested, the challenges associated with meeting the needs of the learner within the constraints of the design often result in compromises in flexibility and cognitive fidelity. [11]

Practicality dictates that designers must choose from a discrete set of methods to teach and support learners. Limited choices of methods, prompts and hints may be effective in supporting some learners but may conflict with the methods already in use by others. [19] In addition, it is possible that learners will use the system of prompts and hints to access the answers prematurely thereby advancing through the exercises which may result in them not meeting the learning objectives.

Model

The cognitive model, which inspired Cognitive Tutor is based on assumptions about how learning occurs which dictates the chosen instructional methods such as hints, directions and timing of the tutoring prompts. Given these assumptions and the limited methods of presentation, Cognitive Tutor may not account for the flexible, complex and diverse ways humans create knowledge. [19] Human tutors outperform Cognitive Tutor by providing a higher level of responsiveness to student errors. They are capable of providing more effective feedback and scaffolding to learners than Cognitive Tutor, indicating the cognitive model may still be incomplete. [20]

See also

Related Research Articles

Instructional design (ID), also known as instructional systems design and originally known as instructional systems development (ISD), is the practice of systematically designing, developing and delivering instructional materials and experiences, both digital and physical, in a consistent and reliable fashion toward an efficient, effective, appealing, engaging and inspiring acquisition of knowledge. The process consists broadly of determining the state and needs of the learner, defining the end goal of instruction, and creating some "intervention" to assist in the transition. The outcome of this instruction may be directly observable and scientifically measured or completely hidden and assumed. There are many instructional design models, but many are based on the ADDIE model with the five phases: analysis, design, development, implementation, and evaluation.

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.

<span class="mw-page-title-main">Problem-based learning</span> Learner centric pedagogy

Problem-based learning (PBL) is a student-centered pedagogy in which students learn about a subject through the experience of solving an open-ended problem found in trigger material. The PBL process does not focus on problem solving with a defined solution, but it allows for the development of other desirable skills and attributes. This includes knowledge acquisition, enhanced group collaboration and communication.

John Robert Anderson is a Canadian-born American psychologist. He is currently professor of Psychology and Computer Science at Carnegie Mellon University.

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.

Kenneth R. Koedinger is a professor of human–computer interaction and psychology at Carnegie Mellon University. He is the founding and current director of the Pittsburgh Science of Learning Center. He is widely known for his role in the development of the Cognitive Tutor software. He is also widely published in cognitive psychology, intelligent tutoring systems, and educational data mining, and his research group has repeatedly won "Best Paper" awards at scientific conferences in those areas, such as the EDM2008 Best Paper, ITS2006 Best Paper, ITS2004 Best Paper, and ITS2000 Best Paper.

Mitchell J. Nathan is an American academic, who is a Full Professor of Educational Psychology, Chair of the Learning Science program in the School of Education at the University of Wisconsin–Madison, and a researcher at the Wisconsin Center for Education Research.

Carnegie Learning, Inc. is a provider of K–12 education services for math, literacy and ELA, world languages, and applied sciences, as well as high-dosage tutoring and professional learning.

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

The worked-example effect is a learning effect predicted by cognitive load theory. Specifically, it refers to improved learning observed when worked examples are used as part of instruction, compared to other instructional techniques such as problem-solving and discovery learning. According to Sweller: "The worked example effect is the best known and most widely studied of the cognitive load effects".

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.

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. 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.

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.

Albert T. Corbett is an associate research professor emeritus of human–computer interaction at Carnegie Mellon University. He is widely known for his role in the development of the Cognitive Tutor software, leading to one article with over 1,000 citations. Along with John Robert Anderson, he developed the Bayesian knowledge tracing algorithm, which is used in Cognitive Tutor software. This work has been particularly influential in the educational data mining community—over half of the EDM conference papers published in 2011 and 2012 cited Bayesian knowledge-tracing. Corbett studied psychology at Brown University, and obtained a doctorate in psychology from the University of Oregon. His doctoral advisor was Wayne Wickelgren.

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

Jill Huston Larkin is an American cognitive scientist, science educator and Professor at the Carnegie Mellon University known for her work on information representations.

The Cognitive Theory of Inquiry Teaching, also referred to as the Cognitive Theory of Interactive Teaching, was developed by Allan Collins and Albert L. Stevens. Allan Collins was a chief scientist at Bolt Beranek and Newman Inc., a research firm in Cambridge Massachusetts. He is also a specialist in the field of cognitive science and human semantic processing. Albert L. Stevens was a senior scientist at Bolt Beranek and Newman Inc. He was also director of the company's Artificial Intelligence, Education Technology and Training Systems Division. He is also a specialist in cognitive science. The Cognitive Theory of Inquiry Teaching according to Collins and Stevens (1981) requires the learner to construct theories and principles through dialogue, the teaching of self-questioning techniques and the teaching of metacognitive or self-monitoring skills, all with the intent of clarifying misconceptions so the theory or principle is well articulated and developed. The essence of the cognitive theory of Inquiry teaching is that of developing students' metacognitive skills. Inquiry teaching deliberately attempts to develop these stills through instruction.

<span class="mw-page-title-main">Bruce M. McLaren</span> American researcher, academic and author (born 1959)

Bruce Martin McLaren is an American researcher, scientist and author. He is an Associate Research Professor at Carnegie Mellon University and a former President of the International Artificial Intelligence in Education Society (2017-2019).

<span class="mw-page-title-main">Learning engineering</span> Interdisciplinary academic field

Learning Engineering is the systematic application of evidence-based principles and methods from educational technology and the learning sciences to create engaging and effective learning experiences, support the difficulties and challenges of learners as they learn, and come to better understand learners and learning. It emphasizes the use of a human-centered design approach in conjunction with analyses of rich data sets to iteratively develop and improve those designs to address specific learning needs, opportunities, and problems, often with the help of technology. Working with subject-matter and other experts, the Learning Engineer deftly combines knowledge, tools, and techniques from a variety of technical, pedagogical, empirical, and design-based disciplines to create effective and engaging learning experiences and environments and to evaluate the resulting outcomes. While doing so, the Learning Engineer strives to generate processes and theories that afford generalization of best practices, along with new tools and infrastructures that empower others to create their own learning designs based on those best practices.

References

  1. 1 2 3 4 Anderson, J. R.; Corbett, A. T.; Koedinger, K. R. & Pelletier, R. (1995). "Cognitive tutor: Lesson learned" (PDF). The Journal of the Learning Sciences. 4 (2): 167–207. doi:10.1207/s15327809jls0402_2.
  2. ↑ Blessing, Stephen. "The Cognitive Tutor™: Successful Application of Cognitive Science". Carnegie Learning. Carnegie Learning. Retrieved 17 July 2014.
  3. 1 2 "High School Mathematics intervention report: Carnegie Learning Curricula and Cognitive Tutor" (PDF). Education Resources Information Center. 2013.
  4. ↑ "What Works Clearinghouse: Cognitive Tutor Algebra I" (PDF). Institute of Education Sciences. 2009. Archived from the original (PDF) on 2016-03-31. Retrieved 2016-07-23.
  5. ↑ Self, J. (1990). "Theoretical Foundations for Intelligent Tutoring Systems". Journal of Artificial Intelligence in Education. 1 (4): 3–14. CiteSeerX   10.1.1.53.6841 .
  6. ↑ Wenger, E. (1987). Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge . Los Altos: Morgan Kaufmann Publishers Inc. ISBN   978-0-934613-26-2.
  7. 1 2 Roger Nkambou; Jacqueline Bourdeau; Riichiro Mizoguchi, eds. (2010). Advances in Intelligent Tutoring Systems. p. 308. ISBN   978-3-642-14362-5.
  8. ↑ Corbett, A.T.; Koedinger, K.R.; Anderson, J.R. (1997). "Intelligent Tutoring Systems". In Helander, T.K.; Landauer, P. (eds.). Handbook of Human-Computer Interaction. Amsterdam: Elsevier Science.
  9. ↑ Corbett, A.; Kauffman, L.; MacLaren, B.; Wagner, A.; Jones, E. (2010). "A Cognitive Tutor for genetics problem solving: Learning gains and student modeling" (PDF). Journal of Educational Computing Research. 42 (2): 219–239. doi:10.2190/EC.42.2.e. S2CID   62695743.
  10. ↑ VanLehn, K. (2006). "The behavior of tutoring systems" (PDF). International Journal of Artificial Intelligence in Education. 16 (3): 227–265.
  11. 1 2 Aleven, V. (2010). "Rule-Based Cognitive Modeling for Intelligent Tutoring Systems". In R. Nkambou; et al. (eds.). Advances in Intelligent Tutoring Systems. SCI 308. pp. 33–62.
  12. 1 2 Koedinger, K.R.; Corbett, A.T. (2006). "Cognitive tutors: Technology bringing learning sciences to the classroom". In Sawyer, R.K. (ed.). The Cambridge handbook of the learning sciences (PDF). New York: Cambridge University Press.
  13. ↑ P. Langley and J. E. Laird, "Cognitive Architectures: Research Issues and Challenges". Draft of October 31, 2002.
  14. 1 2 3 4 "PACT Center @ Carnegie Mellon University, Home Page". May 2003.
  15. ↑ "PACT Center @ Carnegie Mellon University, Current Research Projects". May 2003.
  16. ↑ "PACT Center @ Carnegie Mellon University, Completed Research Projects". May 2003.
  17. ↑ "Timeline of Cognitive Tutor History. (2003-2013)". Cognitive Tutoring Authoring Tools.
  18. 1 2 Griswold, Alison (March 6, 2014). "This Cognitive Tutor Software Is Already Having A Revolutionary Effect". Business Insider.
  19. 1 2 3 Scandura, J. M. (2012). "The role of automation in instruction: Recent advances in authorIT and TutorIT solve fundamental problems in developing intelligent tutoring systems" (PDF). Technology, Instruction, Cognition and Learning. 9: 3–8.
  20. ↑ VanLehn, K. (2011). "The relative effectiveness of human tutoring, intelligent tutuoring systems and other tutoring systems" (PDF). Educational Psychologist. 46 (4): 197–221. doi:10.1080/00461520.2011.611369. S2CID   16188384.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.