Cognitive Tutor Research Articles

Recent Progress in the Use of Artificial Intelligence Tools in Education

The use of artificial intelligent (AI) tools in education has had a significant impact on learning experiences and outcomes. This review looks at recent advances in artificial intelligence tools and their implications for future research and practice. The review article followed the PRISMA method. Relevant articles on AI in education, specifically those describing the integration of AI or machine learning in undergraduate was extracted from the SCOPUS database. The inclusion criteria focus on articles directly related to teaching and training in structured programs that published in 2019 to 2023. A total of twelve documents were recognized and subjected to hand identification for subsequent analysis. The result shows that cognitive tutors, which are interactive learning environments facilitated by intelligent tutoring systems, improve learning outcomes. The challenge of balancing instructional assistance and self-directed learning, on the other hand, is inherent in AI-driven tools. AI tools in higher education provide numerous advantages at the institutional, social, and instructional levels. Disruptive AI tools such as ChatGPT have emerged, but challenges include job displacement concerns and the need for constant adaptation. AI language learning tools are important in language acquisition processes because they provide personalized learning paths and interactive engagement. However, ethical considerations and competencies related to AI-based tools in education are assessed, with parallels drawn to healthcare guidelines. It emphasizes the importance of strong ethical frameworks, as well as the critical role of educators and professionals in responsible AI use, in order to maintain a symbiotic relationship between human expertise and technological advancement. Overall, this review summarizes recent advances in the use of artificial intelligence tools to revolutionize education, emphasizing the importance of ongoing research, cross-disciplinary collaboration, and careful implementation.

Flow with an intelligent tutor: A latent variable modeling approach to tracking flow during artificial tutoring.

Increasing use of intelligent tutoring systems in education calls for analytic methods that can unravel students' learning behaviors. In this study, we explore a latent variable modeling approach for tracking learning flow during computer-interactive artificial tutoring. The study considers three models that give discrete profiles of a latent process: the (i) latent class model, (ii) latent transition model, and (iii) hidden Markov model. We illustrate application of each model using example log data from Cognitive Tutor Algebra I and suggest analytic procedures of drawing learning flow. Through experimental application, we show that the models can reveal substantive information about students' learning behaviors and have potential utility for describing the learning flow. The models differed in the assumptions and data constraints but yielded consistent findings on the flow states and interaction modalities. Based on our experiential analyses, we discuss strengths and limitations of the models and illuminate areas of future development.

The Open-Response Chemistry Cognitive Assistance Tutor System: Development and Implementation

This report showcases a new type of online homework system that provides students with a free-form interface and dynamic feedback. The ORCCA Tutor (Open-Response Chemistry Cognitive Assistance Tutor) is a production rules-based online tutoring system utilizing the Cognitive Tutoring Authoring Tools (CTAT) developed by Carnegie Mellon University. In this report, we discuss the interface design and the production rules that allow for a multitude of chemistry calculations to be solved in a wide variety of ways by students. We discuss improvements of the software, already implemented or planned for the future, based on think aloud/interviews with students.

Knowledge tracing for adaptive learning in a metacognitive tutor

Abstract Adaptive learning is provided in intelligent tutoring systems (ITS) to enable learners with varying abilities to meet their expected learning outcomes. Despite the personalized learning afforded by ITSes using adaptive learning, learners are still susceptible to shallow learning. Introducing metacognitive tutoring to teach learners how to be aware of their knowledge can enable deeper learning. However, metacognitive tutoring on top of cognitive tutoring can lead to unsustainable cognitive loads. Using metacognitive inputs for knowledge tracing was explored for managing cognitive loads. Hidden Markov models (HMM) and artificial neural networks were used to train models on a synthetic dataset created from predetermined learner personas. The models created with metacognitive inputs were compared with the models created without said inputs. The models using metacognitive inputs performed better than the standard models while still following learning intuitions. This indicates that combining knowledge tracing and metacognitive tutoring is a viable option for improving learning outcomes. This is an important finding since online learning, which demands metacognitive skills, is becoming popular for various topics, including those that are challenging even with immediate teacher assistance.

Aspects of Cognitive Neuroscience for the Solution of Problems Found in Contemporary Philosophy of Science

In this research a deep definition of cognitive science has been provided along with research philosophy of neuroscience is also conducted here in this paper. Philosophy such as challenges in different languages, history, analysis of and interpersonal and so on is mentioned in this study. The impact of cognitive neuroscience on modern science philosophy is also included through this research. Also, cognitive neuroscience is the foundation of modern philosophy has been shown in this study. Moreover, usages of neuroscience in the education sector are also added in this paper. Data such as Facilitate learning, spacing learning, cognitive tutoring and so on is covered through this research in order to understand the implementing techniques that are used by teachers. Lastly, interconnection between AI and neuroscience along with its effect in modern businesses is included in this research. Keywords : Cognitive neuroscience, Philosophy of neuroscience, Artificial intelligence, human automation, cognitive tutoring, customer behaviour

Intelligent Tutoring System: Learning Math for 6th-Grade Primary School Students

This paper proposes a web-based application designed to help elementary school students who have difficulty learning online independently and also their parents who are currently having difficulty teaching their children to study at home online, especially at this time of difficulty with a pandemic outbreak like COVID-19; this time does not allow for physical meetings for the learning process in primary schools. In this paper, we only focus on mathematics because based on several other studies, it is very difficult and important to learn mathematics at the beginning of educational activities such as at the elementary school level. In this paper, the system is modeled using the Unified Modeling Language (UML) tool in the form of a use case diagram which is used to describe the proposed business process and uses class diagrams to describe the database model diagram. In this case, the class diagram is used to describe the data in the class diagram where each class refers to a table in the database. The web-based application user interface is shown at the end to show the communication between users and applications, where this web-based application is implemented using Personal Home Pages (PHP) as server programming and using MySQL to store database model designs. Moreover, for the Intelligent Tutoring System (ITS), content was created using the Cognitive Tutor Authoring Tools (CTAT) which is an authoring tool for learning mathematics created by Carnegie Mellon University. In the end, this web-based application is expected to be used and support teachers as a complement to online mathematics learning, especially during difficult times such as during the COVID-19 pandemic.

Student Log-Data from a Randomized Evaluation of Educational Technology: A Causal Case Study

Randomized evaluations of educational technology produce log data as a bi-product: highly granular data on student and teacher usage. These datasets could shed light on causal mechanisms, effect heterogeneity, or optimal use. However, there are methodological challenges: implementation is not randomized and is only defined for the treatment group, and log datasets have a complex structure. This article discusses three approaches to help surmount these issues. One approach uses data from the treatment group to estimate the effect of usage on outcomes in an observational study. Another, causal mediation analysis, estimates the role of usage in driving the overall effect. Finally, principal stratification estimates overall effects for groups of students with the same “potential” usage. We analyze hint data from an evaluation of the Cognitive Tutor Algebra I curriculum using these three approaches, with possibly conflicting results: the observational study and mediation analysis suggest that hints reduce posttest scores, while principal stratification finds that treatment effects may be correlated with higher rates of hint requests. We discuss these mixed conclusions and give broader methodological recommendations.

Online Teaching Experiences of Language Teachers in Higher Education in the Confinement Period

The inclusion of information and communication technology has been a challenge for higher education institutions in the confinement period caused by the COVID-19 pandemic. Mexican educational institutions seem not to be prepared to face that demand due to the lack of technological resources and lack of internet connection (INEGI, 2019). In the confinement period, in Mexico, face-to-face institutions turned into eLearning to continue students’ education. The use of ICT demands teachers to develop digital competencies to make learning a successful process. This descriptive-exploratory qualitative study aimed to identify the online teaching experiences of language teachers in the confinement period. The results showed that some teachers became social tutors, while others were cognitive tutors. Besides, they had positive online teaching experiences, which led them to grow academically, be in touch with their students, develop, and select materials. However, the increase of time and overwork, lack of internet and computer essential tools, and students’ low commitment affected teachers physically and emotionally, causing them to lived negative online teaching experiences. Teachers were having a difficult time trying to fulfill their responsibilities, and it seems that they were not considering students’ learning styles in their online teaching practice.

The acquisition of German declension in additive and concept-based approaches to instruction via computer-based cognitive tutors

AbstractThis study examines the different learning outcomes of two computer-based cognitive tutors using two approaches to instructing German declension: an additive, bottom-up approach, which focuses on a stepwise introduction of each case, and a concept-based, top-down approach, which focuses on developing students’ conceptual understanding of the functions related to each case form and the case marking system as a whole. The results indicate that both groups learned, but what and how they learned differed depending on the method of instruction. The additive group showed general gains in production and a slight increase in their ability to correctly interpret object-first sentences. The concept-based group showed larger gains, but in fewer areas. Specifically, the production of adjective endings increased, although there were no differences in determiner production or accuracy. The concept-based group also had a larger gain in their ability to interpret non-SVO word order sentences. This study shows how concept-based approaches to grammar can outperform additive ones, and that the development of these concepts can prepare students for future learning.

The Effect of Metacognitive Scaffolding for Learning by Teaching a Teachable Agent

The effect of metacognitive scaffolding for learning by teaching was investigated and compared against learning by being tutored. Three versions of an online learning environment for learning algebra equations were created: (1) APLUS that allows students to interactively teach a synthetic peer with a goal to have the synthetic peer pass the quiz while the system provides students with metacognitive scaffolding on how to teach. (2) AplusTutor that provides cognitive tutoring (i.e., immediate feedback and just-in-time hint) and metacognitive scaffolding on how to learn. And, (3) CogTutor+ that provides traditional cognitive tutoring on mastery learning. Two school studies were conducted with a total of 444 6th through 8th grade students. 208 students completed the study and were included in the analysis. The results show that (i) students’ proficiency in solving equations increased after using our interventions for 4 days, but there was no difference in the effectiveness across three interventions, and (ii) learning by teaching with metacognitive scaffolding facilitated learning equally across various levels of students’ prior competency.

Fuzzy System Inference and Fuzzy Cognitive Maps for a Cognitive Tutor of Algebra

Fuzzy System Inference and Fuzzy Cognitive Maps for a Cognitive Tutor of Algebra

Exploring how teachers support students’ mathematical learning in computer-directed learning environments

PurposeThis study explores the teacher’s role for implementing a cognitive tutor (CT) intended to increase students’ knowledge of proportional reasoning and potential impacts on students’ learning.Design/methodology/approachUsing a mixed methods approach to design-based research, the authors examine results from three different phases of the CT implementation using frameworks from mathematics education research.FindingsBased on observations of 10 educators, the authors identify 4 different types of interactions among the CT, students and educators. Using observations and student assessment results (n= 134), the authors begin to build an argument that different types of interactions have the potential to impact students’ opportunities to learn in computer-directed learning environments.Originality/valueThe authors conclude that research on the efficacy of computer-directed learning environments should consider differences in implementation of CT materials and that the types of CT, student and educator interactions described herein provide a framework to support such exploration.

PACA-ITS: A Multi-Agent System for Intelligent Virtual Laboratory Courses

This paper describes an intensive design leading to the implementation of an intelligent lab companion (ILC) agent for an intelligent virtual laboratory (IVL) platform. An IVL enables virtual labs (VL) to be used as online research laboratories, thereby facilitating and improving the analytical skills of students using agent technology. A multi-agent system enhances the capability of the learning system and solves students’ problems automatically. To ensure an exhaustive Agent Unified Modeling Language (AUML) design, identification of the agents’ types and responsibilities on well-organized AUML strategies is carried out. This work also traces the design challenge of IVL modeling and the ILC agent functionality of six basic agents: the practical coaching agent (PCA), practical dispatcher agent (PDA), practical interaction and coordination agent (PICA), practical expert agent (PEA), practical knowledge management agent (PKMA), and practical inspection agent (PIA). Furthermore, this modeling technique is compatible with ontology mapping based on an enabling technology using the Java Agent Development Framework (JADE), Cognitive Tutor Authoring Tools (CTAT), and Protégé platform integration. The potential Java Expert System Shell (Jess) programming implements the cognitive model algorithm criteria that are applied to measure progress through the CTAT for C++ programming concept task on IVL and successfully deployed on the TutorShop web server for evaluation. The results are estimated through the learning curve to assess the preceding knowledge, error rate, and performance profiler to engage cognitive Jess agent efficiency as well as practicable and active decisions to improve student learning.

Evaluating an automated mediator for joint narratives in a conflict situation

ABSTRACT Joint narratives are often used in the context of reconciliation interventions for people in social conflict situations, which arise, for example, due to ethnic or religious differences. The interventions aim to encourage a change in attitudes of the participants towards each other. Typically, a human mediator is fundamental for achieving a successful intervention. In this work, we present an automated approach to support remote interactions between pairs of participants as they contribute to a shared story in their own language. A key component is an automated cognitive tutor that guides the participants through a controlled escalation/de-escalation process during the development of a joint narrative. We performed a controlled study comparing a trained human mediator to the automated mediator. The results demonstrate that an automated mediator, although simple at this stage, effectively supports interactions and helps to achieve positive outcomes comparable to those attained by the trained human mediator.

The role of mastery learning in an intelligent tutoring system: Principal stratification on a latent variable

Students in Algebra I classrooms typically learn at different rates and struggle at different points in the curriculum—a common challenge for math teachers. Cognitive Tutor Algebra I (CTA1), an educational computer program, addresses such student heterogeneity via what they term “mastery learning,” where students progress from one section of the curriculum to the next by demonstrating appropriate “mastery” at each stage. However, when students are unable to master a section’s skills even after trying many problems, they are automatically promoted to the next section anyway. Does promotion without mastery impair the program’s effectiveness? At least in certain domains, CTA1 was recently shown to improve student learning on average in a randomized effectiveness study. This paper uses student log data from that study in a continuous principal stratification model to estimate the relationship between students’ potential mastery and the CTA1 treatment effect. In contrast to extant principal stratification applications, a student’s propensity to master worked sections here is never directly observed. Consequently we embed an item-response model, which measures students’ potential mastery, within the larger principal stratification model. We find that the tutor may, in fact, be more effective for students who are more frequently promoted (despite unsuccessfully completing sections of the material). However, since these students are distinctive in their educational strength (as well as in other respects), it remains unclear whether this enhanced effectiveness can be directly attributed to aspects of the mastery learning program.

The Effect of Assistance on Learning and Affect in an Algebra Tutor

We investigated the impact of assistance on learning and affect during problem-solving activities with a computer tutor we built using the Cognitive Tutor Authoring Tools framework. The tutor delivered its primary form of assistance in the form of worked-out examples. We manipulated the level of assistance the examples in the tutor provided, by having similar problem-example pairs in one version of the tutor (high-assistance condition) and reduced similarity problem-example pairs in the other version (reduced-assistance condition). The reduced-assistance condition resulted in significantly higher learning, without increasing negative affect like frustration.

A computer-based tutor for learning energy and power

Educating our future citizens in science and engineering is vitally important to ensure future advancement. Presently, in the light of environmental sustainability, it is critical that students learn concepts relating to energy, its consumption and future demands. In this article, we harness the state of the educational technology, namely Intelligent Tutoring System to teach concepts relating to energy and power. We describe the design and implementation of our computer-based tutor to bridge this knowledge and skills gap. We built a responsive tutoring system with Carnegie Mellon University’s Cognitive Tutor Authoring Tools that challenges the students with quantitative problems based on energy, provides immediate feedback to student answers and provides need-based, timely help that supports student learning. We explain our research-based design that provides a friendly and welcoming user interface, displays astute and anticipatory software behaviour, and scaffolds student learning through appropriate clues and hints.

Toward the Design of a Cognitive Tutor for Algebra with Gamification: A Survey of State-of-the-Art

Toward the Design of a Cognitive Tutor for Algebra with Gamification: A Survey of State-of-the-Art

How Flexible Is Your Data? A Comparative Analysis of Scoring Methodologies across Learning Platforms in the Context of Group Differentiation

Data is flexible in that it is molded by not only the features and variables available to a researcher for analysis and interpretation, but also by how those features and variables are recorded and processed prior to evaluation. “Big Data” from online learning platforms and intelligent tutoring systems is no different. The work presented herein questions the quality and flexibility of data from two popular learning platforms, comparing binary measures of problem-level accuracy, the scoring method typically used to inform learner analytics, with partial credit scoring, a more robust, real-world methodology. This work extends previous research by examining how the manipulation of scoring methodology has the potential to alter outcomes when testing hypotheses, or specifically, when looking for significant differences between groups of students. Datasets from ASSISTments and Cognitive Tutor are used to assess the implications of data availability and manipulation within twelve mathematics skills. A resampling approach is used to determine the size of equivalent samples of high- and low-performing students required to reliably differentiate performance when considering each scoring methodology. Results suggest that in eleven out of twelve observed skills, partial credit offers more efficient group differentiation, increasing analytic power and reducing Type II error. Alternative applications of this approach and implications for the Learning Analytics community are discussed.

A Cognitive Tutor of Arabic Word Root Extraction Using Artificial Word Generation, Scaffolding and Self-Explanation

Extracting the roots (stemming) of Arabic words is one of the most challenging skills taught to Arabic language learners. To address this challenge, this paper proposes the Arabic word Root extraction Tutor (ART). ART is a cognitive tutor intended to teach students production rules needed for Arabic word root extraction. In the passive mode, ART accepts an input word and generates its root with explanation. In the active mode, on the other hand, words are generated by ART and the student is prompted to provide the correct roots. ART integrates several techniques for enhanced tutoring. It provides a positive feedback for a correct answer and a negative one otherwise. In the latter case, Prompting Answer Strategy (PAS) is employed, where the student is guided to detect the error by integrating scaffolding and self-explanation. Scaffolding prompts the student to apply the relevant production rule step by step. In each step, a number of options are given to the student to select the correct one via self-explanation. If the error persists, the correct answer is generated with explanation. In addition to generating real words, artificial words are generated using the production rules. This novel technique is intended to ensure that the student applies the production rules rather than memorizes the roots of common words. Evaluation has shown the effectiveness of ART tutoring process and suggests artificial word generation as a promising technique in language tutoring.